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Improved gradient plot and sections.

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D Fournier
2015-12-15 11:59:44 -08:00
parent 2a76852e33
commit fc3893c72f
15 changed files with 2270 additions and 1741 deletions
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simpegDCIP/Dev/DC3D_Demo_TwoSpheres.py
+417
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"""
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
from matplotlib.colors import LogNorm
import os
home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
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 = 'dpdp'
# Survey parameters
a = 30
b = 30
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 = [(423230. , 546440.), (423715. , 546440.)]
#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] = 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
writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
#%% Load 3D data
[Tx, Rx, data, wd] = readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
#%% Convert 3D obs to 2D and write to file
[Tx2d, Rx2d] = convertObs_DC3D_to_2D(Tx,Rx)
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(1,1,1)
plt.xlim([-dx,nc*dx+dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
plt.gca().set_aspect('equal', adjustable='box')
circle1=plt.Circle((150,1500),50,color='w',fill=False, lw=3)
circle2=plt.Circle((325,1500),50,color='k',fill=False, lw=3)
axs.add_artist(circle1)
axs.add_artist(circle2)
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D))#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
cmin,cmax = cbar.get_clim()
ticks = np.linspace(cmin,cmax,3)
cbar.set_ticks(ticks)
# Plot poles
plt.scatter(Tx2d[0][0],mesh2d.vectorNy[-1]+dx,s=50,c='r',marker='v')
plt.scatter(Tx2d[0][1],mesh2d.vectorNy[-1]+dx,s=50,c='b',marker='v')
plt.scatter(Rx2d[0][:,0],np.ones(Rx2d[0].shape[0])*mesh2d.vectorNy[-1]+dx,s=50,c='g')
#mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
#%% Plot pseudo section
plt.figure()
axs = plt.subplot(1,1,1)
plt.xlim([-dx,nc*dx+dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
plt.gca().set_aspect('equal', adjustable='box')
circle1=plt.Circle((150,1500),50,color='w',fill=False, lw=3)
circle2=plt.Circle((325,1500),50,color='k',fill=False, lw=3)
axs.add_artist(circle1)
axs.add_artist(circle2)
plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
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
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 = readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
plt.figure()
axs = plt.subplot(1,1,1)
plt.xlim([-dx,nc*dx+dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
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')
circle1=plt.Circle((150,1500),50,color='w',fill=False, lw=3)
circle2=plt.Circle((325,1500),50,color='k',fill=False, lw=3)
axs.add_artist(circle1)
axs.add_artist(circle2)
axp = plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=1,vmin = np.min(np.log10(minv)), vmax = np.max(np.log10(minv)))
#t = [-3, -2, -1]
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
cmin,cmax = cbar.get_clim()
ticks = np.linspace(cmin,cmax,3)
cbar.set_ticks(ticks)
#cbar.set_ticklabels('%.2f')
#%% 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][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],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 *((rC1P1)**2 / rP1P2)#/ ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )
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.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()
simpegDCIP/Dev/DC3D_fwr_live_test.py
+54 -30
View File
@@ -37,34 +37,41 @@ from gen_DCIPsurvey import gen_DCIPsurvey
from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
import os
home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
dsep = '\\'
#from scipy.linalg import solve_banded
# Load UBC mesh 3D
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_20m.msh')
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 + '\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
#model = model**0 * 1e-2
# Specify survey type
stype = 'gradient'
stype = 'dpdp'
# Survey parameters
a = 40
n = 10
a = 30
b = 30
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-3
ref_mod = 1e-2
#%% Create system
#Set boundary conditions
@@ -83,9 +90,15 @@ A = sp.csc_matrix(A)
start_time = time.time()
if re.match(slvr,'BiCGStab'):
# Create Jacobi Preconditioner
dA = A.diagonal()
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
# 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
@@ -100,7 +113,7 @@ 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([546200,547200])
plt.ylim([546200,547000])
plt.gca().set_aspect('equal', adjustable='box')
plt.show()
@@ -115,8 +128,11 @@ cfm1.activateWindow()
plt.sca(ax_prim)
# Takes two points from ginput and create survey
#gin = plt.ginput(2, timeout = 0)
gin = [(423187. , 546311.), (423867. , 546991.)]
#if re.match(stype,'gradient'):
# gin = [(423187. , 546311.), (423867. , 546991.)]
#else:
gin = plt.ginput(2, timeout = 0)
#==============================================================================
@@ -124,8 +140,6 @@ gin = [(423187. , 546311.), (423867. , 546991.)]
# print 'SimPED - Simulation has ended with return'
# break
#==============================================================================
#gin = [(465.9879032258068, 12141.34375), (1648.0443548387098, 12149.083333333334)]
# Add z coordinate to all survey... assume flat
nz = mesh.vectorNz
@@ -135,9 +149,9 @@ var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
indx = Utils.closestPoints(mesh, var )
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
[Tx, Rx] = gen_DCIPsurvey(endl, mesh, stype, a, n)
[Tx, Rx] = gen_DCIPsurvey(endl, mesh, stype, a, b, n)
dl_len = np.sqrt( np.sum((Tx[0][0:2,0] - Tx[-1][0:2,1])**2) )
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)
@@ -180,17 +194,26 @@ for ii in range(len(Tx)):
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
if re.match(slvr,'BiCGStab'):
# Create Jacobi Preconditioner
dA = A.diagonal()
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
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)
# Iterative Solve
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
phi = mkvc(Ainvb[0])
# Compute potential at each electrode
dtemp = (P1*phi - P2*phi)*np.pi
@@ -257,7 +280,7 @@ if not re.match(stype,'gradient'):
axs = plt.subplot(2,1,1)
plt.xlim([0,nc*dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,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]])
@@ -325,7 +348,7 @@ if not re.match(stype,'gradient'):
axs = plt.subplot(2,1,2)
plt.xlim([0,nc*dx])
plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]])
plt.gca().set_aspect('equal', adjustable='box')
minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
@@ -345,16 +368,17 @@ elif re.match(stype,'gradient'):
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])# * 2*np.pi / ((rC1C2/2.)**2 - (rP1P2/2.)**2) / rP1P2
rho = np.abs(data[0]) * np.pi *((rC1P1)**2 / rP1P2)#/ ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )
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]]), np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]])]
grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], np.log10(abs(1/rho.T)), (grid_x, grid_z), method='linear')
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.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 - Ratio (max-min)/max % =' + str(np.round((np.max(rho)-np.min(rho))/np.max(rho)*100.))
