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Appended functions to BaseDC

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D Fournier
2016-01-12 17:46:58 -08:00
parent 9d861267e1
commit 2dfacdc904
23 changed files with 5164 additions and 2377 deletions
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simpegDCIP/BaseDC.py
+562
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@@ -287,3 +287,565 @@ class ProblemDC_CC(Problem.BaseProblem):
Jtv = - mT_dm.T * ( Jtv )
return Jtv
def readUBC_DC2DModel(fileName):
from SimPEG import np, mkvc
"""
Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
Input:
:param fileName, path to the UBC GIF 2D model file
Output:
:param SimPEG TensorMesh 2D object
:return
Created on Thu Nov 12 13:14:10 2015
@author: dominiquef
"""
# Open fileand skip header... assume that we know the mesh already
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
dim = np.array(obsfile[0].split(),dtype=float)
temp = np.array(obsfile[1].split(),dtype=float)
if len(temp) > 1:
model = np.zeros(dim)
for ii in range(len(obsfile)-1):
mm = np.array(obsfile[ii+1].split(),dtype=float)
model[:,ii] = mm
model = model[:,::-1]
else:
if len(obsfile[1:])==1:
mm = np.array(obsfile[1:].split(),dtype=float)
else:
mm = np.array(obsfile[1:],dtype=float)
# Permute the second dimension to flip the order
model = mm.reshape(dim[1],dim[0])
model = model[::-1,:]
model = np.transpose(model, (1, 0))
model = mkvc(model)
return model
def plot_pseudoSection(Tx,Rx,data,z0, stype):
from SimPEG import np, mkvc
from scipy.interpolate import griddata
from matplotlib.colors import LogNorm
import pylab as plt
import re
"""
Read list of 2D tx-rx location and plot a speudo-section of apparent
resistivity.
Assumes flat topo for now...
Input:
:param d2D, z0
:switch stype -> Either 'pdp' (pole-dipole) | 'dpdp' (dipole-dipole)
Output:
:figure scatter plot overlayed on image
Created on Mon December 7th, 2015
@author: dominiquef
"""
#d2D = np.asarray(d2D)
midl = []
midz = []
rho = []
for ii in range(len(Tx)):
# Get distances between each poles
rC1P1 = np.abs(Tx[ii][0] - Rx[ii][:,0])
rC2P1 = np.abs(Tx[ii][1] - Rx[ii][:,0])
rC1P2 = np.abs(Tx[ii][1] - Rx[ii][:,1])
rC2P2 = np.abs(Tx[ii][0] - Rx[ii][:,1])
rP1P2 = np.abs(Rx[ii][:,1] - Rx[ii][:,0])
# Compute apparent resistivity
if re.match(stype,'pdp'):
rho = np.hstack([rho, data[ii] * 2*np.pi * rC1P1 * ( rC1P1 + rP1P2 ) / rP1P2] )
elif re.match(stype,'dpdp'):
rho = np.hstack([rho, data[ii] * 2*np.pi / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 ) ])
Cmid = (Tx[ii][0] + Tx[ii][1])/2
Pmid = (Rx[ii][:,0] + Rx[ii][:,1])/2
midl = np.hstack([midl, ( Cmid + Pmid )/2 ])
midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + z0 ])
# Grid points
grid_x, grid_z = np.mgrid[np.min(midl):np.max(midl), np.min(midz):np.max(midz)]
grid_rho = griddata(np.c_[midl,midz], np.log10(abs(1/rho.T)), (grid_x, grid_z), method='linear')
#plt.subplot(2,1,2)
plt.imshow(grid_rho.T, extent = (np.min(midl),np.max(midl),np.min(midz),np.max(midz)), origin='lower', alpha=0.8)
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
cmin,cmax = cbar.get_clim()
ticks = np.linspace(cmin,cmax,3)
cbar.set_ticks(ticks)
# Plot apparent resistivity
plt.scatter(midl,midz,s=50,c=np.log10(abs(1/rho.T)))
def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
from SimPEG import np
import re
"""
Load in endpoints and survey specifications to generate Tx, Rx location
stations.
Assumes flat topo for now...
Input:
:param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
:object mesh -> SimPEG mesh object
:switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole)
: param a, n -> pole seperation, number of rx dipoles per tx
Output:
:param Tx, Rx -> List objects for each tx location
Lines: P1x, P1y, P1z, P2x, P2y, P2z
Created on Wed December 9th, 2015
@author: dominiquef
"""
def xy_2_r(x1,x2,y1,y2):
r = np.sqrt( np.sum((x2 - x1)**2 + (y2 - y1)**2) )
return r
## Evenly distribute electrodes and put on surface
# Mesure survey length and direction
dl_len = xy_2_r(endl[0,0],endl[1,0],endl[0,1],endl[1,1])
dl_x = ( endl[1,0] - endl[0,0] ) / dl_len
dl_y = ( endl[1,1] - endl[0,1] ) / dl_len
nstn = np.floor( dl_len / a )
# Compute discrete pole location along line
stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*a
stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*a
# Create line of P1 locations
M = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
# Create line of P2 locations
N = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
## Build list of Tx-Rx locations depending on survey type
# Dipole-dipole: Moving tx with [a] spacing -> [AB a MN1 a MN2 ... a MNn]
# Pole-dipole: Moving pole on one end -> [A a MN1 a MN2 ... MNn a B]
Tx = []
Rx = []
if not re.match(stype,'gradient'):
for ii in range(0, int(nstn)-1):
if re.match(stype,'dpdp'):
tx = np.c_[M[ii,:],N[ii,:]]
elif re.match(stype,'pdp'):
tx = np.c_[M[ii,:],M[ii,:]]
#Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
# Current elctrode seperation
AB = xy_2_r(tx[0,1],endl[1,0],tx[1,1],endl[1,1])
# Number of receivers to fit
nstn = np.min([np.floor( (AB - b) / a ) , n])
# Check if there is enough space, else break the loop
if nstn <= 0:
continue
# Compute discrete pole location along line
stn_x = N[ii,0] + dl_x*b + np.array(range(int(nstn)))*dl_x*a
stn_y = N[ii,1] + dl_y*b + np.array(range(int(nstn)))*dl_y*a
# Create receiver poles
# Create line of P1 locations
P1 = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
# Create line of P2 locations
P2 = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
Rx.append(np.c_[P1,P2])
Tx.append(tx)
#==============================================================================
# elif re.match(stype,'dpdp'):
#
# for ii in range(0, int(nstn)-2):
#
# indx = np.min([ii+n+1,nstn])
# Tx.append(np.c_[M[ii,:],N[ii,:]])
# Rx.append(np.c_[M[ii+2:indx,:],N[ii+2:indx,:]])
#==============================================================================
elif re.match(stype,'gradient'):
# Gradient survey only requires Tx at end of line and creates a square
# grid of receivers at in the middle at a pre-set minimum distance
Tx.append(np.c_[M[0,:],N[-1,:]])
# Get the edge limit of survey area
min_x = endl[0,0] + dl_x * b
min_y = endl[0,1] + dl_y * b
max_x = endl[1,0] - dl_x * b
max_y = endl[1,1] - dl_y * b
box_l = np.sqrt( (min_x - max_x)**2 + (min_y - max_y)**2 )
box_w = box_l/2.
nstn = np.floor( box_l / a )
# Compute discrete pole location along line
stn_x = min_x + np.array(range(int(nstn)))*dl_x*a
stn_y = min_y + np.array(range(int(nstn)))*dl_y*a
# Define number of cross lines
nlin = int(np.floor( box_w / a ))
lind = range(-nlin,nlin+1)
ngrad = nstn * len(lind)
rx = np.zeros([ngrad,6])
for ii in range( len(lind) ):
# Move line in perpendicular direction by dipole spacing
lxx = stn_x - lind[ii]*a*dl_y
lyy = stn_y + lind[ii]*a*dl_x
M = np.c_[ lxx, lyy , np.ones(nstn).T*mesh.vectorNz[-1]]
N = np.c_[ lxx+a*dl_x, lyy+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
rx[(ii*nstn):((ii+1)*nstn),:] = np.c_[M,N]
Rx.append(rx)
else:
print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
return Tx, Rx
def writeUBC_DCobs(fileName,Tx,Rx,d,wd, dtype):
from SimPEG import np, mkvc
import re
"""
Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
Input:
:param fileName, path to the UBC GIF 3D obs file
Output:
:param rx, tx, d, wd
:return
Created on Mon December 7th, 2015
@author: dominiquef
"""
fid = open(fileName,'w')
fid.write('! GENERAL FORMAT\n')
for ii in range(len(Tx)):
tx = np.asarray(Tx[ii])
rx = np.asarray(Rx[ii])
nrx = rx.shape[0]
fid.write('\n')
if re.match(dtype,'2D'):
for jj in range(nrx):
fid.writelines("%e " % ii for ii in mkvc(tx))
fid.writelines("%e " % ii for ii in mkvc(rx[jj]))
fid.write('%e %e\n'% (d[ii][jj],wd[ii][jj]))
#np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n')
elif re.match(dtype,'3D'):
fid.write('\n')
fid.writelines("%e " % ii for ii in mkvc(tx))
fid.write('%i\n'% nrx)
np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n')
fid.close()
def convertObs_DC3D_to_2D(Tx,Rx):
from SimPEG import np
import numpy.matlib as npm
"""
Read list of 3D Tx Rx location and change coordinate system to distance
along line assuming all data is acquired along line
First transmitter pole is assumed to be at the origin
Assumes flat topo for now...
Input:
:param Tx, Rx
Output:
:figure Tx2d, Rx2d
Created on Mon December 7th, 2015
@author: dominiquef
"""
Tx2d = []
Rx2d = []
for ii in range(len(Tx)):
if ii == 0:
endp = Tx[0][0:2,0]
nrx = Rx[ii].shape[0]
rP1 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,0] )**2 , axis=0))
rP2 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,1] )**2 , axis=0))
rC1 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,0:2] )**2 , axis=1))
rC2 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,3:5] )**2 , axis=1))
Tx2d.append( np.r_[rP1, rP2] )
Rx2d.append( np.c_[rC1, rC2] )
#np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n')
return Tx2d, Rx2d
def readUBC_DC3Dobs(fileName):
from SimPEG import np
"""
Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
Input:
:param fileName, path to the UBC GIF 3D obs file
Output:
:param rx, tx, d, wd
:return
Created on Mon December 7th, 2015
@author: dominiquef
"""
# Load file
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
# Pre-allocate
Tx = []
Rx = []
d = []
wd = []
# Countdown for number of obs/tx
count = 0
for ii in range(obsfile.shape[0]):
if not obsfile[ii]:
continue
# First line is transmitter with number of receivers
if count==0:
temp = (np.fromstring(obsfile[ii], dtype=float,sep=' ').T)
count = int(temp[-1])
temp = np.reshape(temp[0:-1],[2,3]).T
Tx.append(temp)
rx = []
continue
temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
rx.append(temp)
count = count -1
# Reach the end of
if count == 0:
temp = np.asarray(rx)
Rx.append(temp[:,0:6])
# Check for data + uncertainties
if temp.shape[1]==8:
d.append(temp[:,6])
wd.append(temp[:,7])
# Check for data only
elif temp.shape[1]==7:
d.append(temp[:,6])
return Tx, Rx, d, wd
def readUBC_DC2DLoc(fileName):
from SimPEG import np
"""
Read UBC GIF 2D observation file and generate arrays for tx-rx location
Input:
:param fileName, path to the UBC GIF 2D model file
Output:
:param rx, tx
:return
Created on Thu Nov 12 13:14:10 2015
@author: dominiquef
"""
# Open fileand skip header... assume that we know the mesh already
#==============================================================================
# fopen = open(fileName,'r')
# lines = fopen.readlines()
# fopen.close()
#==============================================================================
# Load file
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
# Check first line and figure out if 2D or 3D file format
line = np.array(obsfile[0].split(),dtype=float)
tx_A = []
tx_B = []
rx_M = []
rx_N = []
d = []
wd = []
for ii in range(obsfile.shape[0]):
# If len==3, then simple format where tx-rx is listed on each line
if len(line) == 4:
temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
tx_A = np.hstack((tx_A,temp[0]))
tx_B = np.hstack((tx_B,temp[1]))
rx_M = np.hstack((rx_M,temp[2]))
rx_N = np.hstack((rx_N,temp[3]))
rx = np.transpose(np.array((rx_M,rx_N)))
tx = np.transpose(np.array((tx_A,tx_B)))
return tx, rx, d, wd
def readUBC_DC2DMesh(fileName):
from SimPEG import np
"""
Read UBC GIF 2DTensor mesh and generate 2D Tensor mesh in simpeg
Input:
:param fileName, path to the UBC GIF mesh file
Output:
:param SimPEG TensorMesh 2D object
:return
Created on Thu Nov 12 13:14:10 2015
@author: dominiquef
"""
# Open file
fopen = open(fileName,'r')
# Read down the file and unpack dx vector
def unpackdx(fid,nrows):
for ii in range(nrows):
line = fid.readline()
var = np.array(line.split(),dtype=float)
if ii==0:
x0= var[0]
xvec = np.ones(int(var[2])) * (var[1] - var[0]) / int(var[2])
xend = var[1]
else:
xvec = np.hstack((xvec,np.ones(int(var[1])) * (var[0] - xend) / int(var[1])))
xend = var[0]
return x0, xvec
#%% Start with dx block
# First line specifies the number of rows for x-cells
line = fopen.readline()
nl = np.array(line.split(),dtype=float)
[x0, dx] = unpackdx(fopen,nl)
#%% Move down the file until reaching the z-block
line = fopen.readline()
if not line:
line = fopen.readline()
#%% End with dz block
# First line specifies the number of rows for z-cells
line = fopen.readline()
nl = np.array(line.split(),dtype=float)
[z0, dz] = unpackdx(fopen,nl)
# Flip z0 to be the bottom of the mesh for SimPEG
z0 = z0 - sum(dz)
dz = dz[::-1]
#%% Make the mesh using SimPEG
from SimPEG import Mesh
tensMsh = Mesh.TensorMesh([dx,dz],(x0, z0))
return tensMsh
simpegDCIP/Dev/DC3D_fwr_MtIsa.py
+390
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@@ -0,0 +1,390 @@
"""
Experimental script for the forward modeling of DC resistivity data
along survey lines defined by the user. The program loads in a 3D mesh
and model which is used to design pole-dipole or dipole-dipole survey
lines.
Uses SimPEG to generate the forward problem and compute the LU
factorization.
Calls DCIP2D for the inversion of a projected 2D section from the full
3D model.
Assumes flat topo for now...
Created on Mon December 7th, 2015
@author: dominiquef
"""
#%%
from SimPEG import np, Utils, Mesh, mkvc, sp
import simpegDCIP as DC
import pylab as plt
from pylab import get_current_fig_manager
from scipy.interpolate import griddata
import time
import re
import numpy.matlib as npm
#from readUBC_DC3Dobs import readUBC_DC3Dobs
#from readUBC_DC2DModel import readUBC_DC2DModel
#from writeUBC_DCobs import writeUBC_DCobs
import scipy.interpolate as interpolation
#from plot_pseudoSection import plot_pseudoSection
#from gen_DCIPsurvey import gen_DCIPsurvey
#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
import os
#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
home_dir ='C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
dsep = '\\'
#from scipy.linalg import solve_banded
# Load UBC mesh 3D
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_10m.msh')
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
# Load model
model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
#model = model**0 * 1e-2
# Specify survey type
stype = 'pdp'
# Survey parameters
a = 100
b = 100
n = 20
# Forward solver
slvr = 'BiCGStab' #'LU'
# Preconditioner
pcdr = 'Jacobi'#'Gauss-Seidel'#
# Inversion parameter
pct = 0.01
flr = 1e-4
chifact = 100
ref_mod = 1e-2
#%% Create system
#Set boundary conditions
mesh.setCellGradBC('neumann')
Div = mesh.faceDiv
Grad = mesh.cellGrad
Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
A = Div*Msig*Grad
# Change one corner to deal with nullspace
A[0,0] = 1
A = sp.csc_matrix(A)
start_time = time.time()
if re.match(slvr,'BiCGStab'):
# Create Jacobi Preconditioner
if re.match(pcdr,'Jacobi'):
dA = A.diagonal()
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
# Create Gauss-Seidel Preconditioner
elif re.match(pcdr,'Gauss-Seidel'):
LD = sp.tril(A,k=0)
#LDinv = sp.linalg.splu(LD)
elif re.match(slvr,'LU'):
# Factor A matrix
Ainv = sp.linalg.splu(A)
print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
#%% Create survey
# Display top section
top = int(mesh.nCz)-1
plt.figure()
ax_prim = plt.subplot(1,1,1)
mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
#plt.xlim([423000,424000])
#plt.ylim([546200,547000])
plt.gca().set_aspect('equal', adjustable='box')
plt.show()
cfm1=get_current_fig_manager().window
gin=[1]
# Keep creating sections until returns an empty ginput (press enter on figure)
#while bool(gin)==True:
# Bring back the plan view figure and pick points
cfm1.activateWindow()
plt.sca(ax_prim)
# Takes two points from ginput and create survey
#if re.match(stype,'gradient'):
gin = [(400.,12200.), (1400.,12200.)]