var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
plt.title(var)
plt.colorbar()
simpegDCIP/Dev/FWR_3D_2_2D.dat
+75 -121
View File
@@ -1,123 +1,77 @@
! GENERAL FORMAT
0.000000e+00 8.400135e+02 2.828374e+02 3.123907e+02 -2.057527e-02 3.057527e-04
0.000000e+00 8.400135e+02 3.123907e+02 3.440508e+02 -2.154118e-02 3.154118e-04
0.000000e+00 8.400135e+02 3.440508e+02 3.774103e+02 -2.683822e-02 3.683822e-04
0.000000e+00 8.400135e+02 3.774103e+02 4.118482e+02 -3.762848e-02 4.762848e-04
0.000000e+00 8.400135e+02 4.118482e+02 4.472384e+02 -3.681028e-02 4.681028e-04
0.000000e+00 8.400135e+02 4.472384e+02 4.833161e+02 -3.347386e-02 4.347386e-04
0.000000e+00 8.400135e+02 4.833161e+02 5.199614e+02 -2.948096e-02 3.948096e-04
0.000000e+00 8.400135e+02 5.199614e+02 5.571043e+02 -1.704286e-03 1.170429e-04
0.000000e+00 8.400135e+02 5.571043e+02 5.946672e+02 3.024944e-03 1.302494e-04
0.000000e+00 8.400135e+02 5.946672e+02 6.324898e+02 2.353606e-03 1.235361e-04
0.000000e+00 8.400135e+02 6.324898e+02 6.705208e+02 7.767006e-03 1.776701e-04
0.000000e+00 8.400135e+02 2.561700e+02 2.884667e+02 -2.259887e-02 3.259887e-04
0.000000e+00 8.400135e+02 2.884667e+02 3.224843e+02 -2.478452e-02 3.478452e-04
0.000000e+00 8.400135e+02 3.224843e+02 3.577652e+02 -3.917151e-02 4.917151e-04
0.000000e+00 8.400135e+02 3.577652e+02 3.939173e+02 -4.730231e-02 5.730231e-04
0.000000e+00 8.400135e+02 3.939173e+02 4.307831e+02 -4.297424e-02 5.297424e-04
0.000000e+00 8.400135e+02 4.307831e+02 4.682147e+02 -4.112094e-02 5.112094e-04
0.000000e+00 8.400135e+02 4.682147e+02 5.060011e+02 -2.491715e-02 3.491715e-04
0.000000e+00 8.400135e+02 5.060011e+02 5.440600e+02 7.771423e-05 1.007771e-04
0.000000e+00 8.400135e+02 5.440600e+02 5.823732e+02 -5.594451e-03 1.559445e-04
0.000000e+00 8.400135e+02 5.823732e+02 6.209451e+02 3.106795e-03 1.310679e-04
0.000000e+00 8.400135e+02 6.209451e+02 6.596377e+02 1.267745e-02 2.267745e-04
0.000000e+00 8.400135e+02 2.332299e+02 2.682920e+02 -2.799864e-02 3.799864e-04
0.000000e+00 8.400135e+02 2.682920e+02 3.046930e+02 -3.451769e-02 4.451769e-04
0.000000e+00 8.400135e+02 3.046930e+02 3.417890e+02 -4.844293e-02 5.844293e-04
0.000000e+00 8.400135e+02 3.417890e+02 3.794662e+02 -5.520628e-02 6.520628e-04
0.000000e+00 8.400135e+02 3.794662e+02 4.176163e+02 -5.581737e-02 6.581737e-04
0.000000e+00 8.400135e+02 4.176163e+02 4.560374e+02 -4.744215e-02 5.744215e-04
0.000000e+00 8.400135e+02 4.560374e+02 4.947547e+02 -1.225269e-02 2.225269e-04
0.000000e+00 8.400135e+02 4.947547e+02 5.336951e+02 -1.344730e-02 2.344730e-04
0.000000e+00 8.400135e+02 5.336951e+02 5.727578e+02 -1.171166e-02 2.171166e-04
0.000000e+00 8.400135e+02 5.727578e+02 6.118885e+02 5.346910e-03 1.534691e-04
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0.000000e+00 8.400135e+02 2.529775e+02 2.912116e+02 -4.143742e-02 5.143742e-04
0.000000e+00 8.400135e+02 2.912116e+02 3.298189e+02 -5.463578e-02 6.463578e-04
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0.000000e+00 8.400135e+02 3.687688e+02 4.079531e+02 -7.137670e-02 8.137670e-04
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0.000000e+00 8.400135e+02 4.472635e+02 4.866315e+02 -1.409697e-02 2.409697e-04
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0.000000e+00 8.400135e+02 5.260882e+02 5.656765e+02 -2.103318e-02 3.103318e-04
0.000000e+00 8.400135e+02 5.656765e+02 6.052606e+02 1.030322e-02 2.030322e-04
0.000000e+00 8.400135e+02 6.052606e+02 6.450312e+02 3.092502e-02 4.092502e-04
0.000000e+00 8.400135e+02 2.039354e+02 2.432458e+02 -4.019637e-02 5.019637e-04
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0.000000e+00 8.400135e+02 2.828777e+02 3.224843e+02 -5.715230e-02 6.715230e-04
0.000000e+00 8.400135e+02 3.224843e+02 3.622314e+02 -7.716031e-02 8.716031e-04
0.000000e+00 8.400135e+02 3.622314e+02 4.019699e+02 -8.038666e-02 9.038666e-04
0.000000e+00 8.400135e+02 4.019699e+02 4.418129e+02 -3.929514e-02 4.929514e-04
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0.000000e+00 8.400135e+02 5.215927e+02 5.614414e+02 -3.279248e-02 4.279248e-04
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0.000000e+00 8.400135e+02 6.013043e+02 6.412463e+02 4.181046e-02 5.181046e-04