#else:
#gin = plt.ginput(2, timeout = 0)
#==============================================================================
# if not gin:
# print 'SimPED - Simulation has ended with return'
# break
#==============================================================================
# Add z coordinate to all survey... assume flat
nz = mesh.vectorNz
var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, var )
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) )
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
azm = np.arctan(dl_y/dl_x)
# Plot stations along line
plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
#%% Forward model data
data = []#np.zeros( nstn*nrx )
unct = []
problem = DC.ProblemDC_CC(mesh)
for ii in range(len(Tx)):
start_time = time.time()
# Select dipole locations for receiver
rxloc_M = np.asarray(Rx[ii][:,0:3])
rxloc_N = np.asarray(Rx[ii][:,3:])
# Number of receivers
nrx = rxloc_M.shape[0]
if not re.match(stype,'pdp'):
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
else:
# Create an "inifinity" pole
tx = np.squeeze(Tx[ii][:,0:1])
tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
# Solve for phi on pole locations
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
if re.match(slvr,'BiCGStab'):
if re.match(pcdr,'Jacobi'):
dA = A.diagonal()
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
# Iterative Solve
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
# Create Gauss-Seidel Preconditioner
elif re.match(pcdr,'Gauss-Seidel'):
LD = sp.tril(A,k=0)
phi = mkvc(Ainvb[0])
elif re.match(slvr,'LU'):
#Direct Solve
phi = Ainv.solve(RHS)
# Compute potential at each electrode
dtemp = (P1*phi - P2*phi)*np.pi
data.append( dtemp )
unct.append( np.abs(dtemp) * pct + flr)
print("--- %s seconds ---" % (time.time() - start_time))
#%% Run 2D inversion if pdp or dpdp survey
# Otherwise just plot and apparent susceptibility map
if not re.match(stype,'gradient'):
#%% Write data file in UBC-DCIP3D format
DC.writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
#%% Load 3D data
[Tx, Rx, data, wd] = DC.readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
#%% Convert 3D obs to 2D and write to file
[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
DC.writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')
#%% Create a 2D mesh along axis of Tx end points and keep z-discretization
dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
nc = np.ceil(dl_len/dx)+3
padx = dx*np.power(1.4,range(1,15))
# Creating padding cells
h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
# Create mesh with 0 coordinate centerer on the ginput points in cell center
mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
# Create array of points for interpolating from 3D to 2D mesh
xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
zz = mesh2d.vectorCCy
[XX,ZZ] = np.meshgrid(xx,zz)
[YY,ZZ] = np.meshgrid(yy,zz)
xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
#plt.scatter(xx,yy,s=20,c='y')
F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
#==============================================================================
# mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
# m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
# m2D = m3D[:,1,:]
#==============================================================================
plt.figure()
axs = plt.subplot()
plt.xlim([0,nc*dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
plt.gca().set_aspect('equal', adjustable='box')
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
#mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
#%% Plot pseudo section
DC.plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
plt.colorbar
plt.show()
#%% Create dcin2d inversion files and run
inv_dir = home_dir + '\Inv2D'
if not os.path.exists(inv_dir):
os.makedirs(inv_dir)
mshfile2d = 'Mesh_2D.msh'
modfile2d = 'MtIsa_2D.con'
obsfile2d = 'FWR_3D_2_2D.dat'
inp_file = 'dcinv2d.inp'
# Export 2D mesh
fid = open(inv_dir + dsep + mshfile2d,'w')
fid.write('%i\n'% mesh2d.nCx)
fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))
np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
fid.write('\n')
fid.write('%i\n'% mesh2d.nCy)
fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))
np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
fid.close()
# Export 2D model
fid = open(inv_dir + dsep + modfile2d,'w')
fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
fid.close()
# Export data file
DC.writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D')
# Write input file
fid = open(inv_dir + dsep + inp_file,'w')
fid.write('OBS LOC_X %s \n'% obsfile2d)
fid.write('MESH FILE %s \n'% mshfile2d)
fid.write('CHIFACT 1 %f\n'% chifact)
fid.write('TOPO DEFAULT %s \n')
fid.write('INIT_MOD DEFAULT\n')
fid.write('REF_MOD VALUE %e\n'% ref_mod)
fid.write('ALPHA DEFAULT\n')
fid.write('WEIGHT DEFAULT\n')
fid.write('STORE_ALL_MODELS FALSE\n')
fid.write('INVMODE SVD\n')
fid.write('USE_MREF TRUE\n')
fid.close()
os.chdir(inv_dir)
os.system('dcinv2d ' + inp_file)
#%%
#Load model
minv = DC.readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
plt.figure()
axs = plt.subplot()
plt.xlim([0,nc*dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
plt.gca().set_aspect('equal', adjustable='box')
minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=0.5, clim=(np.min(np.log10(m2D)),np.max(np.log10(m2D))))
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
cmin,cmax = cbar.get_clim()
ticks = np.linspace(cmin,cmax,3)
cbar.set_ticks(ticks)
#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
elif re.match(stype,'gradient'):
rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
rho = np.abs(data[0]) *np.pi *2. / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )#*((rC1P1)**2 / rP1P2)#
Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2
# Grid points
grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
#plt.subplot(2,1,2)
plt.figure()
plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z)) ,origin='lower')
var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
plt.title(var)
plt.colorbar()
plt.contour(grid_x,grid_z,grid_rho, colors='k')
simpegDCIP/Dev/DC3D_fwr_live_test.py
+11 -11
View File
@@ -28,29 +28,29 @@ from scipy.interpolate import griddata
import time
import re
import numpy.matlib as npm
from readUBC_DC3Dobs import readUBC_DC3Dobs
from readUBC_DC2DModel import readUBC_DC2DModel
from writeUBC_DCobs import writeUBC_DCobs
#from readUBC_DC3Dobs import readUBC_DC3Dobs
#from readUBC_DC2DModel import readUBC_DC2DModel
#from writeUBC_DCobs import writeUBC_DCobs
import scipy.interpolate as interpolation
from plot_pseudoSection import plot_pseudoSection
from gen_DCIPsurvey import gen_DCIPsurvey
from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
#from plot_pseudoSection import plot_pseudoSection
#from gen_DCIPsurvey import gen_DCIPsurvey
#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
import os
#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
home_dir ='C:\\LC\Private\\dominiquef\\Projects\\4414_Minsim\\Model'
home_dir ='C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
dsep = '\\'
#from scipy.linalg import solve_banded
# Load UBC mesh 3D
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_10m.msh')
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
# Load model
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
#model = model**0 * 1e-2
simpegDCIP/Dev/FWR_3D_2_2D.dat
+45 -67
View File
@@ -1,77 +1,55 @@
! GENERAL FORMAT
0.000000e+00 3.996561e+01 8.000381e+01 1.199694e+02 -1.304873e+00 1.314873e-02
0.000000e+00 3.996561e+01 1.199694e+02 1.600076e+02 -3.424035e-01 3.524035e-03
0.000000e+00 3.996561e+01 1.600076e+02 1.999732e+02 -1.402531e-01 1.502531e-03
0.000000e+00 3.996561e+01 1.999732e+02 2.400076e+02 -8.021133e-02 9.021133e-04
0.000000e+00 3.996561e+01 2.400076e+02 2.800458e+02 -5.329879e-02 6.329879e-04
0.000000e+00 3.996561e+01 2.800458e+02 3.200114e+02 -2.072487e-02 3.072487e-04
0.000000e+00 3.996561e+01 3.200114e+02 3.599770e+02 -8.478644e-03 1.847864e-04
0.000000e+00 3.996561e+01 3.599770e+02 4.000152e+02 -7.572275e-03 1.757227e-04
0.000000e+00 3.996561e+01 4.000152e+02 4.399808e+02 -4.013936e-03 1.401394e-04
0.000000e+00 3.996561e+01 4.399808e+02 4.800190e+02 -2.685902e-03 1.268590e-04
0.000000e+00 0.000000e+00 2.000000e+02 3.000000e+02 8.353735e-02 9.353735e-04
0.000000e+00 0.000000e+00 3.000000e+02 4.000000e+02 4.392504e-03 1.439250e-04
0.000000e+00 0.000000e+00 4.000000e+02 5.000000e+02 1.243627e-02 2.243627e-04
0.000000e+00 0.000000e+00 5.000000e+02 6.000000e+02 2.058920e-03 1.205892e-04
0.000000e+00 0.000000e+00 6.000000e+02 7.000000e+02 6.812879e-04 1.068129e-04
0.000000e+00 0.000000e+00 7.000000e+02 8.000000e+02 4.313826e-04 1.043138e-04
0.000000e+00 0.000000e+00 8.000000e+02 9.000000e+02 2.625458e-04 1.026255e-04
0.000000e+00 0.000000e+00 9.000000e+02 1.000000e+03 1.651300e-04 1.016513e-04
0.000000e+00 0.000000e+00 1.000000e+03 1.100000e+03 5.231997e-05 1.005232e-04
3.996561e+01 8.000381e+01 1.199694e+02 1.600076e+02 -1.259731e+00 1.269731e-02
3.996561e+01 8.000381e+01 1.600076e+02 1.999732e+02 -3.381238e-01 3.481238e-03
3.996561e+01 8.000381e+01 1.999732e+02 2.400076e+02 -1.544808e-01 1.644808e-03
3.996561e+01 8.000381e+01 2.400076e+02 2.800458e+02 -9.297726e-02 1.029773e-03
3.996561e+01 8.000381e+01 2.800458e+02 3.200114e+02 -3.427967e-02 4.427967e-04
3.996561e+01 8.000381e+01 3.200114e+02 3.599770e+02 -1.297306e-02 2.297306e-04
3.996561e+01 8.000381e+01 3.599770e+02 4.000152e+02 -1.112178e-02 2.112178e-04
3.996561e+01 8.000381e+01 4.000152e+02 4.399808e+02 -5.659736e-03 1.565974e-04
3.996561e+01 8.000381e+01 4.399808e+02 4.800190e+02 -3.546577e-03 1.354658e-04
3.996561e+01 8.000381e+01 4.800190e+02 5.199846e+02 -3.652172e-03 1.365217e-04
1.000000e+02 1.000000e+02 3.000000e+02 4.000000e+02 8.045914e-03 1.804591e-04
1.000000e+02 1.000000e+02 4.000000e+02 5.000000e+02 1.749001e-02 2.749001e-04
1.000000e+02 1.000000e+02 5.000000e+02 6.000000e+02 2.785547e-03 1.278555e-04
1.000000e+02 1.000000e+02 6.000000e+02 7.000000e+02 8.714611e-04 1.087146e-04
1.000000e+02 1.000000e+02 7.000000e+02 8.000000e+02 5.383535e-04 1.053835e-04
1.000000e+02 1.000000e+02 8.000000e+02 9.000000e+02 3.220988e-04 1.032210e-04
1.000000e+02 1.000000e+02 9.000000e+02 1.000000e+03 2.004558e-04 1.020046e-04
1.000000e+02 1.000000e+02 1.000000e+03 1.100000e+03 6.438889e-05 1.006439e-04
8.000381e+01 1.199694e+02 1.600076e+02 1.999732e+02 -1.298964e+00 1.308964e-02
8.000381e+01 1.199694e+02 1.999732e+02 2.400076e+02 -3.679137e-01 3.779137e-03
8.000381e+01 1.199694e+02 2.400076e+02 2.800458e+02 -1.840285e-01 1.940285e-03
8.000381e+01 1.199694e+02 2.800458e+02 3.200114e+02 -6.292465e-02 7.292465e-04
8.000381e+01 1.199694e+02 3.200114e+02 3.599770e+02 -2.173162e-02 3.173162e-04
8.000381e+01 1.199694e+02 3.599770e+02 4.000152e+02 -1.774587e-02 2.774587e-04
8.000381e+01 1.199694e+02 4.000152e+02 4.399808e+02 -8.601492e-03 1.860149e-04
8.000381e+01 1.199694e+02 4.399808e+02 4.800190e+02 -4.934796e-03 1.493480e-04
8.000381e+01 1.199694e+02 4.800190e+02 5.199846e+02 -4.779958e-03 1.477996e-04
2.000000e+02 2.000000e+02 4.000000e+02 5.000000e+02 2.987416e-02 3.987416e-04
2.000000e+02 2.000000e+02 5.000000e+02 6.000000e+02 4.374492e-03 1.437449e-04
2.000000e+02 2.000000e+02 6.000000e+02 7.000000e+02 1.244707e-03 1.124471e-04