0.000000e+00 8.400135e+02 1.999962e+02 2.400247e+02 -4.447915e-02 5.447915e-04
0.000000e+00 8.400135e+02 2.400247e+02 2.799813e+02 -5.013051e-02 6.013051e-04
0.000000e+00 8.400135e+02 2.799813e+02 3.200098e+02 -5.989494e-02 6.989494e-04
0.000000e+00 8.400135e+02 3.200098e+02 3.600359e+02 -7.602209e-02 8.602209e-04
0.000000e+00 8.400135e+02 3.600359e+02 3.999925e+02 -8.157308e-02 9.157308e-04
0.000000e+00 8.400135e+02 3.999925e+02 4.400210e+02 -4.671418e-02 5.671418e-04
0.000000e+00 8.400135e+02 4.400210e+02 4.799776e+02 -1.074983e-01 1.174983e-03
0.000000e+00 8.400135e+02 4.799776e+02 5.200060e+02 -9.208287e-02 1.020829e-03
0.000000e+00 8.400135e+02 5.200060e+02 5.599626e+02 -4.808770e-02 5.808770e-04
0.000000e+00 8.400135e+02 5.599626e+02 6.000606e+02 -1.692085e-03 1.169208e-04
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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
1.199694e+02 1.600076e+02 1.999732e+02 2.400076e+02 -1.321899e+00 1.331899e-02
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simpegDCIP/Dev/FWR_data3D.dat
+96 -122
View File
@@ -1,125 +1,99 @@
! GENERAL FORMAT
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simpegDCIP/Dev/Inv2D/FWR_3D_2_2D.dat
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9.99987E-04 9.99977E-04 9.99944E-04 9.99856E-04 9.99647E-04 9.99227E-04 9.98574E-04 9.97789E-04 9.97033E-04 9.96469E-04 9.96088E-04 9.95861E-04 9.95736E-04 9.95674E-04 9.95648E-04 9.95632E-04 9.95620E-04 9.95617E-04 9.95629E-04 9.95652E-04 9.95685E-04 9.95728E-04 9.95782E-04 9.95846E-04 9.95922E-04 9.96009E-04 9.96107E-04 9.96216E-04 9.96334E-04 9.96463E-04 9.96604E-04 9.96754E-04 9.96912E-04 9.97075E-04 9.97236E-04 9.97393E-04 9.97567E-04 9.97759E-04 9.97949E-04 9.98139E-04 9.98354E-04 9.98625E-04 9.99026E-04 9.99572E-04 1.00017E-03 1.00076E-03 1.00108E-03 1.00101E-03 1.00067E-03 1.00033E-03 1.00013E-03 1.00004E-03 1.00001E-03 9.99999E-04
9.99988E-04 9.99977E-04 9.99947E-04 9.99874E-04 9.99724E-04 9.99478E-04 9.99169E-04 9.98862E-04 9.98622E-04 9.98461E-04 9.98369E-04 9.98327E-04 9.98311E-04 9.98307E-04 9.98308E-04 9.98311E-04 9.98317E-04 9.98327E-04 9.98339E-04 9.98354E-04 9.98372E-04 9.98393E-04 9.98416E-04 9.98442E-04 9.98472E-04 9.98505E-04 9.98540E-04 9.98578E-04 9.98619E-04 9.98663E-04 9.98709E-04 9.98758E-04 9.98809E-04 9.98861E-04 9.98917E-04 9.98973E-04 9.99029E-04 9.99085E-04 9.99144E-04 9.99207E-04 9.99286E-04 9.99388E-04 9.99515E-04 9.99697E-04 9.99935E-04 1.00019E-03 1.00041E-03 1.00048E-03 1.00040E-03 1.00024E-03 1.00011E-03 1.00004E-03 1.00001E-03 9.99999E-04
9.99988E-04 9.99978E-04 9.99952E-04 9.99892E-04 9.99791E-04 9.99662E-04 9.99539E-04 9.99445E-04 9.99377E-04 9.99334E-04 9.99314E-04 9.99307E-04 9.99304E-04 9.99304E-04 9.99305E-04 9.99307E-04 9.99310E-04 9.99313E-04 9.99317E-04 9.99322E-04 9.99328E-04 9.99334E-04 9.99341E-04 9.99349E-04 9.99358E-04 9.99368E-04 9.99379E-04 9.99390E-04 9.99402E-04 9.99415E-04 9.99430E-04 9.99445E-04 9.99460E-04 9.99476E-04 9.99494E-04 9.99512E-04 9.99530E-04 9.99548E-04 9.99567E-04 9.99587E-04 9.99613E-04 9.99651E-04 9.99700E-04 9.99765E-04 9.99858E-04 9.99973E-04 1.00010E-03 1.00020E-03 1.00023E-03 1.00017E-03 1.00009E-03 1.00004E-03 1.00001E-03 9.99999E-04
58 30
9.99872E-04 9.99872E-04 9.99870E-04 9.99856E-04 9.99792E-04 9.99488E-04 9.98081E-04 9.92617E-04 9.77076E-04 9.47952E-04 9.13568E-04 8.90910E-04 8.95035E-04 9.32081E-04 9.94947E-04 1.00487E-03 1.01236E-03 1.05880E-03 1.18007E-03 1.17002E-03 1.04750E-03 1.27452E-03 1.03308E-03 1.12409E-03 1.31353E-03 9.96341E-04 1.18901E-03 9.88380E-04 1.14887E-03 2.43556E-03 5.40849E-03 1.82056E-03 1.11977E-03 1.17224E-03 9.54210E-04 1.11430E-03 1.07412E-03 1.13560E-03 1.09526E-03 9.36401E-04 7.64055E-04 7.21346E-04 7.31328E-04 7.51916E-04 7.78573E-04 8.14253E-04 8.57952E-04 9.05767E-04 9.49364E-04 9.79420E-04 9.93654E-04 9.98381E-04 9.99549E-04 9.99803E-04 9.99857E-04 9.99869E-04 9.99872E-04 9.99873E-04
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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
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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
simpegDCIP/Dev/Inv2D/dcinv2d.log
+233 -210
View File
@@ -14,7 +14,7 @@ Distributed by:
Mira Geoscience Ltd.
DCINV2D started on:12/11/2015 12:49:39
DCINV2D started on:12/14/2015 14:39:32
Reading input file: dcinv2d.inp
@@ -35,17 +35,17 @@ USE_MREF TRUE
maximum # of iterations: 100
data were read from: FWR_3D_2_2D.dat
# of current locations: 12
# of data: 72
# of current locations: 11
# of data: 65
chifact: 1.00000E+00
target misfit: 7.20000E+01
target misfit: 6.50000E+01
mesh was read from: Mesh_2D.msh
# of cells: 54 x 30
total # of cells: 1620
# of active cells: 1620
# of cells: 58 x 30
total # of cells: 1740
# of active cells: 1740
# of unique data locations: 12
# of unique data locations: 11
# 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
@@ -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: 23 + 3 + 1 = 27
number of basis vectors: 21 + 3 + 1 = 25
init cpu time: 0:00:00.05
initial misfit = 1.20369E+05
initial misfit = 2.29963E+05
init. model norm = 0.00000E+00
norm comp Ws = 0.00000E+00
norm comp Wx = 0.00000E+00
@@ -73,342 +73,365 @@ Iteration 1
beta vs. misfit:
beta misfit
1.79203E+04 2.40202E+04
3.58406E+04 3.35441E+04
8.96016E+04 5.31602E+04
1.14702E+05 5.96677E+04
1.16837E+05 6.01657E+04
1.16917E+05 6.01843E+04
2.92292E+05 8.46349E+04
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
chosen beta = 1.16917E+05
target misfit = 6.01844E+04
achieved misfit = 6.01844E+04
model norm = 2.14462E-01
chosen beta = 2.20496E+05
target misfit = 1.14982E+05
achieved misfit = 1.14982E+05
model norm = 2.23402E-01
misfit change = 5.00000E-01
model norm change = 0.00000E+00
norm comp Ws = 1.21979E-01
norm comp Wx = 5.71490E-02
norm comp Wz = 3.53340E-02
norm comp Ws = 1.20709E-01
norm comp Wx = 5.36664E-02
norm comp Wz = 4.90259E-02
iter cpu time: 0:00:00.30
iter cpu time: 0:00:00.35
Iteration 2
beta vs. misfit:
beta misfit
2.92293E+04 2.83571E+04
5.84585E+04 4.27230E+04
5.51240E+04 5.38776E+04
1.10248E+05 8.30234E+04
chosen beta = 3.23175E+04
target misfit = 3.00922E+04
achieved misfit = 3.01009E+04
model norm = 7.74780E-01
misfit change = 4.99855E-01
model norm change = 2.61267E+00
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
norm comp Ws = 3.95707E-01
norm comp Wx = 2.57560E-01
norm comp Wz = 1.21513E-01
norm comp Ws = 4.21387E-01
norm comp Wx = 2.22769E-01
norm comp Wz = 1.69261E-01
iter cpu time: 0:00:00.46
iter cpu time: 0:00:00.19
Iteration 3
beta vs. misfit:
beta misfit
8.07937E+03 1.24985E+04
1.61587E+04 1.94052E+04
1.52922E+04 2.43015E+04
3.05844E+04 3.74992E+04
chosen beta = 1.08271E+04
target misfit = 1.50505E+04
achieved misfit = 1.50193E+04
model norm = 1.64045E+00
misfit change = 5.01035E-01
model norm change = 1.11731E+00
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
norm comp Ws = 7.14500E-01
norm comp Wx = 6.78931E-01
norm comp Wz = 2.47017E-01
norm comp Ws = 8.31643E-01
norm comp Wx = 5.75399E-01
norm comp Wz = 3.36966E-01
iter cpu time: 0:00:00.17
iter cpu time: 0:00:00.45
Iteration 4
beta vs. misfit:
beta misfit
2.70679E+03 5.57540E+03
5.41357E+03 9.10604E+03
5.00045E+03 1.28642E+04
1.00009E+04 1.86842E+04
chosen beta = 4.12295E+03
target misfit = 7.50965E+03
achieved misfit = 7.52021E+03
model norm = 2.79115E+00
misfit change = 4.99297E-01
model norm change = 7.01455E-01
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
norm comp Ws = 1.04121E+00
norm comp Wx = 1.32464E+00
norm comp Wz = 4.25302E-01
norm comp Ws = 1.28958E+00
norm comp Wx = 1.28927E+00
norm comp Wz = 5.53778E-01
iter cpu time: 0:00:00.18
iter cpu time: 0:00:00.21
Iteration 5
beta vs. misfit:
beta misfit
1.03074E+03 2.54379E+03
2.06147E+03 4.28531E+03
1.50950E+03 5.99773E+03
3.01900E+03 9.21452E+03
chosen beta = 1.73265E+03
target misfit = 3.76010E+03
achieved misfit = 3.75408E+03
model norm = 4.19293E+00
misfit change = 5.00801E-01
model norm change = 5.02222E-01
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
norm comp Ws = 1.40651E+00
norm comp Wx = 2.08363E+00
norm comp Wz = 7.02789E-01
norm comp Ws = 1.74210E+00
norm comp Wx = 2.47066E+00
norm comp Wz = 9.74574E-01
iter cpu time: 0:00:00.35
iter cpu time: 0:00:00.27
Iteration 6
beta vs. misfit:
beta misfit
4.33163E+02 1.39429E+03
8.66326E+02 2.12817E+03
4.92062E+02 2.05275E+03
9.84123E+02 3.92917E+03