2.000000e+02 2.000000e+02 7.000000e+02 8.000000e+02 7.390806e-04 1.073908e-04
2.000000e+02 2.000000e+02 8.000000e+02 9.000000e+02 4.306959e-04 1.043070e-04
2.000000e+02 2.000000e+02 9.000000e+02 1.000000e+03 2.629704e-04 1.026297e-04
2.000000e+02 2.000000e+02 1.000000e+03 1.100000e+03 8.356921e-05 1.008357e-04
1.199694e+02 1.600076e+02 1.999732e+02 2.400076e+02 -1.321899e+00 1.331899e-02
1.199694e+02 1.600076e+02 2.400076e+02 2.800458e+02 -4.394093e-01 4.494093e-03
1.199694e+02 1.600076e+02 2.800458e+02 3.200114e+02 -1.314934e-01 1.414934e-03
1.199694e+02 1.600076e+02 3.200114e+02 3.599770e+02 -4.008058e-02 5.008058e-04
1.199694e+02 1.600076e+02 3.599770e+02 4.000152e+02 -3.095258e-02 4.095258e-04
1.199694e+02 1.600076e+02 4.000152e+02 4.399808e+02 -1.426874e-02 2.426874e-04
1.199694e+02 1.600076e+02 4.399808e+02 4.800190e+02 -7.372283e-03 1.737228e-04
1.199694e+02 1.600076e+02 4.800190e+02 5.199846e+02 -6.588671e-03 1.658867e-04
3.000000e+02 3.000000e+02 5.000000e+02 6.000000e+02 7.102682e-03 1.710268e-04
3.000000e+02 3.000000e+02 6.000000e+02 7.000000e+02 1.756322e-03 1.175632e-04
3.000000e+02 3.000000e+02 7.000000e+02 8.000000e+02 9.969346e-04 1.099693e-04
3.000000e+02 3.000000e+02 8.000000e+02 9.000000e+02 5.678287e-04 1.056783e-04
3.000000e+02 3.000000e+02 9.000000e+02 1.000000e+03 3.423076e-04 1.034231e-04
3.000000e+02 3.000000e+02 1.000000e+03 1.100000e+03 1.100753e-04 1.011008e-04
1.600076e+02 1.999732e+02 2.400076e+02 2.800458e+02 -1.472708e+00 1.482708e-02
1.600076e+02 1.999732e+02 2.800458e+02 3.200114e+02 -3.274511e-01 3.374511e-03
1.600076e+02 1.999732e+02 3.200114e+02 3.599770e+02 -8.304805e-02 9.304805e-04
1.600076e+02 1.999732e+02 3.599770e+02 4.000152e+02 -5.936226e-02 6.936226e-04
1.600076e+02 1.999732e+02 4.000152e+02 4.399808e+02 -2.590406e-02 3.590406e-04
1.600076e+02 1.999732e+02 4.399808e+02 4.800190e+02 -1.208626e-02 2.208626e-04
1.600076e+02 1.999732e+02 4.800190e+02 5.199846e+02 -9.897218e-03 1.989722e-04
4.000000e+02 4.000000e+02 6.000000e+02 7.000000e+02 3.330609e-03 1.333061e-04
4.000000e+02 4.000000e+02 7.000000e+02 8.000000e+02 1.534079e-03 1.153408e-04
4.000000e+02 4.000000e+02 8.000000e+02 9.000000e+02 7.882245e-04 1.078822e-04
4.000000e+02 4.000000e+02 9.000000e+02 1.000000e+03 4.439992e-04 1.044400e-04
4.000000e+02 4.000000e+02 1.000000e+03 1.100000e+03 1.345726e-04 1.013457e-04
1.999732e+02 2.400076e+02 2.800458e+02 3.200114e+02 -1.224327e+00 1.234327e-02
1.999732e+02 2.400076e+02 3.200114e+02 3.599770e+02 -2.092444e-01 2.192444e-03
1.999732e+02 2.400076e+02 3.599770e+02 4.000152e+02 -1.298913e-01 1.398913e-03
1.999732e+02 2.400076e+02 4.000152e+02 4.399808e+02 -5.279369e-02 6.279369e-04
1.999732e+02 2.400076e+02 4.399808e+02 4.800190e+02 -2.217701e-02 3.217701e-04
1.999732e+02 2.400076e+02 4.800190e+02 5.199846e+02 -1.652061e-02 2.652061e-04
5.000000e+02 5.000000e+02 7.000000e+02 8.000000e+02 6.629023e-03 1.662902e-04
5.000000e+02 5.000000e+02 8.000000e+02 9.000000e+02 2.814898e-03 1.281490e-04
5.000000e+02 5.000000e+02 9.000000e+02 1.000000e+03 1.424089e-03 1.142409e-04
5.000000e+02 5.000000e+02 1.000000e+03 1.100000e+03 4.141629e-04 1.041416e-04
2.400076e+02 2.800458e+02 3.200114e+02 3.599770e+02 -6.840422e-01 6.940422e-03
2.400076e+02 2.800458e+02 3.599770e+02 4.000152e+02 -2.894901e-01 2.994901e-03
2.400076e+02 2.800458e+02 4.000152e+02 4.399808e+02 -9.905137e-02 1.090514e-03
2.400076e+02 2.800458e+02 4.399808e+02 4.800190e+02 -3.599753e-02 4.599753e-04
2.400076e+02 2.800458e+02 4.800190e+02 5.199846e+02 -2.367732e-02 3.367732e-04
6.000000e+02 6.000000e+02 8.000000e+02 9.000000e+02 4.338127e-03 1.433813e-04
6.000000e+02 6.000000e+02 9.000000e+02 1.000000e+03 1.980291e-03 1.198029e-04
6.000000e+02 6.000000e+02 1.000000e+03 1.100000e+03 5.550423e-04 1.055504e-04
2.800458e+02 3.200114e+02 3.599770e+02 4.000152e+02 -5.269723e-01 5.369723e-03
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simpegDCIP/Dev/FWR_data3D.dat
+54 -80
View File
@@ -1,99 +1,73 @@
! GENERAL FORMAT
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9.99873E-04 9.99872E-04 9.99869E-04 9.99857E-04 9.99808E-04 9.99615E-04 9.99006E-04 9.97667E-04 9.96016E-04 9.95535E-04 9.97139E-04 1.00009E-03 1.00322E-03 1.00602E-03 1.00823E-03 1.01025E-03 1.01230E-03 1.01438E-03 1.01655E-03 1.01869E-03 1.02083E-03 1.02290E-03 1.02481E-03 1.02650E-03 1.02795E-03 1.02910E-03 1.02993E-03 1.03041E-03 1.03054E-03 1.03027E-03 1.02967E-03 1.02868E-03 1.02738E-03 1.02576E-03 1.02391E-03 1.02179E-03 1.01956E-03 1.01717E-03 1.01474E-03 1.01236E-03 1.01005E-03 1.00784E-03 1.00578E-03 1.00386E-03 1.00174E-03 9.99333E-04 9.96802E-04 9.95061E-04 9.94663E-04 9.95980E-04 9.97858E-04 9.99124E-04 9.99651E-04 9.99815E-04 9.99859E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99869E-04 9.99858E-04 9.99811E-04 9.99639E-04 9.99137E-04 9.98138E-04 9.97020E-04 9.96817E-04 9.97718E-04 9.99286E-04 1.00092E-03 1.00235E-03 1.00340E-03 1.00444E-03 1.00548E-03 1.00662E-03 1.00779E-03 1.00891E-03 1.01000E-03 1.01097E-03 1.01187E-03 1.01263E-03 1.01329E-03 1.01379E-03 1.01414E-03 1.01432E-03 1.01433E-03 1.01414E-03 1.01381E-03 1.01328E-03 1.01264E-03 1.01184E-03 1.01096E-03 1.00997E-03 1.00892E-03 1.00783E-03 1.00669E-03 1.00557E-03 1.00444E-03 1.00331E-03 1.00232E-03 1.00133E-03 1.00027E-03 9.98931E-04 9.97576E-04 9.96510E-04 9.96198E-04 9.96980E-04 9.98272E-04 9.99232E-04 9.99671E-04 9.99818E-04 9.99859E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99869E-04 9.99858E-04 9.99815E-04 9.99663E-04 9.99253E-04 9.98500E-04 9.97721E-04 9.97555E-04 9.98158E-04 9.98965E-04 9.99886E-04 1.00059E-03 1.00122E-03 1.00177E-03 1.00236E-03 1.00297E-03 1.00356E-03 1.00411E-03 1.00464E-03 1.00512E-03 1.00557E-03 1.00594E-03 1.00627E-03 1.00651E-03 1.00668E-03 1.00676E-03 1.00675E-03 1.00667E-03 1.00649E-03 1.00623E-03 1.00589E-03 1.00547E-03 1.00501E-03 1.00451E-03 1.00398E-03 1.00343E-03 1.00288E-03 1.00229E-03 1.00175E-03 1.00113E-03 1.00059E-03 1.00004E-03 9.99454E-04 9.98741E-04 9.97997E-04 9.97374E-04 9.97164E-04 9.97707E-04 9.98602E-04 9.99328E-04 9.99690E-04 9.99821E-04 9.99860E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99870E-04 9.99859E-04 9.99818E-04 9.99685E-04 9.99354E-04 9.98801E-04 9.98268E-04 9.98142E-04 9.98421E-04 9.98906E-04 9.99356E-04 9.99790E-04 1.00015E-03 1.00045E-03 1.00073E-03 1.00102E-03 1.00129E-03 1.00155E-03 1.00178E-03 1.00203E-03 1.00222E-03 1.00242E-03 1.00255E-03 1.00267E-03 1.00274E-03 1.00279E-03 1.00277E-03 1.00274E-03 1.00264E-03 1.00252E-03 1.00233E-03 1.00214E-03 1.00188E-03 1.00163E-03 1.00134E-03 1.00107E-03 1.00077E-03 1.00051E-03 1.00020E-03 9.99948E-04 9.99648E-04 9.99410E-04 9.99075E-04 9.98675E-04 9.98296E-04 9.97980E-04 9.97899E-04 9.98249E-04 9.98868E-04 9.99413E-04 9.99709E-04 9.99824E-04 9.99860E-04 9.99870E-04 9.99873E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99870E-04 9.99859E-04 9.99823E-04 9.99713E-04 9.99464E-04 9.99088E-04 9.98749E-04 9.98643E-04 9.98761E-04 9.98980E-04 9.99201E-04 9.99406E-04 9.99555E-04 9.99669E-04 9.99786E-04 9.99898E-04 1.00001E-03 1.00011E-03 1.00022E-03 1.00030E-03 1.00039E-03 1.00045E-03 1.00051E-03 1.00055E-03 1.00058E-03 1.00059E-03 1.00059E-03 1.00056E-03 1.00052E-03 1.00045E-03 1.00039E-03 1.00029E-03 1.00020E-03 1.00008E-03 9.99959E-04 9.99828E-04 9.99706E-04 9.99583E-04 9.99469E-04 9.99350E-04 9.99241E-04 9.99129E-04 9.99021E-04 9.98853E-04 9.98673E-04 9.98553E-04 9.98499E-04 9.98763E-04 9.99138E-04 9.99506E-04 9.99732E-04 9.99829E-04 9.99861E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99870E-04 9.99860E-04 9.99829E-04 9.99744E-04 9.99574E-04 9.99353E-04 9.99185E-04 9.99094E-04 9.99137E-04 9.99203E-04 9.99274E-04 9.99339E-04 9.99382E-04 9.99417E-04 9.99454E-04 9.99490E-04 9.99527E-04 9.99565E-04 9.99594E-04 9.99622E-04 9.99647E-04 9.99668E-04 9.99684E-04 9.99697E-04 9.99702E-04 9.99703E-04 9.99697E-04 9.99687E-04 9.99670E-04 9.99649E-04 9.99623E-04 9.99593E-04 9.99559E-04 9.99522E-04 9.99481E-04 9.99439E-04 9.99396E-04 9.99353E-04 9.99313E-04 9.99276E-04 9.99241E-04 9.99207E-04 9.99166E-04 9.99119E-04 9.99069E-04 9.99047E-04 9.99059E-04 9.99196E-04 9.99377E-04 9.99601E-04 9.99758E-04 9.99834E-04 9.99861E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99870E-04 9.99862E-04 9.99838E-04 9.99783E-04 9.99690E-04 9.99585E-04 9.99516E-04 9.99476E-04 9.99470E-04 9.99473E-04 9.99482E-04 9.99487E-04 9.99490E-04 9.99494E-04 9.99499E-04 9.99504E-04 9.99509E-04 9.99512E-04 9.99515E-04 9.99518E-04 9.99521E-04 9.99523E-04 9.99525E-04 9.99525E-04 9.99525E-04 9.99525E-04 9.99523E-04 9.99521E-04 9.99518E-04 9.99515E-04 9.99510E-04 9.99506E-04 9.99501E-04 9.99495E-04 9.99490E-04 9.99484E-04 9.99478E-04 9.99472E-04 9.99466E-04 9.99461E-04 9.99456E-04 9.99453E-04 9.99450E-04 9.99444E-04 9.99440E-04 9.99443E-04 9.99465E-04 9.99515E-04 9.99596E-04 9.99705E-04 9.99791E-04 9.99841E-04 9.99863E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99872E-04 9.99870E-04 9.99864E-04 9.99849E-04 9.99823E-04 9.99788E-04 9.99750E-04 9.99721E-04 9.99703E-04 9.99691E-04 9.99684E-04 9.99679E-04 9.99676E-04 9.99674E-04 9.99673E-04 9.99672E-04 9.99670E-04 9.99669E-04 9.99667E-04 9.99667E-04 9.99666E-04 9.99665E-04 9.99664E-04 9.99663E-04 9.99663E-04 9.99663E-04 9.99662E-04 9.99662E-04 9.99662E-04 9.99662E-04 9.99662E-04 9.99662E-04 9.99663E-04 9.99663E-04 9.99664E-04 9.99665E-04 9.99665E-04 9.99666E-04 9.99667E-04 9.99669E-04 9.99670E-04 9.99671E-04 9.99673E-04 9.99675E-04 9.99679E-04 9.99684E-04 9.99692E-04 9.99704E-04 9.99724E-04 9.99755E-04 9.99793E-04 9.99827E-04 9.99851E-04 9.99865E-04 9.99870E-04 9.99872E-04 9.99873E-04
9.99873E-04 9.99873E-04 9.99871E-04 9.99866E-04 9.99857E-04 9.99845E-04 9.99831E-04 9.99814E-04 9.99797E-04 9.99783E-04 9.99773E-04 9.99766E-04 9.99761E-04 9.99758E-04 9.99756E-04 9.99754E-04 9.99753E-04 9.99751E-04 9.99750E-04 9.99748E-04 9.99747E-04 9.99746E-04 9.99746E-04 9.99745E-04 9.99744E-04 9.99744E-04 9.99744E-04 9.99743E-04 9.99743E-04 9.99743E-04 9.99744E-04 9.99744E-04 9.99745E-04 9.99745E-04 9.99746E-04 9.99747E-04 9.99749E-04 9.99750E-04 9.99751E-04 9.99753E-04 9.99754E-04 9.99756E-04 9.99758E-04 9.99759E-04 9.99762E-04 9.99765E-04 9.99770E-04 9.99777E-04 9.99787E-04 9.99800E-04 9.99816E-04 9.99832E-04 9.99846E-04 9.99858E-04 9.99866E-04 9.99871E-04 9.99873E-04 9.99873E-04
81 45
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00422E-02 1.01036E-02 1.02324E-02 1.04313E-02 1.05054E-02 9.84770E-03 7.88758E-03 5.21056E-03 3.16670E-03 2.48044E-03 4.75998E-03 8.77166E-03 1.15206E-02 1.31677E-02 1.39808E-02 1.43267E-02 1.45905E-02 1.40787E-02 1.25439E-02 9.39334E-03 4.25422E-03 1.94679E-03 3.74368E-03 9.57644E-03 2.12550E-02 2.66451E-02 2.18952E-02 1.83729E-02 1.30285E-02 8.01519E-03 4.19143E-03 1.75255E-03 1.06087E-03 1.20231E-03 2.65494E-03 9.73502E-03 1.56866E-02 1.41785E-02 1.32011E-02 1.92029E-02 6.02855E-02 1.15763E-01 8.41411E-02 3.94050E-02 2.42574E-02 7.98561E-02 1.75843E-01 1.64939E-01 1.09164E-01 7.05542E-02 9.83501E-02 1.72424E-01 2.07011E-01 2.15154E-01 1.99924E-01 1.48401E-01 1.24690E-01 1.34185E-01 1.41765E-01 1.43873E-01 1.43735E-01 1.58018E-01 1.52491E-01 1.19719E-01 4.71725E-02 2.31558E-03 1.58827E-03 3.30763E-03 6.13008E-03 8.30077E-03 9.35467E-03 9.77369E-03 9.92676E-03 9.97879E-03 9.99530E-03 1.00003E-02 1.00019E-02