chosen beta = 7.05172E+02
target misfit = 1.87704E+03
achieved misfit = 1.85569E+03
model norm = 5.84262E+00
misfit change = 5.05685E-01
model norm change = 3.93448E-01
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
norm comp Ws = 1.80726E+00
norm comp Wx = 2.92669E+00
norm comp Wz = 1.10867E+00
norm comp Ws = 2.19885E+00
norm comp Wx = 3.77400E+00
norm comp Wz = 1.79107E+00
iter cpu time: 0:00:00.20
iter cpu time: 0:00:00.21
Iteration 7
beta vs. misfit:
beta misfit
1.76293E+02 8.23938E+02
3.52586E+02 1.12973E+03
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
chosen beta = 2.28828E+02
target misfit = 9.27847E+02
achieved misfit = 9.18382E+02
model norm = 7.96769E+00
misfit change = 5.05101E-01
model norm change = 3.63718E-01
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
norm comp Ws = 2.39387E+00
norm comp Wx = 3.93052E+00
norm comp Wz = 1.64329E+00
norm comp Ws = 2.65166E+00
norm comp Wx = 4.83319E+00
norm comp Wz = 2.92980E+00
iter cpu time: 0:00:00.18
iter cpu time: 0:00:00.26
Iteration 8
beta vs. misfit:
beta misfit
1.18196E+01 5.56593E+02
2.60031E+01 5.49364E+02
5.72069E+01 5.79150E+02
1.14414E+02 6.61868E+02
1.16247E+02 4.08040E+02
2.32494E+02 7.73743E+02
2.72599E+02 9.16098E+02
chosen beta = 2.60031E+01
target misfit = 4.59191E+02
achieved misfit = 5.49364E+02
model norm = 1.20297E+01
misfit change = 4.01813E-01
model norm change = 5.09814E-01
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
norm comp Ws = 2.50439E+00
norm comp Wx = 6.75644E+00
norm comp Wz = 2.76890E+00
norm comp Ws = 3.12662E+00
norm comp Wx = 5.80666E+00
norm comp Wz = 3.86755E+00
iter cpu time: 0:00:00.34
iter cpu time: 0:00:00.30
Iteration 9
beta vs. misfit:
beta misfit
2.95490E+00 2.89198E+02
6.50079E+00 2.90609E+02
1.30016E+01 2.96823E+02
6.67531E+01 2.47097E+02
1.33506E+02 4.19229E+02
1.45642E+02 4.54732E+02
chosen beta = 6.50079E+00
target misfit = 2.74682E+02
achieved misfit = 2.90609E+02
model norm = 1.61063E+01
misfit change = 4.71008E-01
model norm change = 3.38877E-01
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
norm comp Ws = 3.18688E+00
norm comp Wx = 8.74009E+00
norm comp Wz = 4.17936E+00
norm comp Ws = 3.56241E+00
norm comp Wx = 6.64092E+00
norm comp Wz = 4.83532E+00
iter cpu time: 0:00:00.18
iter cpu time: 0:00:00.21
Iteration 10
beta vs. misfit:
beta misfit
1.62520E+00 1.32688E+02
3.25039E+00 1.36212E+02
8.12598E+00 1.51520E+02
3.58330E+01 1.94330E+02
7.16660E+01 2.56470E+02
chosen beta = 5.66721E+00
target misfit = 1.45305E+02
achieved misfit = 1.43202E+02
model norm = 2.09909E+01
misfit change = 5.07237E-01
model norm change = 3.03268E-01
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
norm comp Ws = 3.87211E+00
norm comp Wx = 1.01396E+01
norm comp Wz = 6.97916E+00
norm comp Ws = 4.09954E+00
norm comp Wx = 7.65264E+00
norm comp Wz = 5.77092E+00
iter cpu time: 0:00:00.19
iter cpu time: 0:00:00.26
Iteration 11
beta vs. misfit:
beta misfit
6.51202E-01 1.00416E+02
1.43264E+00 1.00817E+02
2.84940E+00 1.02479E+02
2.63809E+00 1.40839E+02
5.80380E+00 1.40006E+02
1.27684E+01 1.44718E+02
2.55367E+01 1.58122E+02
chosen beta = 1.43264E+00
target misfit = 7.20000E+01
achieved misfit = 1.00817E+02
model norm = 2.48156E+01
misfit change = 2.95976E-01
model norm change = 1.82208E-01
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
norm comp Ws = 4.13209E+00
norm comp Wx = 1.18099E+01
norm comp Wz = 8.87357E+00
norm comp Ws = 5.01489E+00
norm comp Wx = 8.20406E+00
norm comp Wz = 6.88163E+00
iter cpu time: 0:00:00.29
iter cpu time: 0:00:00.22
Iteration 12
beta vs. misfit:
beta misfit
6.45407E-01 7.19782E+01
7.30688E-01 7.20262E+01
1.02314E+00 7.23329E+01
1.45095E+00 7.61897E+01
2.90190E+00 7.59815E+01
5.80380E+00 7.71909E+01
chosen beta = 6.82819E-01
target misfit = 7.20000E+01
achieved misfit = 7.19973E+01
model norm = 2.81474E+01
misfit change = 2.85865E-01
model norm change = 1.34263E-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
norm comp Ws = 4.48349E+00
norm comp Wx = 1.28628E+01
norm comp Wz = 1.08011E+01
norm comp Ws = 5.70037E+00
norm comp Wx = 9.53924E+00
norm comp Wz = 8.03554E+00
iter cpu time: 0:00:00.19
Target misfit achieved. Minimizing model norm.