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00422E-02 1.01035E-02 1.02323E-02 1.04314E-02 1.05094E-02 9.87256E-03 7.96995E-03 5.37823E-03 3.41942E-03 2.79696E-03 3.94421E-03 5.76043E-03 7.56965E-03 8.90061E-03 9.58321E-03 9.67705E-03 9.29751E-03 8.32677E-03 6.73106E-03 4.74795E-03 3.01345E-03 2.42512E-03 4.49935E-03 1.05101E-02 2.20729E-02 3.25799E-02 3.31792E-02 2.74602E-02 1.83159E-02 1.04396E-02 5.20156E-03 2.37644E-03 1.45881E-03 1.60773E-03 3.22847E-03 8.60311E-03 1.46341E-02 1.58886E-02 1.65150E-02 2.37386E-02 5.24371E-02 9.00341E-02 8.25175E-02 4.98898E-02 3.41591E-02 5.47403E-02 8.43579E-02 9.22769E-02 7.65582E-02 5.95454E-02 6.32497E-02 8.12883E-02 9.90527E-02 1.09008E-01 1.09162E-01 1.02647E-01 1.01153E-01 1.07025E-01 1.11720E-01 1.14580E-01 1.18622E-01 1.20448E-01 1.04621E-01 7.01147E-02 2.51409E-02 2.48372E-03 1.67636E-03 3.36908E-03 6.15785E-03 8.30813E-03 9.35617E-03 9.77398E-03 9.92682E-03 9.97880E-03 9.99531E-03 1.00003E-02 1.00019E-02
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00421E-02 1.01034E-02 1.02319E-02 1.04315E-02 1.05172E-02 9.92060E-03 8.12690E-03 5.68973E-03 3.84958E-03 3.20542E-03 3.62160E-03 4.34385E-03 5.14488E-03 5.77386E-03 6.06720E-03 5.98219E-03 5.56410E-03 4.87831E-03 4.04280E-03 3.27009E-03 2.85106E-03 3.27915E-03 5.99386E-03 1.29046E-02 2.62345E-02 4.26231E-02 5.00437E-02 4.24503E-02 2.71828E-02 1.45784E-02 7.11443E-03 3.49180E-03 2.21726E-03 2.30495E-03 3.91320E-03 8.14376E-03 1.38028E-02 1.76511E-02 2.08780E-02 2.92305E-02 5.06542E-02 7.80191E-02 8.31830E-02 6.39127E-02 4.79202E-02 5.01202E-02 5.63749E-02 5.76996E-02 5.19335E-02 4.44732E-02 4.25197E-02 4.62284E-02 5.25981E-02 5.89998E-02 6.43205E-02 6.92673E-02 7.51651E-02 8.14398E-02 8.61647E-02 8.90957E-02 8.97058E-02 8.31005E-02 6.58880E-02 4.08223E-02 1.51413E-02 2.71282E-03 1.84397E-03 3.48862E-03 6.21228E-03 8.32284E-03 9.35912E-03 9.77452E-03 9.92693E-03 9.97882E-03 9.99531E-03 1.00003E-02 1.00019E-02
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00421E-02 1.01032E-02 1.02315E-02 1.04317E-02 1.05284E-02 9.98882E-03 8.34606E-03 6.11522E-03 4.41055E-03 3.70443E-03 3.70021E-03 3.89851E-03 4.15980E-03 4.35676E-03 4.40075E-03 4.26392E-03 3.97902E-03 3.61869E-03 3.28779E-03 3.13645E-03 3.41053E-03 4.67680E-03 8.35227E-03 1.66857E-02 3.23497E-02 5.33561E-02 6.60063E-02 5.73923E-02 3.66617E-02 1.94846E-02 9.69018E-03 5.06710E-03 3.32848E-03 3.24595E-03 4.67593E-03 8.09652E-03 1.31668E-02 1.84597E-02 2.41469E-02 3.36487E-02 5.13026E-02 7.33718E-02 8.44052E-02 7.64492E-02 6.26642E-02 5.56927E-02 5.22465E-02 4.86043E-02 4.35455E-02 3.84742E-02 3.58061E-02 3.62296E-02 3.90283E-02 4.33280E-02 4.86337E-02 5.47805E-02 6.12618E-02 6.68866E-02 7.04434E-02 7.11191E-02 6.76281E-02 5.81469E-02 4.38469E-02 2.69140E-02 1.11222E-02 3.02076E-03 2.08821E-03 3.66153E-03 6.29123E-03 8.34424E-03 9.36342E-03 9.77529E-03 9.92706E-03 9.97884E-03 9.99532E-03 1.00003E-02 1.00019E-02
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1.00031E-02 1.00040E-02 1.00060E-02 1.00099E-02 1.00162E-02 1.00243E-02 1.00328E-02 1.00402E-02 1.00458E-02 1.00497E-02 1.00521E-02 1.00537E-02 1.00546E-02 1.00552E-02 1.00556E-02 1.00559E-02 1.00561E-02 1.00563E-02 1.00565E-02 1.00566E-02 1.00567E-02 1.00568E-02 1.00568E-02 1.00568E-02 1.00567E-02 1.00567E-02 1.00565E-02 1.00563E-02 1.00561E-02 1.00558E-02 1.00555E-02 1.00551E-02 1.00547E-02 1.00542E-02 1.00537E-02 1.00532E-02 1.00526E-02 1.00519E-02 1.00512E-02 1.00505E-02 1.00497E-02 1.00489E-02 1.00480E-02 1.00471E-02 1.00461E-02 1.00451E-02 1.00441E-02 1.00431E-02 1.00420E-02 1.00408E-02 1.00396E-02 1.00384E-02 1.00372E-02 1.00359E-02 1.00346E-02 1.00333E-02 1.00320E-02 1.00307E-02 1.00293E-02 1.00280E-02 1.00266E-02 1.00252E-02 1.00239E-02 1.00225E-02 1.00211E-02 1.00197E-02 1.00184E-02 1.00168E-02 1.00145E-02 1.00114E-02 1.00074E-02 1.00021E-02 9.99590E-03 9.98953E-03 9.98484E-03 9.98408E-03 9.98784E-03 9.99362E-03 9.99826E-03 1.00008E-02 1.00019E-02
1.00031E-02 1.00039E-02 1.00056E-02 1.00086E-02 1.00126E-02 1.00170E-02 1.00210E-02 1.00241E-02 1.00261E-02 1.00274E-02 1.00281E-02 1.00285E-02 1.00288E-02 1.00289E-02 1.00289E-02 1.00290E-02 1.00290E-02 1.00290E-02 1.00290E-02 1.00290E-02 1.00290E-02 1.00289E-02 1.00289E-02 1.00288E-02 1.00287E-02 1.00286E-02 1.00285E-02 1.00283E-02 1.00282E-02 1.00280E-02 1.00278E-02 1.00276E-02 1.00274E-02 1.00271E-02 1.00269E-02 1.00266E-02 1.00263E-02 1.00260E-02 1.00257E-02 1.00253E-02 1.00250E-02 1.00247E-02 1.00243E-02 1.00238E-02 1.00234E-02 1.00230E-02 1.00226E-02 1.00221E-02 1.00217E-02 1.00212E-02 1.00207E-02 1.00202E-02 1.00197E-02 1.00191E-02 1.00186E-02 1.00180E-02 1.00175E-02 1.00169E-02 1.00164E-02 1.00158E-02 1.00152E-02 1.00147E-02 1.00141E-02 1.00135E-02 1.00129E-02 1.00123E-02 1.00117E-02 1.00110E-02 1.00100E-02 1.00087E-02 1.00068E-02 1.00044E-02 1.00013E-02 9.99777E-03 9.99449E-03 9.99262E-03 9.99320E-03 9.99588E-03 9.99890E-03 1.00010E-02 1.00020E-02
1.00031E-02 1.00038E-02 1.00053E-02 1.00074E-02 1.00099E-02 1.00120E-02 1.00136E-02 1.00146E-02 1.00152E-02 1.00155E-02 1.00156E-02 1.00156E-02 1.00157E-02 1.00157E-02 1.00156E-02 1.00156E-02 1.00156E-02 1.00156E-02 1.00156E-02 1.00155E-02 1.00155E-02 1.00155E-02 1.00154E-02 1.00154E-02 1.00153E-02 1.00152E-02 1.00152E-02 1.00151E-02 1.00150E-02 1.00149E-02 1.00149E-02 1.00148E-02 1.00147E-02 1.00146E-02 1.00145E-02 1.00144E-02 1.00143E-02 1.00142E-02 1.00140E-02 1.00139E-02 1.00138E-02 1.00136E-02 1.00135E-02 1.00133E-02 1.00132E-02 1.00130E-02 1.00129E-02 1.00127E-02 1.00125E-02 1.00123E-02 1.00122E-02 1.00120E-02 1.00118E-02 1.00116E-02 1.00114E-02 1.00112E-02 1.00110E-02 1.00108E-02 1.00106E-02 1.00103E-02 1.00101E-02 1.00099E-02 1.00097E-02 1.00094E-02 1.00092E-02 1.00090E-02 1.00087E-02 1.00084E-02 1.00080E-02 1.00075E-02 1.00067E-02 1.00056E-02 1.00040E-02 1.00021E-02 9.99987E-03 9.99783E-03 9.99688E-03 9.99765E-03 9.99947E-03 1.00011E-02 1.00020E-02
simpegDCIP/Dev/Inv2D/dcinv2d.inp
+1 -1
View File
@@ -3,7 +3,7 @@ MESH FILE Mesh_2D.msh
CHIFACT 1 100.000000
TOPO DEFAULT %s
INIT_MOD DEFAULT
REF_MOD VALUE 1.000000e-03
REF_MOD VALUE 1.000000e-02
ALPHA DEFAULT
WEIGHT DEFAULT
STORE_ALL_MODELS FALSE
simpegDCIP/Dev/Inv2D/dcinv2d.log
+209 -236
View File
@@ -14,7 +14,7 @@ Distributed by:
Mira Geoscience Ltd.
DCINV2D started on:12/14/2015 14:39:32
DCINV2D started on: 1/12/2016 16:42:18
Reading input file: dcinv2d.inp
@@ -25,7 +25,7 @@ MESH FILE Mesh_2D.msh
CHIFACT 1 100.000000
TOPO DEFAULT %s
INIT_MOD DEFAULT
REF_MOD VALUE 1.000000e-03
REF_MOD VALUE 1.000000e-02
ALPHA DEFAULT
WEIGHT DEFAULT
STORE_ALL_MODELS FALSE
@@ -35,21 +35,21 @@ USE_MREF TRUE
maximum # of iterations: 100
data were read from: FWR_3D_2_2D.dat
# of current locations: 11
# of data: 65
# of current locations: 9
# of data: 45
chifact: 1.00000E+00
target misfit: 6.50000E+01
target misfit: 4.50000E+01
mesh was read from: Mesh_2D.msh
# of cells: 58 x 30
total # of cells: 1740
# of active cells: 1740
# of cells: 81 x 45
total # of cells: 3645
# of active cells: 3645
# of unique data locations: 11
# of unique data locations: 9
# of wave values: 13
2.5000E-04 4.9901E-04 9.9606E-04 1.9882E-03 3.9685E-03 7.9213E-03 1.5811E-02 3.1560E-02 6.2996E-02 1.2574E-01 2.5099E-01 5.0099E-01 1.0000E+00
reference conductivity model is set to a constant: 1.000000E-03
reference conductivity model is set to a constant: 1.000000E-02
initial model is set to the reference model.
using default length scales (Lx, Lz): ( 8.00000E+01, 8.00000E+01)
@@ -57,12 +57,12 @@ corresponding alpha (a_s, a_x, a_z): ( 1.56250E-04, 1.0000E+00, 1.0000E+00)
Using basis vectors and SVD.
reference model will be used in the derivative terms.
number of basis vectors: 21 + 3 + 1 = 25
number of basis vectors: 17 + 3 + 1 = 21
init cpu time: 0:00:00.05
init cpu time: 0:00:00.18
initial misfit = 2.29963E+05
initial misfit = 2.57080E+05
init. model norm = 0.00000E+00
norm comp Ws = 0.00000E+00
norm comp Wx = 0.00000E+00
@@ -73,365 +73,338 @@ Iteration 1
beta vs. misfit:
beta misfit
2.76242E+04 4.25611E+04
5.52485E+04 5.73807E+04
1.38121E+05 9.14695E+04
2.16516E+05 1.14020E+05
2.20257E+05 1.14925E+05
2.20495E+05 1.14982E+05
5.51237E+05 1.62414E+05
1.11493E+03 4.74485E+04
2.22985E+03 5.68364E+04
5.57463E+03 8.02516E+04
1.39366E+04 1.21526E+05
1.57750E+04 1.28279E+05
1.58488E+04 1.28536E+05
1.58499E+04 1.28540E+05
3.96247E+04 1.79392E+05
chosen beta = 2.20496E+05
target misfit = 1.14982E+05
achieved misfit = 1.14982E+05
model norm = 2.23402E-01
chosen beta = 1.58499E+04
target misfit = 1.28540E+05
achieved misfit = 1.28540E+05
model norm = 3.63297E+00
misfit change = 5.00000E-01
model norm change = 0.00000E+00
norm comp Ws = 1.20709E-01
norm comp Wx = 5.36664E-02
norm comp Wz = 4.90259E-02
norm comp Ws = 2.88514E+00
norm comp Wx = 3.70226E-01
norm comp Wz = 3.77603E-01
iter cpu time: 0:00:00.35
iter cpu time: 0:00:01.20
Iteration 2
beta vs. misfit:
beta misfit
5.51240E+04 5.38776E+04
1.10248E+05 8.30234E+04
3.96247E+03 6.15053E+04
7.92494E+03 9.46707E+04
chosen beta = 6.11687E+04
target misfit = 5.74909E+04
achieved misfit = 5.74953E+04
model norm = 8.13417E-01
misfit change = 4.99961E-01
model norm change = 2.64105E+00
chosen beta = 4.25263E+03
target misfit = 6.42701E+04
achieved misfit = 6.42774E+04
model norm = 1.37169E+01
misfit change = 4.99943E-01
model norm change = 2.77567E+00
norm comp Ws = 4.21387E-01
norm comp Wx = 2.22769E-01
norm comp Wz = 1.69261E-01
norm comp Ws = 1.06979E+01
norm comp Wx = 1.61088E+00
norm comp Wz = 1.40814E+00
iter cpu time: 0:00:00.19
iter cpu time: 0:00:00.78
Iteration 3
beta vs. misfit:
beta misfit
1.52922E+04 2.43015E+04
3.05844E+04 3.74992E+04
1.06316E+03 2.49833E+04
2.12631E+03 4.10044E+04
chosen beta = 2.00018E+04
target misfit = 2.87477E+04
achieved misfit = 2.84776E+04
model norm = 1.74401E+00
misfit change = 5.04697E-01
model norm change = 1.14405E+00
chosen beta = 1.51222E+03
target misfit = 3.21387E+04
achieved misfit = 3.17674E+04
model norm = 2.91306E+01
misfit change = 5.05777E-01
model norm change = 1.12370E+00
norm comp Ws = 8.31643E-01
norm comp Wx = 5.75399E-01
norm comp Wz = 3.36966E-01
norm comp Ws = 2.19928E+01
norm comp Wx = 4.11507E+00
norm comp Wz = 3.02271E+00
iter cpu time: 0:00:00.45
iter cpu time: 0:00:01.00
Iteration 4
beta vs. misfit:
beta misfit
5.00045E+03 1.28642E+04
1.00009E+04 1.86842E+04
3.78054E+02 1.14051E+04
7.56108E+02 1.87488E+04
chosen beta = 6.03799E+03
target misfit = 1.42388E+04
achieved misfit = 1.41388E+04
model norm = 3.13263E+00
misfit change = 5.03513E-01
model norm change = 7.96223E-01
chosen beta = 6.00000E+02
target misfit = 1.58837E+04
achieved misfit = 1.56569E+04
model norm = 4.84312E+01
misfit change = 5.07137E-01
model norm change = 6.62554E-01
norm comp Ws = 1.28958E+00
norm comp Wx = 1.28927E+00
norm comp Wz = 5.53778E-01
norm comp Ws = 3.49213E+01
norm comp Wx = 8.32898E+00
norm comp Wz = 5.18098E+00
iter cpu time: 0:00:00.21
iter cpu time: 0:00:00.86
Iteration 5
beta vs. misfit:
beta misfit
1.50950E+03 5.99773E+03
3.01900E+03 9.21452E+03
1.50000E+02 5.05151E+03
3.00000E+02 8.76679E+03
chosen beta = 1.96825E+03
target misfit = 7.06938E+03
achieved misfit = 7.12029E+03
model norm = 5.18734E+00
misfit change = 4.96399E-01
model norm change = 6.55905E-01
chosen beta = 2.60199E+02
target misfit = 7.82847E+03
achieved misfit = 7.79602E+03
model norm = 6.97577E+01
misfit change = 5.02073E-01
model norm change = 4.40344E-01