iter cpu time: 0:00:00.23
Iteration 13
beta vs. misfit:
beta misfit
6.82845E-01 5.51235E+01
6.82871E-01 5.51235E+01
1.70718E+00 5.63927E+01
4.26794E+00 6.20058E+01
1.06699E+01 8.42251E+01
9.65320E-01 6.59663E+01
2.12370E+00 6.63626E+01
2.48250E+00 6.65339E+01
chosen beta = 6.67404E+00
target misfit = 7.20000E+01
achieved misfit = 6.92297E+01
model norm = 2.60729E+01
misfit change = 3.84394E-02
model norm change = -7.37010E-02
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
norm comp Ws = 4.54269E+00
norm comp Wx = 1.16020E+01
norm comp Wz = 9.92826E+00
norm comp Ws = 5.80622E+00
norm comp Wx = 9.69831E+00
norm comp Wz = 8.40024E+00
iter cpu time: 0:00:00.24
iter cpu time: 0:00:00.26
Iteration 14
beta vs. misfit:
beta misfit
6.94110E+00 6.96952E+01
7.21885E+00 7.05885E+01
7.67292E+00 7.20897E+01
2.03739E+00 4.61300E+01
2.08010E+00 4.61580E+01
5.20024E+00 5.05536E+01
1.30006E+01 6.80030E+01
chosen beta = 7.64528E+00
target misfit = 7.20000E+01
achieved misfit = 7.19969E+01
model norm = 2.47618E+01
misfit change = -3.99705E-02
model norm change = -5.02868E-02
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
norm comp Ws = 4.37322E+00
norm comp Wx = 1.12042E+01
norm comp Wz = 9.18441E+00
norm comp Ws = 5.35300E+00
norm comp Wx = 9.06543E+00
norm comp Wz = 8.60927E+00
iter cpu time: 0:00:00.34
iter cpu time: 0:00:00.30
Iteration 15
beta vs. misfit:
beta misfit
7.64561E+00 7.10307E+01
7.64594E+00 7.10318E+01
7.95109E+00 7.20211E+01
1.00060E+01 6.37285E+01
1.15036E+01 6.57179E+01
1.17036E+01 6.60002E+01
chosen beta = 7.94451E+00
target misfit = 7.20000E+01
achieved misfit = 7.19996E+01
model norm = 2.43805E+01
misfit change = -3.71422E-05
model norm change = -1.53965E-02
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
norm comp Ws = 4.48699E+00
norm comp Wx = 1.10931E+01
norm comp Wz = 8.80046E+00
norm comp Ws = 5.25353E+00
norm comp Wx = 9.12332E+00
norm comp Wz = 8.19063E+00
iter cpu time: 0:00:00.26
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
Exit at convergence.
Iterations performed: 15
Iterations performed: 16
total cpu time: 0:00:04.00
total cpu time: 0:00:04.60
DCINV2D ended on:12/11/2015 12:49:43
DCINV2D ended on:12/14/2015 14:39:36
simpegDCIP/Dev/Inv2D/dcinv2d.out
+20 -19
View File
@@ -1,19 +1,20 @@
15 iter data misfit model norm beta
0 1.20369E+05 0.00000E+00 0.00000E+00
1 6.01844E+04 2.14462E-01 1.16917E+05
2 3.01009E+04 7.74780E-01 3.23175E+04
3 1.50193E+04 1.64045E+00 1.08271E+04
4 7.52021E+03 2.79115E+00 4.12295E+03
5 3.75408E+03 4.19293E+00 1.73265E+03
6 1.85569E+03 5.84262E+00 7.05172E+02
7 9.18382E+02 7.96769E+00 2.28828E+02
8 5.49364E+02 1.20297E+01 2.60031E+01
9 2.90609E+02 1.61063E+01 6.50079E+00
10 1.43202E+02 2.09909E+01 5.66721E+00
11 1.00817E+02 2.48156E+01 1.43264E+00
12 7.19973E+01 2.81474E+01 6.82819E-01
13 6.92297E+01 2.60729E+01 6.67404E+00
14 7.19969E+01 2.47618E+01 7.64528E+00
15 7.19996E+01 2.43805E+01 7.94451E+00
7.20000E+01 target misfit
72 number of data
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
simpegDCIP/Dev/Inv2D/dcinv2d.pre
+65 -72
View File
@@ -1,73 +1,66 @@
! Predicted data ! GENERAL FORMAT
0.0000000E+00 0.0000000E+00 4.0064950E+01 7.9993120E+01 2.38498E+00
0.0000000E+00 0.0000000E+00 7.9993120E+01 1.2005810E+02 8.68138E-01
0.0000000E+00 0.0000000E+00 1.2005810E+02 1.5998620E+02 4.30517E-01
0.0000000E+00 0.0000000E+00 1.5998620E+02 2.0005120E+02 2.53695E-01
0.0000000E+00 0.0000000E+00 2.0005120E+02 2.3997940E+02 1.18464E-01
0.0000000E+00 0.0000000E+00 2.3997940E+02 2.8004430E+02 4.41007E-02
0.0000000E+00 0.0000000E+00 2.8004430E+02 3.1997250E+02 6.43269E-02
0.0000000E+00 0.0000000E+00 3.1997250E+02 3.6003740E+02 5.35173E-02
0.0000000E+00 0.0000000E+00 3.6003740E+02 3.9996560E+02 4.12748E-02
4.0064950E+01 4.0064950E+01 7.9993120E+01 1.2005810E+02 2.49482E+00
4.0064950E+01 4.0064950E+01 1.2005810E+02 1.5998620E+02 8.69303E-01
4.0064950E+01 4.0064950E+01 1.5998620E+02 2.0005120E+02 4.26875E-01
4.0064950E+01 4.0064950E+01 2.0005120E+02 2.3997940E+02 1.85755E-01
4.0064950E+01 4.0064950E+01 2.3997940E+02 2.8004430E+02 6.22139E-02
4.0064950E+01 4.0064950E+01 2.8004430E+02 3.1997250E+02 8.43972E-02