norm comp Ws = 1.74210E+00
norm comp Wx = 2.47066E+00
norm comp Wz = 9.74574E-01
norm comp Ws = 4.74916E+01
norm comp Wx = 1.43993E+01
norm comp Wz = 7.86671E+00
iter cpu time: 0:00:00.27
iter cpu time: 0:00:00.88
Iteration 6
beta vs. misfit:
beta misfit
4.92062E+02 2.05275E+03
9.84123E+02 3.92917E+03
6.50498E+01 1.83324E+03
1.30100E+02 3.68412E+03
1.37599E+02 3.90387E+03
chosen beta = 8.85768E+02
target misfit = 3.56015E+03
achieved misfit = 3.58474E+03
model norm = 7.76393E+00
misfit change = 4.96546E-01
model norm change = 4.96708E-01
chosen beta = 1.37400E+02
target misfit = 3.89801E+03
achieved misfit = 3.89800E+03
model norm = 8.95751E+01
misfit change = 5.00001E-01
model norm change = 2.84090E-01
norm comp Ws = 2.19885E+00
norm comp Wx = 3.77400E+00
norm comp Wz = 1.79107E+00
norm comp Ws = 5.75580E+01
norm comp Wx = 2.11440E+01
norm comp Wz = 1.08731E+01
iter cpu time: 0:00:00.21
iter cpu time: 0:00:00.81
Iteration 7
beta vs. misfit:
beta misfit
2.21442E+02 8.33133E+02
4.42884E+02 1.70498E+03
4.64839E+02 1.79178E+03
4.64988E+02 1.79237E+03
1.16247E+03 4.25206E+03
3.43499E+01 8.99667E+02
6.86999E+01 1.65819E+03
8.25106E+01 2.00613E+03
chosen beta = 4.64988E+02
target misfit = 1.79237E+03
achieved misfit = 1.79237E+03
model norm = 1.04147E+01
misfit change = 5.00000E-01
model norm change = 3.41416E-01
chosen beta = 8.02499E+01
target misfit = 1.94900E+03
achieved misfit = 1.94758E+03
model norm = 1.06566E+02
misfit change = 5.00364E-01
model norm change = 1.89686E-01
norm comp Ws = 2.65166E+00
norm comp Wx = 4.83319E+00
norm comp Wz = 2.92980E+00
norm comp Ws = 6.57720E+01
norm comp Wx = 2.69488E+01
norm comp Wz = 1.38454E+01
iter cpu time: 0:00:00.26
iter cpu time: 0:00:00.89
Iteration 8
beta vs. misfit:
beta misfit
1.16247E+02 4.08040E+02
2.32494E+02 7.73743E+02
2.72599E+02 9.16098E+02
2.00625E+01 5.63946E+02
4.01250E+01 9.43655E+02
4.18598E+01 9.78372E+02
chosen beta = 2.67012E+02
target misfit = 8.96186E+02
achieved misfit = 8.95980E+02
model norm = 1.28008E+01
misfit change = 5.00115E-01
model norm change = 2.29117E-01
chosen beta = 4.16303E+01
target misfit = 9.73791E+02
achieved misfit = 9.73755E+02
model norm = 1.22904E+02
misfit change = 5.00019E-01
model norm change = 1.53314E-01
norm comp Ws = 3.12662E+00
norm comp Wx = 5.80666E+00
norm comp Wz = 3.86755E+00
norm comp Ws = 7.39851E+01
norm comp Wx = 3.20341E+01
norm comp Wz = 1.68852E+01
iter cpu time: 0:00:00.30
iter cpu time: 0:00:00.91
Iteration 9
beta vs. misfit:
beta misfit
6.67531E+01 2.47097E+02
1.33506E+02 4.19229E+02
1.45642E+02 4.54732E+02
1.04076E+01 3.54740E+02
2.08152E+01 5.47348E+02
chosen beta = 1.43332E+02
target misfit = 4.47990E+02
achieved misfit = 4.47897E+02
model norm = 1.50386E+01
misfit change = 5.00103E-01
model norm change = 1.74818E-01
chosen beta = 1.72632E+01
target misfit = 4.86877E+02
achieved misfit = 4.81217E+02
model norm = 1.41185E+02
misfit change = 5.05813E-01
model norm change = 1.48742E-01
norm comp Ws = 3.56241E+00
norm comp Wx = 6.64092E+00
norm comp Wz = 4.83532E+00
norm comp Ws = 8.27069E+01
norm comp Wx = 3.78831E+01
norm comp Wz = 2.05954E+01
iter cpu time: 0:00:00.21
iter cpu time: 0:00:00.78
Iteration 10
beta vs. misfit:
beta misfit
3.58330E+01 1.94330E+02
7.16660E+01 2.56470E+02
4.31579E+00 2.09965E+02
8.63158E+00 2.93092E+02
chosen beta = 5.10735E+01
target misfit = 2.23949E+02
achieved misfit = 2.17974E+02
model norm = 1.75231E+01
misfit change = 5.13339E-01
model norm change = 1.65205E-01
chosen beta = 5.72809E+00
target misfit = 2.40608E+02
achieved misfit = 2.34998E+02
model norm = 1.66836E+02
misfit change = 5.11659E-01
model norm change = 1.81682E-01
norm comp Ws = 4.09954E+00
norm comp Wx = 7.65264E+00
norm comp Wz = 5.77092E+00
norm comp Ws = 9.43748E+01
norm comp Wx = 4.67077E+01
norm comp Wz = 2.57538E+01
iter cpu time: 0:00:00.26
iter cpu time: 0:00:00.66
Iteration 11
beta vs. misfit:
beta misfit
2.63809E+00 1.40839E+02
5.80380E+00 1.40006E+02
1.27684E+01 1.44718E+02
2.55367E+01 1.58122E+02
1.43202E+00 1.08765E+02
2.86405E+00 1.48776E+02
chosen beta = 5.80380E+00
target misfit = 1.08987E+02
achieved misfit = 1.40006E+02
model norm = 2.01006E+01
misfit change = 3.57693E-01
model norm change = 1.47090E-01
chosen beta = 1.69894E+00
target misfit = 1.17499E+02
achieved misfit = 1.13855E+02
model norm = 2.07214E+02
misfit change = 5.15505E-01
model norm change = 2.42021E-01
norm comp Ws = 5.01489E+00
norm comp Wx = 8.20406E+00
norm comp Wz = 6.88163E+00
norm comp Ws = 1.10709E+02
norm comp Wx = 6.50107E+01
norm comp Wz = 3.14941E+01
iter cpu time: 0:00:00.22
iter cpu time: 0:00:00.81
Iteration 12
beta vs. misfit:
beta misfit
1.45095E+00 7.61897E+01
2.90190E+00 7.59815E+01
5.80380E+00 7.71909E+01
4.24735E-01 4.69288E+01
8.49470E-01 5.82083E+01
chosen beta = 2.90190E+00
target misfit = 7.00031E+01
achieved misfit = 7.59815E+01
model norm = 2.32752E+01
misfit change = 4.57299E-01
model norm change = 1.57934E-01
chosen beta = 7.90778E-01
target misfit = 5.69276E+01
achieved misfit = 5.61534E+01
model norm = 2.39426E+02
misfit change = 5.06799E-01
model norm change = 1.55454E-01
norm comp Ws = 5.70037E+00
norm comp Wx = 9.53924E+00
norm comp Wz = 8.03554E+00
norm comp Ws = 1.22208E+02
norm comp Wx = 7.90861E+01
norm comp Wz = 3.81319E+01
iter cpu time: 0:00:00.23
iter cpu time: 0:00:00.72
Iteration 13
beta vs. misfit:
beta misfit
9.65320E-01 6.59663E+01
2.12370E+00 6.63626E+01
2.48250E+00 6.65339E+01
5.07840E-01 2.85576E+01
6.33710E-01 3.23824E+01
1.13145E+00 5.17204E+01
chosen beta = 2.12370E+00
target misfit = 6.50000E+01
achieved misfit = 6.63626E+01
model norm = 2.39048E+01
misfit change = 1.26594E-01
model norm change = 2.70511E-02
chosen beta = 9.52357E-01
target misfit = 4.50000E+01
achieved misfit = 4.40177E+01
model norm = 2.37754E+02
misfit change = 2.16117E-01
model norm change = -6.98309E-03
norm comp Ws = 5.80622E+00
norm comp Wx = 9.69831E+00
norm comp Wz = 8.40024E+00
norm comp Ws = 1.26074E+02
norm comp Wx = 7.63269E+01
norm comp Wz = 3.53536E+01
iter cpu time: 0:00:00.26
iter cpu time: 0:00:01.02
Iteration 14
beta vs. misfit:
beta misfit
2.03739E+00 4.61300E+01
2.08010E+00 4.61580E+01
5.20024E+00 5.05536E+01
1.30006E+01 6.80030E+01
9.73609E-01 3.64390E+01
9.95336E-01 3.71506E+01
1.23871E+00 4.57613E+01
chosen beta = 1.13071E+01
target misfit = 6.50000E+01
achieved misfit = 6.38895E+01
model norm = 2.30277E+01
misfit change = 3.72669E-02
model norm change = -3.66901E-02
chosen beta = 1.21709E+00
target misfit = 4.50000E+01
achieved misfit = 4.49504E+01
model norm = 2.30537E+02
misfit change = -2.11895E-02
model norm change = -3.03571E-02
norm comp Ws = 5.35300E+00
norm comp Wx = 9.06543E+00
norm comp Wz = 8.60927E+00
norm comp Ws = 1.26466E+02
norm comp Wx = 7.21243E+01
norm comp Wz = 3.19461E+01
iter cpu time: 0:00:00.30
iter cpu time: 0:00:01.21
Target misfit achieved. Minimizing model norm.
Iteration 15
beta vs. misfit:
beta misfit
1.00060E+01 6.37285E+01
1.15036E+01 6.57179E+01
1.17036E+01 6.60002E+01
1.21843E+00 3.86348E+01
1.21978E+00 3.86736E+01
1.44018E+00 4.53382E+01
chosen beta = 1.09443E+01
target misfit = 6.50000E+01
achieved misfit = 6.49491E+01
model norm = 2.25675E+01
misfit change = -1.65848E-02
model norm change = -1.99856E-02
chosen beta = 1.42896E+00
target misfit = 4.50000E+01
achieved misfit = 4.49850E+01
model norm = 2.28950E+02
misfit change = -7.69307E-04
model norm change = -6.88525E-03
norm comp Ws = 5.25353E+00
norm comp Wx = 9.12332E+00
norm comp Wz = 8.19063E+00
norm comp Ws = 1.25759E+02
norm comp Wx = 6.90749E+01
norm comp Wz = 3.41159E+01
iter cpu time: 0:00:00.45
Target misfit achieved. Minimizing model norm.
Iteration 16
beta vs. misfit:
beta misfit
1.09529E+01 6.22414E+01
1.09615E+01 6.22535E+01
1.30478E+01 6.53929E+01
chosen beta = 1.27723E+01
target misfit = 6.50000E+01
achieved misfit = 6.49555E+01
model norm = 2.25114E+01
misfit change = -9.87955E-05
model norm change = -2.48393E-03
norm comp Ws = 5.19089E+00
norm comp Wx = 9.13767E+00
norm comp Wz = 8.18286E+00
iter cpu time: 0:00:00.28
iter cpu time: 0:00:00.76
Exit at convergence.
Iterations performed: 16
Iterations performed: 15
total cpu time: 0:00:04.60
total cpu time: 0:00:13.54
DCINV2D ended on:12/14/2015 14:39:36
DCINV2D ended on: 1/12/2016 16:42:31
simpegDCIP/Dev/Inv2D/dcinv2d.out
+19 -20
View File
@@ -1,20 +1,19 @@
16 iter data misfit model norm beta
0 2.29963E+05 0.00000E+00 0.00000E+00
1 1.14982E+05 2.23402E-01 2.20496E+05
2 5.74953E+04 8.13417E-01 6.11687E+04
3 2.84776E+04 1.74401E+00 2.00018E+04
4 1.41388E+04 3.13263E+00 6.03799E+03
5 7.12029E+03 5.18734E+00 1.96825E+03
6 3.58474E+03 7.76393E+00 8.85768E+02
7 1.79237E+03 1.04147E+01 4.64988E+02
8 8.95980E+02 1.28008E+01 2.67012E+02
9 4.47897E+02 1.50386E+01 1.43332E+02
10 2.17974E+02 1.75231E+01 5.10735E+01
11 1.40006E+02 2.01006E+01 5.80380E+00
12 7.59815E+01 2.32752E+01 2.90190E+00
13 6.63626E+01 2.39048E+01 2.12370E+00
14 6.38895E+01 2.30277E+01 1.13071E+01
15 6.49491E+01 2.25675E+01 1.09443E+01
16 6.49555E+01 2.25114E+01 1.27723E+01
6.50000E+01 target misfit
65 number of data
15 iter data misfit model norm beta
0 2.57080E+05 0.00000E+00 0.00000E+00
1 1.28540E+05 3.63297E+00 1.58499E+04
2 6.42774E+04 1.37169E+01 4.25263E+03
3 3.17674E+04 2.91306E+01 1.51222E+03
4 1.56569E+04 4.84312E+01 6.00000E+02
5 7.79602E+03 6.97577E+01 2.60199E+02
6 3.89800E+03 8.95751E+01 1.37400E+02
7 1.94758E+03 1.06566E+02 8.02499E+01
8 9.73755E+02 1.22904E+02 4.16303E+01
9 4.81217E+02 1.41185E+02 1.72632E+01
10 2.34998E+02 1.66836E+02 5.72809E+00
11 1.13855E+02 2.07214E+02 1.69894E+00
12 5.61534E+01 2.39426E+02 7.90778E-01
13 4.40177E+01 2.37754E+02 9.52357E-01
14 4.49504E+01 2.30537E+02 1.21709E+00
15 4.49850E+01 2.28950E+02 1.42896E+00
4.50000E+01 target misfit
45 number of data
simpegDCIP/Dev/Inv2D/dcinv2d.pre
+45 -65
View File
@@ -1,66 +1,46 @@
! Predicted data ! GENERAL FORMAT
0.0000000E+00 3.9965610E+01 8.0003810E+01 1.1996940E+02 -1.30700E+00
0.0000000E+00 3.9965610E+01 1.1996940E+02 1.6000760E+02 -3.42456E-01
0.0000000E+00 3.9965610E+01 1.6000760E+02 1.9997320E+02 -1.39215E-01
0.0000000E+00 3.9965610E+01 1.9997320E+02 2.4000760E+02 -7.97272E-02
0.0000000E+00 3.9965610E+01 2.4000760E+02 2.8004580E+02 -5.32947E-02
0.0000000E+00 3.9965610E+01 2.8004580E+02 3.2001140E+02 -2.06798E-02
0.0000000E+00 3.9965610E+01 3.2001140E+02 3.5997700E+02 -8.53607E-03
0.0000000E+00 3.9965610E+01 3.5997700E+02 4.0001520E+02 -7.69726E-03
0.0000000E+00 3.9965610E+01 4.0001520E+02 4.3998080E+02 -4.27307E-03
0.0000000E+00 3.9965610E+01 4.3998080E+02 4.8001900E+02 -2.69281E-03
3.9965610E+01 8.0003810E+01 1.1996940E+02 1.6000760E+02 -1.25844E+00
3.9965610E+01 8.0003810E+01 1.6000760E+02 1.9997320E+02 -3.39589E-01
3.9965610E+01 8.0003810E+01 1.9997320E+02 2.4000760E+02 -1.54405E-01
3.9965610E+01 8.0003810E+01 2.4000760E+02 2.8004580E+02 -9.21686E-02
3.9965610E+01 8.0003810E+01 2.8004580E+02 3.2001140E+02 -3.38887E-02
3.9965610E+01 8.0003810E+01 3.2001140E+02 3.5997700E+02 -1.30227E-02
3.9965610E+01 8.0003810E+01 3.5997700E+02 4.0001520E+02 -1.12601E-02
3.9965610E+01 8.0003810E+01 4.0001520E+02 4.3998080E+02 -5.99453E-03
3.9965610E+01 8.0003810E+01 4.3998080E+02 4.8001900E+02 -3.57583E-03
3.9965610E+01 8.0003810E+01 4.8001900E+02 5.1998460E+02 -3.55986E-03
8.0003810E+01 1.1996940E+02 1.6000760E+02 1.9997320E+02 -1.29660E+00
8.0003810E+01 1.1996940E+02 1.9997320E+02 2.4000760E+02 -3.72299E-01
8.0003810E+01 1.1996940E+02 2.4000760E+02 2.8004580E+02 -1.83367E-01
8.0003810E+01 1.1996940E+02 2.8004580E+02 3.2001140E+02 -6.20943E-02
8.0003810E+01 1.1996940E+02 3.2001140E+02 3.5997700E+02 -2.16124E-02
8.0003810E+01 1.1996940E+02 3.5997700E+02 4.0001520E+02 -1.76751E-02
8.0003810E+01 1.1996940E+02 4.0001520E+02 4.3998080E+02 -8.93272E-03
8.0003810E+01 1.1996940E+02 4.3998080E+02 4.8001900E+02 -4.98346E-03
8.0003810E+01 1.1996940E+02 4.8001900E+02 5.1998460E+02 -4.69244E-03
1.1996940E+02 1.6000760E+02 1.9997320E+02 2.4000760E+02 -1.32070E+00
1.1996940E+02 1.6000760E+02 2.4000760E+02 2.8004580E+02 -4.42511E-01
1.1996940E+02 1.6000760E+02 2.8004580E+02 3.2001140E+02 -1.31098E-01
1.1996940E+02 1.6000760E+02 3.2001140E+02 3.5997700E+02 -3.99969E-02
1.1996940E+02 1.6000760E+02 3.5997700E+02 4.0001520E+02 -3.04870E-02
1.1996940E+02 1.6000760E+02 4.0001520E+02 4.3998080E+02 -1.44934E-02
1.1996940E+02 1.6000760E+02 4.3998080E+02 4.8001900E+02 -7.49208E-03
1.1996940E+02 1.6000760E+02 4.8001900E+02 5.1998460E+02 -6.61132E-03
1.6000760E+02 1.9997320E+02 2.4000760E+02 2.8004580E+02 -1.46472E+00
1.6000760E+02 1.9997320E+02 2.8004580E+02 3.2001140E+02 -3.31306E-01
1.6000760E+02 1.9997320E+02 3.2001140E+02 3.5997700E+02 -8.37006E-02
1.6000760E+02 1.9997320E+02 3.5997700E+02 4.0001520E+02 -5.82908E-02
1.6000760E+02 1.9997320E+02 4.0001520E+02 4.3998080E+02 -2.57862E-02
1.6000760E+02 1.9997320E+02 4.3998080E+02 4.8001900E+02 -1.22347E-02
1.6000760E+02 1.9997320E+02 4.8001900E+02 5.1998460E+02 -1.00199E-02
1.9997320E+02 2.4000760E+02 2.8004580E+02 3.2001140E+02 -1.23923E+00
1.9997320E+02 2.4000760E+02 3.2001140E+02 3.5997700E+02 -2.12909E-01
1.9997320E+02 2.4000760E+02 3.5997700E+02 4.0001520E+02 -1.28560E-01
1.9997320E+02 2.4000760E+02 4.0001520E+02 4.3998080E+02 -5.16188E-02
1.9997320E+02 2.4000760E+02 4.3998080E+02 4.8001900E+02 -2.20377E-02
1.9997320E+02 2.4000760E+02 4.8001900E+02 5.1998460E+02 -1.64456E-02
2.4000760E+02 2.8004580E+02 3.2001140E+02 3.5997700E+02 -6.67006E-01
2.4000760E+02 2.8004580E+02 3.5997700E+02 4.0001520E+02 -2.92792E-01
2.4000760E+02 2.8004580E+02 4.0001520E+02 4.3998080E+02 -9.95755E-02
2.4000760E+02 2.8004580E+02 4.3998080E+02 4.8001900E+02 -3.64738E-02
2.4000760E+02 2.8004580E+02 4.8001900E+02 5.1998460E+02 -2.37845E-02
2.8004580E+02 3.2001140E+02 3.5997700E+02 4.0001520E+02 -5.39917E-01
2.8004580E+02 3.2001140E+02 4.0001520E+02 4.3998080E+02 -1.28793E-01
2.8004580E+02 3.2001140E+02 4.3998080E+02 4.8001900E+02 -3.68611E-02
2.8004580E+02 3.2001140E+02 4.8001900E+02 5.1998460E+02 -1.93746E-02
3.2001140E+02 3.5997700E+02 4.0001520E+02 4.3998080E+02 -1.08413E+00
3.2001140E+02 3.5997700E+02 4.3998080E+02 4.8001900E+02 -1.79806E-01
3.2001140E+02 3.5997700E+02 4.8001900E+02 5.1998460E+02 -5.69603E-02
3.5997700E+02 4.0001520E+02 4.3998080E+02 4.8001900E+02 -1.12975E+00
3.5997700E+02 4.0001520E+02 4.8001900E+02 5.1998460E+02 -2.27185E-01
4.0001520E+02 4.3998080E+02 4.8001900E+02 5.1998460E+02 -1.08910E+00
0.0000000E+00 0.0000000E+00 2.0000000E+02 3.0000000E+02 8.31819E-02
0.0000000E+00 0.0000000E+00 3.0000000E+02 4.0000000E+02 4.41288E-03
0.0000000E+00 0.0000000E+00 4.0000000E+02 5.0000000E+02 1.23515E-02
0.0000000E+00 0.0000000E+00 5.0000000E+02 6.0000000E+02 2.09881E-03
0.0000000E+00 0.0000000E+00 6.0000000E+02 7.0000000E+02 7.28316E-04
0.0000000E+00 0.0000000E+00 7.0000000E+02 8.0000000E+02 5.24885E-04
0.0000000E+00 0.0000000E+00 8.0000000E+02 9.0000000E+02 3.56979E-04
0.0000000E+00 0.0000000E+00 9.0000000E+02 1.0000000E+03 2.71240E-04
0.0000000E+00 0.0000000E+00 1.0000000E+03 1.1000000E+03 1.71849E-04
1.0000000E+02 1.0000000E+02 3.0000000E+02 4.0000000E+02 8.00012E-03
1.0000000E+02 1.0000000E+02 4.0000000E+02 5.0000000E+02 1.75080E-02
1.0000000E+02 1.0000000E+02 5.0000000E+02 6.0000000E+02 2.81046E-03
1.0000000E+02 1.0000000E+02 6.0000000E+02 7.0000000E+02 9.20650E-04
1.0000000E+02 1.0000000E+02 7.0000000E+02 8.0000000E+02 6.40887E-04
1.0000000E+02 1.0000000E+02 8.0000000E+02 9.0000000E+02 4.28101E-04
1.0000000E+02 1.0000000E+02 9.0000000E+02 1.0000000E+03 3.19325E-04
1.0000000E+02 1.0000000E+02 1.0000000E+03 1.1000000E+03 1.97133E-04
2.0000000E+02 2.0000000E+02 4.0000000E+02 5.0000000E+02 3.00094E-02
2.0000000E+02 2.0000000E+02 5.0000000E+02 6.0000000E+02 4.17179E-03
2.0000000E+02 2.0000000E+02 6.0000000E+02 7.0000000E+02 1.23020E-03
2.0000000E+02 2.0000000E+02 7.0000000E+02 8.0000000E+02 8.09705E-04
2.0000000E+02 2.0000000E+02 8.0000000E+02 9.0000000E+02 5.26566E-04
2.0000000E+02 2.0000000E+02 9.0000000E+02 1.0000000E+03 3.82914E-04
2.0000000E+02 2.0000000E+02 1.0000000E+03 1.1000000E+03 2.28587E-04
3.0000000E+02 3.0000000E+02 5.0000000E+02 6.0000000E+02 7.27481E-03
3.0000000E+02 3.0000000E+02 6.0000000E+02 7.0000000E+02 1.78177E-03
3.0000000E+02 3.0000000E+02 7.0000000E+02 8.0000000E+02 1.07707E-03
3.0000000E+02 3.0000000E+02 8.0000000E+02 9.0000000E+02 6.74696E-04
3.0000000E+02 3.0000000E+02 9.0000000E+02 1.0000000E+03 4.74671E-04
3.0000000E+02 3.0000000E+02 1.0000000E+03 1.1000000E+03 2.71808E-04
4.0000000E+02 4.0000000E+02 6.0000000E+02 7.0000000E+02 3.37547E-03
4.0000000E+02 4.0000000E+02 7.0000000E+02 8.0000000E+02 1.52487E-03
4.0000000E+02 4.0000000E+02 8.0000000E+02 9.0000000E+02 8.67080E-04
4.0000000E+02 4.0000000E+02 9.0000000E+02 1.0000000E+03 5.71693E-04
4.0000000E+02 4.0000000E+02 1.0000000E+03 1.1000000E+03 3.07672E-04
5.0000000E+02 5.0000000E+02 7.0000000E+02 8.0000000E+02 6.64946E-03
5.0000000E+02 5.0000000E+02 8.0000000E+02 9.0000000E+02 2.83925E-03
5.0000000E+02 5.0000000E+02 9.0000000E+02 1.0000000E+03 1.50341E-03
5.0000000E+02 5.0000000E+02 1.0000000E+03 1.1000000E+03 6.31242E-04
6.0000000E+02 6.0000000E+02 8.0000000E+02 9.0000000E+02 4.28770E-03
6.0000000E+02 6.0000000E+02 9.0000000E+02 1.0000000E+03 2.02291E-03
6.0000000E+02 6.0000000E+02 1.0000000E+03 1.1000000E+03 7.75855E-04
7.0000000E+02 7.0000000E+02 9.0000000E+02 1.0000000E+03 3.85090E-03
7.0000000E+02 7.0000000E+02 1.0000000E+03 1.1000000E+03 1.15357E-03
8.0000000E+02 8.0000000E+02 1.0000000E+03 1.1000000E+03 2.22667E-03
simpegDCIP/Dev/Inv2D/dcinv2d_0.pre
+45 -65
View File
@@ -1,66 +1,46 @@
! Predicted data ! GENERAL FORMAT Last column are apparent conductivities.
0.0000000E+00 3.9965610E+01 8.0003810E+01 1.1996940E+02 -1.41981E+00 9.31829E-04
0.0000000E+00 3.9965610E+01 1.1996940E+02 1.6000760E+02 -3.67215E-01 9.02896E-04
0.0000000E+00 3.9965610E+01 1.6000760E+02 1.9997320E+02 -1.42202E-01 9.30832E-04
0.0000000E+00 3.9965610E+01 1.9997320E+02 2.4000760E+02 -6.89318E-02 9.61918E-04
0.0000000E+00 3.9965610E+01 2.4000760E+02 2.8004580E+02 -3.84797E-02 9.84325E-04
0.0000000E+00 3.9965610E+01 2.8004580E+02 3.2001140E+02 -2.37196E-02 9.96243E-04
0.0000000E+00 3.9965610E+01 3.2001140E+02 3.5997700E+02 -1.57502E-02 1.00062E-03
0.0000000E+00 3.9965610E+01 3.5997700E+02 4.0001520E+02 -1.10427E-02 1.00086E-03
0.0000000E+00 3.9965610E+01 4.0001520E+02 4.3998080E+02 -8.02898E-03 9.99286E-04
0.0000000E+00 3.9965610E+01 4.3998080E+02 4.8001900E+02 -6.04587E-03 9.97122E-04
3.9965610E+01 8.0003810E+01 1.1996940E+02 1.6000760E+02 -1.42170E+00 9.35109E-04
3.9965610E+01 8.0003810E+01 1.6000760E+02 1.9997320E+02 -3.65942E-01 9.06037E-04
3.9965610E+01 8.0003810E+01 1.9997320E+02 2.4000760E+02 -1.42245E-01 9.34103E-04
3.9965610E+01 8.0003810E+01 2.4000760E+02 2.8004580E+02 -6.88085E-02 9.65049E-04
3.9965610E+01 8.0003810E+01 2.8004580E+02 3.2001140E+02 -3.83673E-02 9.87209E-04
3.9965610E+01 8.0003810E+01 3.2001140E+02 3.5997700E+02 -2.37105E-02 9.98865E-04
3.9965610E+01 8.0003810E+01 3.5997700E+02 4.0001520E+02 -1.57715E-02 1.00296E-03
3.9965610E+01 8.0003810E+01 4.0001520E+02 4.3998080E+02 -1.10206E-02 1.00287E-03
3.9965610E+01 8.0003810E+01 4.3998080E+02 4.8001900E+02 -8.04519E-03 1.00094E-03
3.9965610E+01 8.0003810E+01 4.8001900E+02 5.1998460E+02 -6.03884E-03 9.98281E-04
8.0003810E+01 1.1996940E+02 1.6000760E+02 1.9997320E+02 -1.41516E+00 9.34893E-04
8.0003810E+01 1.1996940E+02 1.9997320E+02 2.4000760E+02 -3.65700E-01 9.06585E-04
8.0003810E+01 1.1996940E+02 2.4000760E+02 2.8004580E+02 -1.41778E-01 9.34827E-04
8.0003810E+01 1.1996940E+02 2.8004580E+02 3.2001140E+02 -6.84887E-02 9.65895E-04
8.0003810E+01 1.1996940E+02 3.2001140E+02 3.5997700E+02 -3.82776E-02 9.88132E-04
8.0003810E+01 1.1996940E+02 3.5997700E+02 4.0001520E+02 -2.36884E-02 9.99799E-04
8.0003810E+01 1.1996940E+02 4.0001520E+02 4.3998080E+02 -1.57005E-02 1.00378E-03
8.0003810E+01 1.1996940E+02 4.3998080E+02 4.8001900E+02 -1.10136E-02 1.00351E-03
8.0003810E+01 1.1996940E+02 4.8001900E+02 5.1998460E+02 -8.01362E-03 1.00120E-03
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simpegDCIP/Dev/Inv2D/sensitivity.txt
+46 -31
View File
@@ -1,31 +1,46 @@
58 30
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2.06568E-14 7.34961E-14 2.65358E-13 9.74990E-13 3.51377E-12 1.12269E-11 2.85943E-11 5.55618E-11 8.49896E-11 1.09224E-10 1.25023E-10 1.33544E-10 1.37570E-10 1.39308E-10 1.39997E-10 1.40266E-10 1.40312E-10 1.40160E-10 1.39838E-10 1.39376E-10 1.38804E-10 1.38149E-10 1.37446E-10 1.36714E-10 1.35985E-10 1.35271E-10 1.34594E-10 1.33958E-10 1.33375E-10 1.32839E-10 1.32349E-10 1.31891E-10 1.31454E-10 1.31015E-10 1.30556E-10 1.30050E-10 1.29476E-10 1.28805E-10 1.28015E-10 1.27083E-10 1.25991E-10 1.24718E-10 1.23257E-10 1.21593E-10 1.19218E-10 1.15327E-10 1.08894E-10 9.84883E-11 8.27981E-11 6.20634E-11 3.93792E-11 2.00930E-11 8.08282E-12 2.66429E-12 7.85255E-13 2.24333E-13 6.41492E-14 1.83950E-14
2.05275E-14 7.24878E-14 2.57139E-13 9.10235E-13 3.07998E-12 9.01321E-12 2.07324E-11 3.67252E-11 5.25387E-11 6.46043E-11 7.20063E-11 7.58774E-11 7.77264E-11 7.85753E-11 7.89618E-11 7.91737E-11 7.92954E-11 7.93357E-11 7.93040E-11 7.92098E-11 7.90622E-11 7.88717E-11 7.86464E-11 7.83956E-11 7.81258E-11 7.78442E-11 7.75551E-11 7.72632E-11 7.69695E-11 7.66758E-11 7.63802E-11 7.60811E-11 7.57739E-11 7.54544E-11 7.51157E-11 7.47516E-11 7.43539E-11 7.39154E-11 7.34274E-11 7.28829E-11 7.22743E-11 7.15957E-11 7.08413E-11 7.00070E-11 6.88523E-11 6.69923E-11 6.39325E-11 5.89168E-11 5.10823E-11 4.01235E-11 2.72142E-11 1.51209E-11 6.65194E-12 2.36762E-12 7.37723E-13 2.17855E-13 6.33040E-14 1.82822E-14
2.02980E-14 7.07435E-14 2.43491E-13 8.09517E-13 2.47303E-12 6.34823E-12 1.27372E-11 2.01320E-11 2.66421E-11 3.13487E-11 3.43255E-11 3.60940E-11 3.71384E-11 3.77696E-11 3.81637E-11 3.84665E-11 3.87388E-11 3.89795E-11 3.91872E-11 3.93607E-11 3.94990E-11 3.96009E-11 3.96657E-11 3.96929E-11 3.96822E-11 3.96337E-11 3.95478E-11 3.94254E-11 3.92675E-11 3.90756E-11 3.88512E-11 3.85963E-11 3.83129E-11 3.80030E-11 3.76686E-11 3.73119E-11 3.69346E-11 3.65385E-11 3.61251E-11 3.56956E-11 3.52511E-11 3.47922E-11 3.43197E-11 3.38339E-11 3.32243E-11 3.23253E-11 3.09764E-11 2.89266E-11 2.58356E-11 2.14088E-11 1.57582E-11 9.76260E-12 4.85911E-12 1.94336E-12 6.63037E-13 2.07046E-13 6.18383E-14 1.80817E-14
1.99103E-14 6.78839E-14 2.22207E-13 6.65021E-13 1.71512E-12 3.62265E-12 6.20152E-12 8.94990E-12 1.13913E-11 1.32488E-11 1.45223E-11 1.53540E-11 1.58905E-11 1.62387E-11 1.64675E-11 1.66486E-11 1.68160E-11 1.69686E-11 1.71052E-11 1.72249E-11 1.73265E-11 1.74093E-11 1.74725E-11 1.75154E-11 1.75378E-11 1.75392E-11 1.75196E-11 1.74793E-11 1.74183E-11 1.73373E-11 1.72369E-11 1.71178E-11 1.69811E-11 1.68277E-11 1.66588E-11 1.64757E-11 1.62796E-11 1.60718E-11 1.58536E-11 1.56261E-11 1.53908E-11 1.51487E-11 1.49009E-11 1.46484E-11 1.43407E-11 1.39006E-11 1.32701E-11 1.23699E-11 1.11053E-11 9.40235E-12 7.29585E-12 5.00877E-12 2.91739E-12 1.39686E-12 5.54377E-13 1.90080E-13 5.94292E-14 1.77427E-14