4.0064950E+01 4.0064950E+01 3.1997250E+02 3.6003740E+02 6.77677E-02
4.0064950E+01 4.0064950E+01 3.6003740E+02 3.9996560E+02 5.10716E-02
4.0064950E+01 4.0064950E+01 3.9996560E+02 4.4003060E+02 3.90883E-02
7.9993120E+01 7.9993120E+01 1.2005810E+02 1.5998620E+02 2.38695E+00
7.9993120E+01 7.9993120E+01 1.5998620E+02 2.0005120E+02 8.16160E-01
7.9993120E+01 7.9993120E+01 2.0005120E+02 2.3997940E+02 3.14719E-01
7.9993120E+01 7.9993120E+01 2.3997940E+02 2.8004430E+02 9.08799E-02
7.9993120E+01 7.9993120E+01 2.8004430E+02 3.1997250E+02 1.11071E-01
7.9993120E+01 7.9993120E+01 3.1997250E+02 3.6003740E+02 8.49045E-02
7.9993120E+01 7.9993120E+01 3.6003740E+02 3.9996560E+02 6.21525E-02
7.9993120E+01 7.9993120E+01 3.9996560E+02 4.4003060E+02 4.65205E-02
7.9993120E+01 7.9993120E+01 4.4003060E+02 4.8000910E+02 3.92378E-02
1.2005810E+02 1.2005810E+02 1.5998620E+02 2.0005120E+02 2.32795E+00
1.2005810E+02 1.2005810E+02 2.0005120E+02 2.3997940E+02 6.90567E-01
1.2005810E+02 1.2005810E+02 2.3997940E+02 2.8004430E+02 1.57475E-01
1.2005810E+02 1.2005810E+02 2.8004430E+02 3.1997250E+02 1.61140E-01
1.2005810E+02 1.2005810E+02 3.1997250E+02 3.6003740E+02 1.12954E-01
1.2005810E+02 1.2005810E+02 3.6003740E+02 3.9996560E+02 7.88071E-02
1.2005810E+02 1.2005810E+02 3.9996560E+02 4.4003060E+02 5.69870E-02
1.2005810E+02 1.2005810E+02 4.4003060E+02 4.8000910E+02 4.68137E-02
1.2005810E+02 1.2005810E+02 4.8000910E+02 5.2002370E+02 4.07846E-02
1.5998620E+02 1.5998620E+02 2.0005120E+02 2.3997940E+02 2.13876E+00
1.5998620E+02 1.5998620E+02 2.3997940E+02 2.8004430E+02 3.38675E-01
1.5998620E+02 1.5998620E+02 2.8004430E+02 3.1997250E+02 2.66375E-01
1.5998620E+02 1.5998620E+02 3.1997250E+02 3.6003740E+02 1.62003E-01
1.5998620E+02 1.5998620E+02 3.6003740E+02 3.9996560E+02 1.04633E-01
1.5998620E+02 1.5998620E+02 3.9996560E+02 4.4003060E+02 7.17551E-02
1.5998620E+02 1.5998620E+02 4.4003060E+02 4.8000910E+02 5.67258E-02
1.5998620E+02 1.5998620E+02 4.8000910E+02 5.2002370E+02 4.83016E-02
2.0005120E+02 2.0005120E+02 2.3997940E+02 2.8004430E+02 1.11044E+00
2.0005120E+02 2.0005120E+02 2.8004430E+02 3.1997250E+02 5.71790E-01
2.0005120E+02 2.0005120E+02 3.1997250E+02 3.6003740E+02 2.72780E-01
2.0005120E+02 2.0005120E+02 3.6003740E+02 3.9996560E+02 1.54175E-01
2.0005120E+02 2.0005120E+02 3.9996560E+02 4.4003060E+02 9.65307E-02
2.0005120E+02 2.0005120E+02 4.4003060E+02 4.8000910E+02 7.15786E-02
2.0005120E+02 2.0005120E+02 4.8000910E+02 5.2002370E+02 5.87830E-02
2.3997940E+02 2.3997940E+02 2.8004430E+02 3.1997250E+02 1.20559E+00
2.3997940E+02 2.3997940E+02 3.1997250E+02 3.6003740E+02 4.28041E-01
2.3997940E+02 2.3997940E+02 3.6003740E+02 3.9996560E+02 2.11670E-01
2.3997940E+02 2.3997940E+02 3.9996560E+02 4.4003060E+02 1.21535E-01
2.3997940E+02 2.3997940E+02 4.4003060E+02 4.8000910E+02 8.48828E-02
2.3997940E+02 2.3997940E+02 4.8000910E+02 5.2002370E+02 6.74711E-02
2.8004430E+02 2.8004430E+02 3.1997250E+02 3.6003740E+02 1.47236E+00
2.8004430E+02 2.8004430E+02 3.6003740E+02 3.9996560E+02 4.58285E-01
2.8004430E+02 2.8004430E+02 3.9996560E+02 4.4003060E+02 2.00605E-01
2.8004430E+02 2.8004430E+02 4.4003060E+02 4.8000910E+02 1.16548E-01
2.8004430E+02 2.8004430E+02 4.8000910E+02 5.2002370E+02 8.41837E-02
3.1997250E+02 3.1997250E+02 3.6003740E+02 3.9996560E+02 1.95167E+00
3.1997250E+02 3.1997250E+02 3.9996560E+02 4.4003060E+02 5.75830E-01
3.1997250E+02 3.1997250E+02 4.4003060E+02 4.8000910E+02 2.44498E-01
3.1997250E+02 3.1997250E+02 4.8000910E+02 5.2002370E+02 1.44424E-01
3.6003740E+02 3.6003740E+02 3.9996560E+02 4.4003060E+02 1.99070E+00
3.6003740E+02 3.6003740E+02 4.4003060E+02 4.8000910E+02 5.72521E-01
3.6003740E+02 3.6003740E+02 4.8000910E+02 5.2002370E+02 2.68902E-01
3.9996560E+02 3.9996560E+02 4.4003060E+02 4.8000910E+02 1.85289E+00
3.9996560E+02 3.9996560E+02 4.8000910E+02 5.2002370E+02 5.95500E-01
4.4003060E+02 4.4003060E+02 4.8000910E+02 5.2002370E+02 1.86500E+00
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
simpegDCIP/Dev/Inv2D/dcinv2d_0.pre
+65 -72
View File
@@ -1,73 +1,66 @@
! Predicted data ! GENERAL FORMAT Last column are apparent conductivities.