1.93402E-14 6.38984E-14 1.95192E-13 5.06804E-13 1.03897E-12 1.71876E-12 2.57650E-12 3.69244E-12 4.88705E-12 5.91106E-12 6.67074E-12 7.19348E-12 7.54194E-12 7.77248E-12 7.92575E-12 8.04669E-12 8.15908E-12 8.26217E-12 8.35528E-12 8.43776E-12 8.50902E-12 8.56856E-12 8.61595E-12 8.65081E-12 8.67290E-12 8.68202E-12 8.67811E-12 8.66118E-12 8.63134E-12 8.58878E-12 8.53382E-12 8.46682E-12 8.38827E-12 8.29869E-12 8.19868E-12 8.08892E-12 7.97012E-12 7.84304E-12 7.70845E-12 7.56717E-12 7.42002E-12 7.26781E-12 7.11136E-12 6.95148E-12 6.75715E-12 6.48086E-12 6.09065E-12 5.54987E-12 4.83066E-12 3.94727E-12 2.99922E-12 2.14915E-12 1.46607E-12 8.86932E-13 4.32322E-13 1.68267E-13 5.60545E-14 1.72435E-14
81 45
1.00411E-13 3.10408E-13 9.74717E-13 3.09564E-12 9.68504E-12 2.91605E-11 8.35615E-11 2.28263E-10 6.04123E-10 1.61401E-09 4.67374E-09 1.58647E-08 6.23317E-08 2.35358E-07 1.71041E-08 2.08590E-07 1.56029E-07 1.46650E-07 1.72752E-07 1.54926E-07 1.77390E-07 2.03028E-07 2.69140E-07 4.10395E-07 1.29687E-07 7.93598E-07 2.97657E-07 1.28094E-07 7.80834E-08 7.94358E-08 1.06544E-07 6.15537E-08 7.23303E-08 1.38411E-07 8.59111E-08 1.68916E-07 1.64240E-07 1.58717E-07 1.44871E-07 1.00497E-07 1.53571E-07 1.14875E-07 1.03632E-07 9.31407E-08 4.54508E-08 5.91715E-08 3.66679E-08 6.10459E-08 1.23422E-07 6.11907E-08 8.25315E-08 6.19628E-08 8.25261E-08 1.49285E-07 7.23998E-08 1.14387E-07 8.11571E-08 6.93066E-08 7.27982E-08 5.00166E-08 1.01158E-07 5.96975E-08 4.37654E-08 4.14493E-08 3.33156E-08 6.30583E-08 3.86673E-08 2.63025E-08 1.28371E-08 7.03143E-09 2.55569E-09 6.86395E-10 2.47006E-10 1.07284E-10 4.64772E-11 1.87670E-11 6.99064E-12 2.43083E-12 8.12035E-13 2.70167E-13 9.04729E-14
1.00408E-13 3.10389E-13 9.74598E-13 3.09493E-12 9.68107E-12 2.91394E-11 8.34535E-11 2.27725E-10 6.01411E-10 1.59939E-09 4.58076E-09 1.50821E-08 5.37357E-08 1.45015E-07 9.49187E-08 3.64320E-08 2.63448E-08 4.94770E-08 6.85655E-08 7.00921E-08 5.92742E-08 6.09382E-08 4.59596E-08 9.87180E-08 2.28836E-07 4.66832E-07 1.96472E-07 6.17388E-08 3.68928E-08 7.81857E-08 6.83590E-08 3.70845E-08 2.61880E-08 3.78832E-08 3.43796E-08 8.14050E-08 1.25510E-07 1.28215E-07 8.53512E-08 2.97777E-08 6.74204E-08 7.99932E-08 7.90362E-08 5.79363E-08 2.71734E-08 3.44196E-08 2.90110E-08 4.29354E-08 7.81656E-08 3.22896E-08 2.56389E-08 2.96368E-08 4.22860E-08 6.50714E-08 3.97379E-08 3.15747E-08 3.02920E-08 2.68593E-08 2.63743E-08 3.31479E-08 4.64930E-08 3.35157E-08 2.40778E-08 1.98269E-08 2.25180E-08 2.99855E-08 2.16250E-08 1.19611E-08 3.79368E-09 5.30861E-09 2.42064E-09 6.74309E-10 2.45568E-10 1.07010E-10 4.64139E-11 1.87527E-11 6.98765E-12 2.43026E-12 8.11935E-13 2.70151E-13 9.04706E-14
1.00403E-13 3.10352E-13 9.74361E-13 3.09352E-12 9.67313E-12 2.90971E-11 8.32379E-11 2.26652E-10 5.96028E-10 1.57061E-09 4.40228E-09 1.37248E-08 4.29042E-08 9.30852E-08 9.29476E-08 7.55456E-08 5.93963E-08 4.76051E-08 4.12935E-08 3.65352E-08 3.71970E-08 5.21912E-08 8.02057E-08 1.21892E-07 1.86496E-07 2.35135E-07 1.04732E-07 4.22783E-08 4.65762E-08 7.58073E-08 7.79331E-08 5.75407E-08 3.61509E-08 2.56255E-08 3.04573E-08 5.36578E-08 9.24811E-08 9.80266E-08 6.17923E-08 2.36365E-08 3.82806E-08 5.03284E-08 5.12491E-08 3.73919E-08 2.34194E-08 2.60215E-08 2.90466E-08 2.45768E-08 3.72154E-08 2.46155E-08 1.95316E-08 1.83760E-08 1.99613E-08 2.36885E-08 2.06672E-08 1.50154E-08 1.45427E-08 1.58183E-08 1.70564E-08 1.86277E-08 2.16690E-08 1.97339E-08 1.67292E-08 1.43288E-08 1.38128E-08 1.49270E-08 1.10184E-08 5.71202E-09 3.08046E-09 4.21637E-09 2.22842E-09 6.53757E-10 2.42850E-10 1.06473E-10 4.62878E-11 1.87241E-11 6.98166E-12 2.42911E-12 8.11735E-13 2.70119E-13 9.04659E-14
1.00395E-13 3.10297E-13 9.74007E-13 3.09140E-12 9.66126E-12 2.90340E-11 8.29162E-11 2.25056E-10 5.88072E-10 1.52870E-09 4.15246E-09 1.20281E-08 3.25763E-08 6.12733E-08 7.24227E-08 7.24351E-08 6.89739E-08 6.44213E-08 6.06043E-08 5.88049E-08 6.12468E-08 7.02526E-08 8.55137E-08 1.01379E-07 1.17435E-07 1.10049E-07 5.93222E-08 3.57569E-08 4.43427E-08 6.79194E-08 7.87981E-08 6.57704E-08 4.31389E-08 2.76129E-08 2.75646E-08 4.19311E-08 6.75913E-08 7.21313E-08 4.69404E-08 2.17775E-08 2.38212E-08 3.41662E-08 3.39607E-08 2.71160E-08 2.20754E-08 2.36058E-08 2.62335E-08 2.39947E-08 2.08291E-08 1.86009E-08 1.69637E-08 1.56636E-08 1.45648E-08 1.45218E-08 1.31646E-08 1.11608E-08 1.04547E-08 1.10253E-08 1.18592E-08 1.22029E-08 1.25574E-08 1.22845E-08 1.09972E-08 9.27076E-09 8.52098E-09 8.26660E-09 6.25092E-09 3.63958E-09 2.74981E-09 3.36581E-09 2.00653E-09 6.28282E-10 2.39092E-10 1.05685E-10 4.60999E-11 1.86812E-11 6.97267E-12 2.42739E-12 8.11435E-13 2.70071E-13 9.04589E-14
1.00384E-13 3.10223E-13 9.73535E-13 3.08858E-12 9.64549E-12 2.89501E-11 8.24901E-11 2.22953E-10 5.77686E-10 1.47517E-09 3.85028E-09 1.02301E-08 2.40819E-08 4.09946E-08 5.15113E-08 5.66371E-08 5.89699E-08 5.94047E-08 5.89331E-08 5.84650E-08 5.93855E-08 6.22206E-08 6.78377E-08 7.13474E-08 6.93776E-08 5.69048E-08 3.70412E-08 2.95279E-08 3.94982E-08 5.92464E-08 7.22061E-08 6.40885E-08 4.40738E-08 2.78255E-08 2.51527E-08 3.40307E-08 4.93936E-08 5.18882E-08 3.56867E-08 2.04985E-08 1.83801E-08 2.24585E-08 2.41321E-08 2.17185E-08 1.94928E-08 2.06288E-08 2.24118E-08 2.28724E-08 2.02549E-08 1.76004E-08 1.56231E-08 1.40811E-08 1.27921E-08 1.17500E-08 1.06340E-08 9.42140E-09 8.66731E-09 8.58213E-09 8.57312E-09 8.46942E-09 8.19420E-09 7.86826E-09 7.17032E-09 5.95046E-09 5.55749E-09 5.19157E-09 4.13317E-09 2.91236E-09 2.33485E-09 2.69307E-09 1.77544E-09 6.00266E-10 2.34548E-10 1.04670E-10 4.58521E-11 1.86242E-11 6.96069E-12 2.42509E-12 8.11034E-13 2.70007E-13 9.04495E-14
1.00371E-13 3.10131E-13 9.72945E-13 3.08506E-12 9.62578E-12 2.88456E-11 8.19609E-11 2.20358E-10 5.65059E-10 1.41203E-09 3.51764E-09 8.52308E-09 1.76652E-08 2.78171E-08 3.52435E-08 4.02390E-08 4.36436E-08 4.55593E-08 4.62885E-08 4.61935E-08 4.58570E-08 4.59609E-08 4.64599E-08 4.56144E-08 4.14404E-08 3.34164E-08 2.49965E-08 2.53810E-08 3.47950E-08 5.06486E-08 6.17827E-08 5.69160E-08 4.16721E-08 2.78329E-08 2.33998E-08 2.81598E-08 3.63162E-08 3.71243E-08 2.70011E-08 1.91329E-08 1.63397E-08 1.47793E-08 1.66401E-08 1.65319E-08 1.60060E-08 1.71740E-08 1.88635E-08 1.96484E-08 1.89244E-08 1.69428E-08 1.49295E-08 1.32093E-08 1.17339E-08 1.04676E-08 9.36066E-09 8.39593E-09 7.61931E-09 7.12654E-09 6.73969E-09 6.24094E-09 5.73430E-09 5.35322E-09 4.79423E-09 4.16788E-09 3.88162E-09 3.63303E-09 3.07894E-09 2.47285E-09 2.04204E-09 2.17776E-09 1.55221E-09 5.71028E-10 2.29430E-10 1.03448E-10 4.55470E-11 1.85534E-11 6.94577E-12 2.42222E-12 8.10534E-13 2.69927E-13 9.04378E-14
1.00355E-13 3.10020E-13 9.72237E-13 3.08084E-12 9.60217E-12 2.87206E-11 8.13304E-11 2.17294E-10 5.50419E-10 1.34158E-09 3.17567E-09 7.02033E-09 1.30320E-08 1.92181E-08 2.39653E-08 2.75259E-08 3.02282E-08 3.19461E-08 3.27113E-08 3.26200E-08 3.18885E-08 3.12732E-08 3.00593E-08 2.86405E-08 2.56339E-08 2.14299E-08 1.89580E-08 2.23895E-08 3.04660E-08 4.24723E-08 5.07654E-08 4.80954E-08 3.77129E-08 2.73072E-08 2.24444E-08 2.34653E-08 2.72261E-08 2.68281E-08 2.14528E-08 1.75685E-08 1.46768E-08 1.25037E-08 1.15821E-08 1.20603E-08 1.24725E-08 1.36978E-08 1.52171E-08 1.60684E-08 1.62655E-08 1.53328E-08 1.38668E-08 1.23503E-08 1.09593E-08 9.72435E-09 8.63431E-09 7.70079E-09 6.89380E-09 6.18921E-09 5.61042E-09 4.95624E-09 4.26757E-09 3.87139E-09 3.35373E-09 3.04604E-09 2.88718E-09 2.74890E-09 2.46405E-09 2.14088E-09 1.79987E-09 1.78719E-09 1.34762E-09 5.41150E-10 2.23890E-10 1.02046E-10 4.51874E-11 1.84692E-11 6.92794E-12 2.41879E-12 8.09934E-13 2.69832E-13 9.04238E-14
1.00336E-13 3.09891E-13 9.71411E-13 3.07592E-12 9.57467E-12 2.85754E-11 8.06014E-11 2.13788E-10 5.34038E-10 1.26628E-09 2.84198E-09 5.76246E-09 9.75193E-09 1.35929E-08 1.65370E-08 1.88158E-08 2.06103E-08 2.17898E-08 2.23262E-08 2.22280E-08 2.16421E-08 2.09737E-08 1.99993E-08 1.85755E-08 1.69017E-08 1.55254E-08 1.60218E-08 1.99564E-08 2.63817E-08 3.50897E-08 4.09329E-08 3.97264E-08 3.32442E-08 2.60457E-08 2.20879E-08 2.11235E-08 2.17143E-08 2.10861E-08 1.89413E-08 1.58676E-08 1.32122E-08 1.10786E-08 9.92740E-09 9.16885E-09 9.39976E-09 1.05910E-08 1.19192E-08 1.28577E-08 1.32372E-08 1.30813E-08 1.22734E-08 1.11880E-08 1.00543E-08 8.97649E-09 7.98457E-09 7.07801E-09 6.25073E-09 5.47228E-09 4.73144E-09 4.05594E-09 3.31529E-09 2.91552E-09 2.49766E-09 2.34118E-09 2.25926E-09 2.19007E-09 2.04976E-09 1.86300E-09 1.58773E-09 1.48800E-09 1.16650E-09 5.10906E-10 2.18020E-10 1.00482E-10 4.47762E-11 1.83718E-11 6.90724E-12 2.41479E-12 8.09235E-13 2.69720E-13 9.04074E-14
1.00315E-13 3.09744E-13 9.70468E-13 3.07030E-12 9.54335E-12 2.84104E-11 7.97777E-11 2.09874E-10 5.16211E-10 1.18845E-09 2.52886E-09 4.74199E-09 7.43801E-09 9.88357E-09 1.17194E-08 1.31381E-08 1.42687E-08 1.50239E-08 1.53761E-08 1.53246E-08 1.49611E-08 1.44268E-08 1.38754E-08 1.31625E-08 1.25099E-08 1.25153E-08 1.42032E-08 1.77108E-08 2.25450E-08 2.87233E-08 3.28039E-08 3.25220E-08 2.87805E-08 2.41115E-08 2.10786E-08 1.99955E-08 1.96202E-08 1.88015E-08 1.69023E-08 1.43791E-08 1.19100E-08 9.99141E-09 8.80203E-09 7.93100E-09 7.52789E-09 8.03339E-09 9.16535E-09 1.00388E-08 1.04709E-08 1.06486E-08 1.03459E-08 9.69202E-09 8.87536E-09 8.01173E-09 7.15691E-09 6.32868E-09 5.52258E-09 4.73767E-09 3.97730E-09 3.31611E-09 2.64651E-09 2.27377E-09 1.98071E-09 1.87590E-09 1.83679E-09 1.80374E-09 1.74085E-09 1.62303E-09 1.40967E-09 1.25413E-09 1.00964E-09 4.80491E-10 2.11866E-10 9.87771E-11 4.43161E-11 1.82615E-11 6.88365E-12 2.41023E-12 8.08437E-13 2.69592E-13 9.03887E-14
1.00291E-13 3.09578E-13 9.69410E-13 3.06400E-12 9.50825E-12 2.82261E-11 7.88633E-11 2.05589E-10 4.97255E-10 1.11022E-09 2.24351E-09 3.92819E-09 5.79462E-09 7.40172E-09 8.57749E-09 9.47480E-09 1.01960E-08 1.06898E-08 1.09436E-08 1.09611E-08 1.07838E-08 1.05014E-08 1.01811E-08 9.97381E-09 1.01025E-08 1.08935E-08 1.26925E-08 1.55304E-08 1.90388E-08 2.33962E-08 2.62872E-08 2.65280E-08 2.45516E-08 2.16871E-08 1.94958E-08 1.82962E-08 1.75802E-08 1.65498E-08 1.49138E-08 1.28468E-08 1.07956E-08 9.17292E-09 7.92457E-09 6.97593E-09 6.48324E-09 6.38049E-09 6.97941E-09 7.72720E-09 8.17911E-09 8.37416E-09 8.36571E-09 8.03001E-09 7.48958E-09 6.84088E-09 6.14292E-09 5.42532E-09 4.69983E-09 3.97609E-09 3.28197E-09 2.69547E-09 2.13889E-09 1.82781E-09 1.61833E-09 1.54897E-09 1.53551E-09 1.51804E-09 1.49590E-09 1.41467E-09 1.25122E-09 1.06965E-09 8.75561E-10 4.50144E-10 2.05449E-10 9.69446E-11 4.38100E-11 1.81387E-11 6.85724E-12 2.40511E-12 8.07539E-13 2.69449E-13 9.03676E-14
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9.85290E-14 2.97576E-13 8.94124E-13 2.63162E-12 7.26366E-12 1.78738E-11 3.76378E-11 6.66855E-11 1.00625E-10 1.33392E-10 1.60813E-10 1.81649E-10 1.96581E-10 2.06968E-10 2.14110E-10 2.19785E-10 2.25178E-10 2.30229E-10 2.34887E-10 2.39097E-10 2.42801E-10 2.45941E-10 2.48461E-10 2.50307E-10 2.51428E-10 2.51779E-10 2.51319E-10 2.50019E-10 2.48331E-10 2.46964E-10 2.44608E-10 2.41266E-10 2.36953E-10 2.31701E-10 2.25555E-10 2.18571E-10 2.10818E-10 2.02373E-10 1.93324E-10 1.83762E-10 1.73783E-10 1.63481E-10 1.52947E-10 1.42273E-10 1.31541E-10 1.21124E-10 1.11057E-10 1.03353E-10 9.82055E-11 9.62554E-11 9.63874E-11 9.65126E-11 9.66144E-11 9.63741E-11 9.54724E-11 9.39593E-11 9.18903E-11 8.94950E-11 8.70891E-11 8.45571E-11 8.23082E-11 7.98607E-11 7.71886E-11 7.45538E-11 7.22056E-11 6.99059E-11 6.76809E-11 6.56747E-11 6.34419E-11 6.01055E-11 5.55396E-11 5.05472E-11 4.38281E-11 3.36189E-11 2.15953E-11 1.15509E-11 5.22201E-12 2.05954E-12 7.44143E-13 2.59070E-13 8.88195E-14
9.82463E-14 2.95685E-13 8.82552E-13 2.56825E-12 6.96387E-12 1.67020E-11 3.40506E-11 5.83022E-11 8.53214E-11 1.10350E-10 1.30569E-10 1.45475E-10 1.55889E-10 1.62977E-10 1.67764E-10 1.71503E-10 1.74995E-10 1.78205E-10 1.81099E-10 1.83640E-10 1.85793E-10 1.87521E-10 1.88789E-10 1.89567E-10 1.89826E-10 1.89541E-10 1.88692E-10 1.87263E-10 1.85762E-10 1.84367E-10 1.82313E-10 1.79605E-10 1.76254E-10 1.72282E-10 1.67717E-10 1.62596E-10 1.56962E-10 1.50861E-10 1.44346E-10 1.37475E-10 1.30305E-10 1.22897E-10 1.15310E-10 1.07600E-10 9.98228E-11 9.22850E-11 8.49465E-11 7.91393E-11 7.50287E-11 7.30825E-11 7.30559E-11 7.30352E-11 7.30190E-11 7.29232E-11 7.23754E-11 7.14059E-11 7.00487E-11 6.83915E-11 6.67409E-11 6.49858E-11 6.33189E-11 6.16696E-11 5.98274E-11 5.80042E-11 5.62788E-11 5.46657E-11 5.30839E-11 5.15254E-11 4.99954E-11 4.78326E-11 4.47483E-11 4.15852E-11 3.69491E-11 2.93414E-11 1.95612E-11 1.08052E-11 5.00684E-12 2.00947E-12 7.34450E-13 2.57437E-13 8.85710E-14
9.78819E-14 2.93262E-13 8.67847E-13 2.48897E-12 6.59922E-12 1.53374E-11 3.00989E-11 4.95772E-11 7.01394E-11 8.83045E-11 1.02385E-10 1.12401E-10 1.19184E-10 1.23679E-10 1.26645E-10 1.28907E-10 1.30975E-10 1.32828E-10 1.34442E-10 1.35797E-10 1.36871E-10 1.37645E-10 1.38099E-10 1.38217E-10 1.37985E-10 1.37389E-10 1.36421E-10 1.35074E-10 1.33811E-10 1.32552E-10 1.30875E-10 1.28786E-10 1.26292E-10 1.23406E-10 1.20145E-10 1.16532E-10 1.12591E-10 1.08352E-10 1.03842E-10 9.90961E-11 9.41480E-11 8.90335E-11 8.37883E-11 7.84469E-11 7.30432E-11 6.77996E-11 6.26579E-11 5.83120E-11 5.50300E-11 5.34055E-11 5.29755E-11 5.29159E-11 5.28504E-11 5.27637E-11 5.24773E-11 5.19157E-11 5.10981E-11 5.00457E-11 4.89525E-11 4.78152E-11 4.66595E-11 4.56012E-11 4.44813E-11 4.32984E-11 4.21304E-11 4.10822E-11 4.00388E-11 3.89027E-11 3.79095E-11 3.66340E-11 3.47793E-11 3.30504E-11 3.01604E-11 2.48716E-11 1.72995E-11 9.93254E-12 4.74524E-12 1.94695E-12 7.22147E-13 2.55345E-13 8.82505E-14
9.74101E-14 2.90149E-13 8.49164E-13 2.39027E-12 6.16106E-12 1.37821E-11 2.58747E-11 4.08183E-11 5.56653E-11 6.80912E-11 7.72716E-11 8.35284E-11 8.76057E-11 9.02150E-11 9.18830E-11 9.31152E-11 9.42055E-11 9.51414E-11 9.59106E-11 9.65014E-11 9.69029E-11 9.71051E-11 9.70987E-11 9.68753E-11 9.64283E-11 9.57523E-11 9.48440E-11 9.37014E-11 9.26813E-11 9.16426E-11 9.03526E-11 8.88147E-11 8.70344E-11 8.50191E-11 8.27788E-11 8.03258E-11 7.76745E-11 7.48404E-11 7.18402E-11 6.86917E-11 6.54140E-11 6.20271E-11 5.85514E-11 5.50069E-11 5.14133E-11 4.79098E-11 4.44512E-11 4.12415E-11 3.89822E-11 3.74683E-11 3.67294E-11 3.66300E-11 3.65187E-11 3.64046E-11 3.62898E-11 3.60055E-11 3.55619E-11 3.49701E-11 3.42833E-11 3.36184E-11 3.29028E-11 3.22326E-11 3.15669E-11 3.08723E-11 3.01831E-11 2.95184E-11 2.89063E-11 2.81975E-11 2.75621E-11 2.69120E-11 2.59717E-11 2.52548E-11 2.37272E-11 2.03732E-11 1.48602E-11 8.93202E-12 4.43078E-12 1.86925E-12 7.06535E-13 2.52658E-13 8.78354E-14
9.67954E-14 2.86136E-13 8.25433E-13 2.26817E-12 5.64280E-12 1.20557E-11 2.15158E-11 3.23754E-11 4.24575E-11 5.03912E-11 5.59325E-11 5.95158E-11 6.17371E-11 6.30912E-11 6.39170E-11 6.44950E-11 6.49769E-11 6.53563E-11 6.56272E-11 6.57838E-11 6.58209E-11 6.57340E-11 6.55190E-11 6.51725E-11 6.46915E-11 6.40744E-11 6.33201E-11 6.24286E-11 6.16342E-11 6.08391E-11 5.99002E-11 5.88199E-11 5.76019E-11 5.62504E-11 5.47708E-11 5.31696E-11 5.14544E-11 4.96335E-11 4.77160E-11 4.57111E-11 4.36288E-11 4.14796E-11 3.92743E-11 3.70239E-11 3.47393E-11 3.24922E-11 3.02597E-11 2.81016E-11 2.64803E-11 2.51406E-11 2.44148E-11 2.41525E-11 2.40547E-11 2.39713E-11 2.38789E-11 2.37625E-11 2.35530E-11 2.32557E-11 2.28767E-11 2.24940E-11 2.21028E-11 2.17027E-11 2.13416E-11 2.09601E-11 2.05958E-11 2.02350E-11 1.98970E-11 1.95079E-11 1.91277E-11 1.88866E-11 1.85338E-11 1.84627E-11 1.78929E-11 1.60375E-11 1.23240E-11 7.81380E-12 4.05832E-12 1.77328E-12 6.86734E-13 2.49196E-13 8.72946E-14
9.58839E-14 2.80293E-13 7.91737E-13 2.10183E-12 4.98034E-12 1.00232E-11 1.68067E-11 2.39038E-11 2.99328E-11 3.42920E-11 3.70977E-11 3.87690E-11 3.97184E-11 4.02437E-11 4.05308E-11 4.07035E-11 4.08200E-11 4.08777E-11 4.08744E-11 4.08080E-11 4.06765E-11 4.04784E-11 4.02124E-11 3.98776E-11 3.94733E-11 3.89990E-11 3.84548E-11 3.78412E-11 3.72416E-11 3.67037E-11 3.60948E-11 3.54162E-11 3.46696E-11 3.38573E-11 3.29816E-11 3.20455E-11 3.10522E-11 3.00054E-11 2.89091E-11 2.77677E-11 2.65853E-11 2.53668E-11 2.41169E-11 2.28408E-11 2.15434E-11 2.02363E-11 1.89620E-11 1.76951E-11 1.64574E-11 1.56028E-11 1.48611E-11 1.44316E-11 1.42477E-11 1.41781E-11 1.41204E-11 1.40565E-11 1.40025E-11 1.39031E-11 1.37606E-11 1.35773E-11 1.34008E-11 1.32200E-11 1.30392E-11 1.28748E-11 1.26993E-11 1.25560E-11 1.24137E-11 1.22520E-11 1.20784E-11 1.20205E-11 1.20131E-11 1.23052E-11 1.23939E-11 1.17070E-11 9.57610E-12 6.48913E-12 3.58096E-12 1.64266E-12 6.58659E-13 2.44158E-13 8.64923E-14
9.45853E-14 2.72125E-13 7.45882E-13 1.88587E-12 4.18208E-12 7.79999E-12 1.21503E-11 1.62390E-11 1.93759E-11 2.14256E-11 2.26089E-11 2.32274E-11 2.35214E-11 2.36446E-11 2.36846E-11 2.36824E-11 2.36510E-11 2.35896E-11 2.34974E-11 2.33739E-11 2.32184E-11 2.30306E-11 2.28102E-11 2.25570E-11 2.22711E-11 2.19526E-11 2.16015E-11 2.12185E-11 2.08098E-11 2.04768E-11 2.01119E-11 1.97157E-11 1.92889E-11 1.88326E-11 1.83480E-11 1.78363E-11 1.72988E-11 1.67370E-11 1.61527E-11 1.55476E-11 1.49235E-11 1.42823E-11 1.36260E-11 1.29566E-11 1.22763E-11 1.15871E-11 1.09034E-11 1.02289E-11 9.56282E-12 8.93731E-12 8.47900E-12 8.08176E-12 7.84549E-12 7.69854E-12 7.65803E-12 7.62033E-12 7.58228E-12 7.55754E-12 7.51848E-12 7.46030E-12 7.38393E-12 7.31582E-12 7.24615E-12 7.17996E-12 7.11762E-12 7.06398E-12 7.01976E-12 6.97063E-12 6.92949E-12 6.95132E-12 7.06851E-12 7.44475E-12 7.81153E-12 7.79631E-12 6.84191E-12 5.02678E-12 3.00325E-12 1.47319E-12 6.20516E-13 2.37118E-13 8.53487E-14
9.29105E-14 2.61903E-13 6.90857E-13 1.64355E-12 3.36904E-12 5.77987E-12 8.35514E-12 1.05216E-11 1.20107E-11 1.28718E-11 1.32952E-11 1.34641E-11 1.35040E-11 1.34871E-11 1.34506E-11 1.34024E-11 1.33400E-11 1.32630E-11 1.31712E-11 1.30646E-11 1.29431E-11 1.28064E-11 1.26547E-11 1.24880E-11 1.23064E-11 1.21100E-11 1.18991E-11 1.16737E-11 1.14343E-11 1.12059E-11 1.09970E-11 1.07744E-11 1.05384E-11 1.02894E-11 1.00281E-11 9.75498E-12 9.47048E-12 9.17526E-12 8.87002E-12 8.55550E-12 8.23245E-12 7.90159E-12 7.56370E-12 7.21963E-12 6.87023E-12 6.51634E-12 6.15882E-12 5.80525E-12 5.45640E-12 5.10952E-12 4.77615E-12 4.53732E-12 4.30485E-12 4.17421E-12 4.04644E-12 4.01937E-12 3.99302E-12 3.97821E-12 3.95837E-12 3.94710E-12 3.92761E-12 3.90021E-12 3.87555E-12 3.85423E-12 3.83325E-12 3.81803E-12 3.80569E-12 3.79848E-12 3.79650E-12 3.84150E-12 3.95655E-12 4.26273E-12 4.64846E-12 4.89623E-12 4.61056E-12 3.68796E-12 2.41122E-12 1.28282E-12 5.74791E-13 2.28312E-13 8.38727E-14
9.06671E-14 2.48805E-13 6.24443E-13 1.37534E-12 2.56476E-12 4.00741E-12 5.35705E-12 6.35734E-12 6.95622E-12 7.24368E-12 7.34172E-12 7.34416E-12 7.30730E-12 7.26003E-12 7.21523E-12 7.16992E-12 7.11836E-12 7.06049E-12 6.99628E-12 6.92573E-12 6.84883E-12 6.76561E-12 6.67607E-12 6.58027E-12 6.47830E-12 6.37025E-12 6.25618E-12 6.13622E-12 6.01050E-12 5.87921E-12 5.74374E-12 5.62936E-12 5.50959E-12 5.38460E-12 5.25460E-12 5.11977E-12 4.98031E-12 4.83645E-12 4.68844E-12 4.53655E-12 4.38104E-12 4.22217E-12 4.06024E-12 3.89553E-12 3.72835E-12 3.55900E-12 3.38781E-12 3.21509E-12 3.04115E-12 2.87034E-12 2.70128E-12 2.53193E-12 2.37434E-12 2.25785E-12 2.14781E-12 2.07037E-12 2.00737E-12 1.96590E-12 1.95374E-12 1.94469E-12 1.93651E-12 1.92894E-12 1.92424E-12 1.91643E-12 1.91099E-12 1.90857E-12 1.90707E-12 1.90928E-12 1.91667E-12 1.94195E-12 2.01744E-12 2.19623E-12 2.49199E-12 2.79163E-12 2.84879E-12 2.50718E-12 1.82182E-12 1.07161E-12 5.19628E-13 2.17034E-13 8.18944E-14
8.76274E-14 2.32052E-13 5.45719E-13 1.08871E-12 1.80554E-12 2.52935E-12 3.10601E-12 3.47319E-12 3.65506E-12 3.71442E-12 3.70935E-12 3.67777E-12 3.63984E-12 3.60461E-12 3.57520E-12 3.54764E-12 3.51773E-12 3.48549E-12 3.45089E-12 3.41395E-12 3.37468E-12 3.33309E-12 3.28920E-12 3.24301E-12 3.19457E-12 3.14392E-12 3.09106E-12 3.03606E-12 2.97895E-12 2.91978E-12 2.85862E-12 2.79551E-12 2.73051E-12 2.66370E-12 2.60454E-12 2.54603E-12 2.48580E-12 2.42392E-12 2.36046E-12 2.29551E-12 2.22913E-12 2.16141E-12 2.09243E-12 2.02228E-12 1.95106E-12 1.87886E-12 1.80576E-12 1.73188E-12 1.65730E-12 1.58213E-12 1.50647E-12 1.43040E-12 1.35405E-12 1.27941E-12 1.20552E-12 1.13158E-12 1.06482E-12 1.01509E-12 9.68017E-13 9.24674E-13 8.96954E-13 8.72307E-13 8.54023E-13 8.48478E-13 8.45922E-13 8.42764E-13 8.39459E-13 8.38982E-13 8.44741E-13 8.55990E-13 8.83681E-13 9.55281E-13 1.10925E-12 1.33704E-12 1.52295E-12 1.51613E-12 1.26106E-12 8.44907E-13 4.54188E-13 2.02613E-13 7.92114E-14
8.36871E-14 2.11997E-13 4.60601E-13 8.15381E-13 1.17142E-12 1.43328E-12 1.58758E-12 1.66071E-12 1.68239E-12 1.67669E-12 1.65967E-12 1.64019E-12 1.62233E-12 1.60750E-12 1.59582E-12 1.58528E-12 1.57415E-12 1.56245E-12 1.55015E-12 1.53727E-12 1.52380E-12 1.50973E-12 1.49507E-12 1.47981E-12 1.46395E-12 1.44749E-12 1.43044E-12 1.41279E-12 1.39454E-12 1.37571E-12 1.35628E-12 1.33627E-12 1.31567E-12 1.29450E-12 1.27277E-12 1.25046E-12 1.22761E-12 1.20420E-12 1.18027E-12 1.15580E-12 1.13082E-12 1.10534E-12 1.07936E-12 1.05292E-12 1.02601E-12 1.00195E-12 9.78502E-13 9.54632E-13 9.30349E-13 9.05668E-13 8.80605E-13 8.55175E-13 8.29397E-13 8.03290E-13 7.76874E-13 7.50167E-13 7.23191E-13 6.95967E-13 6.68518E-13 6.40865E-13 6.13031E-13 5.85039E-13 5.56915E-13 5.28682E-13 5.00433E-13 4.73117E-13 4.45755E-13 4.13820E-13 3.84114E-13 3.45355E-13 3.18491E-13 3.24929E-13 3.61710E-13 4.47700E-13 6.15733E-13 7.72106E-13 7.87687E-13 6.27249E-13 3.83284E-13 1.85351E-13 7.57304E-14
simpegDCIP/Dev/functions/convertObs_DC3D_to_2D.py → simpegDCIP/Dev/Workspace/convertObs_DC3D_to_2D.py
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simpegDCIP/Dev/functions/gen_DCIPsurvey.py → simpegDCIP/Dev/Workspace/gen_DCIPsurvey.py
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simpegDCIP/Dev/functions/plot_pseudoSection.py → simpegDCIP/Dev/Workspace/plot_pseudoSection.py
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simpegDCIP/Dev/functions/readUBC_DC2DLoc.py → simpegDCIP/Dev/Workspace/readUBC_DC2DLoc.py
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simpegDCIP/Dev/functions/readUBC_DC2DMesh.py → simpegDCIP/Dev/Workspace/readUBC_DC2DMesh.py
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simpegDCIP/Dev/functions/readUBC_DC2DModel.py → simpegDCIP/Dev/Workspace/readUBC_DC2DModel.py
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simpegDCIP/Dev/functions/readUBC_DC3Dobs.py → simpegDCIP/Dev/Workspace/readUBC_DC3Dobs.py
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simpegDCIP/Dev/functions/writeUBC_DCobs.py → simpegDCIP/Dev/Workspace/writeUBC_DCobs.py
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