0.0000000E+00 0.0000000E+00 4.0064950E+01 7.9993120E+01 2.09049E+00 9.48494E-04
0.0000000E+00 0.0000000E+00 7.9993120E+01 1.2005810E+02 7.01685E-01 9.46235E-04
0.0000000E+00 0.0000000E+00 1.2005810E+02 1.5998620E+02 3.39337E-01 9.74974E-04
0.0000000E+00 0.0000000E+00 1.5998620E+02 2.0005120E+02 2.00185E-01 9.95245E-04
0.0000000E+00 0.0000000E+00 2.0005120E+02 2.3997940E+02 1.31722E-01 1.00491E-03
0.0000000E+00 0.0000000E+00 2.3997940E+02 2.8004430E+02 9.41624E-02 1.00764E-03
0.0000000E+00 0.0000000E+00 2.8004430E+02 3.1997250E+02 7.04260E-02 1.00700E-03
0.0000000E+00 0.0000000E+00 3.1997250E+02 3.6003740E+02 5.50679E-02 1.00514E-03
0.0000000E+00 0.0000000E+00 3.6003740E+02 3.9996560E+02 4.39979E-02 1.00299E-03
4.0064950E+01 4.0064950E+01 7.9993120E+01 1.2005810E+02 2.13036E+00 9.37130E-04
4.0064950E+01 4.0064950E+01 1.2005810E+02 1.5998620E+02 7.06596E-01 9.37516E-04
4.0064950E+01 4.0064950E+01 1.5998620E+02 2.0005120E+02 3.43193E-01 9.68430E-04
4.0064950E+01 4.0064950E+01 2.0005120E+02 2.3997940E+02 2.00671E-01 9.90121E-04
4.0064950E+01 4.0064950E+01 2.3997940E+02 2.8004430E+02 1.32802E-01 1.00083E-03
4.0064950E+01 4.0064950E+01 2.8004430E+02 3.1997250E+02 9.42053E-02 1.00424E-03
4.0064950E+01 4.0064950E+01 3.1997250E+02 3.6003740E+02 7.09089E-02 1.00405E-03
4.0064950E+01 4.0064950E+01 3.6003740E+02 3.9996560E+02 5.50490E-02 1.00243E-03
4.0064950E+01 4.0064950E+01 3.9996560E+02 4.4003060E+02 4.42808E-02 1.00038E-03
7.9993120E+01 7.9993120E+01 1.2005810E+02 1.5998620E+02 2.11970E+00 9.35419E-04
7.9993120E+01 7.9993120E+01 1.5998620E+02 2.0005120E+02 7.09185E-01 9.36229E-04
7.9993120E+01 7.9993120E+01 2.0005120E+02 2.3997940E+02 3.42168E-01 9.66912E-04
7.9993120E+01 7.9993120E+01 2.3997940E+02 2.8004430E+02 2.01531E-01 9.88595E-04
7.9993120E+01 7.9993120E+01 2.8004430E+02 3.1997250E+02 1.32466E-01 9.99265E-04
7.9993120E+01 7.9993120E+01 3.1997250E+02 3.6003740E+02 9.46289E-02 1.00267E-03
7.9993120E+01 7.9993120E+01 3.6003740E+02 3.9996560E+02 7.07483E-02 1.00241E-03
7.9993120E+01 7.9993120E+01 3.9996560E+02 4.4003060E+02 5.53154E-02 1.00064E-03
7.9993120E+01 7.9993120E+01 4.4003060E+02 4.8000910E+02 4.42570E-02 9.98251E-04
1.2005810E+02 1.2005810E+02 1.5998620E+02 2.0005120E+02 2.13320E+00 9.35884E-04
1.2005810E+02 1.2005810E+02 2.0005120E+02 2.3997940E+02 7.07938E-01 9.35741E-04
1.2005810E+02 1.2005810E+02 2.3997940E+02 2.8004430E+02 3.43938E-01 9.66331E-04
1.2005810E+02 1.2005810E+02 2.8004430E+02 3.1997250E+02 2.01136E-01 9.87834E-04
1.2005810E+02 1.2005810E+02 3.1997250E+02 3.6003740E+02 1.33125E-01 9.98404E-04
1.2005810E+02 1.2005810E+02 3.6003740E+02 3.9996560E+02 9.44518E-02 1.00162E-03
1.2005810E+02 1.2005810E+02 3.9996560E+02 4.4003060E+02 7.11187E-02 1.00109E-03
1.2005810E+02 1.2005810E+02 4.4003060E+02 4.8000910E+02 5.53084E-02 9.98848E-04
1.2005810E+02 1.2005810E+02 4.8000910E+02 5.2002370E+02 4.42167E-02 1.00043E-03
1.5998620E+02 1.5998620E+02 2.0005120E+02 2.3997940E+02 2.12045E+00 9.35091E-04
1.5998620E+02 1.5998620E+02 2.3997940E+02 2.8004430E+02 7.09649E-01 9.35613E-04
1.5998620E+02 1.5998620E+02 2.8004430E+02 3.1997250E+02 3.42490E-01 9.66002E-04
1.5998620E+02 1.5998620E+02 3.1997250E+02 3.6003740E+02 2.01778E-01 9.87383E-04
1.5998620E+02 1.5998620E+02 3.6003740E+02 3.9996560E+02 1.32675E-01 9.97692E-04
1.5998620E+02 1.5998620E+02 3.9996560E+02 4.4003060E+02 9.48272E-02 1.00058E-03
1.5998620E+02 1.5998620E+02 4.4003060E+02 4.8000910E+02 7.10353E-02 9.99457E-04
1.5998620E+02 1.5998620E+02 4.8000910E+02 5.2002370E+02 5.51920E-02 1.00146E-03
2.0005120E+02 2.0005120E+02 2.3997940E+02 2.8004430E+02 2.13352E+00 9.35741E-04
2.0005120E+02 2.0005120E+02 2.8004430E+02 3.1997250E+02 7.08185E-01 9.35415E-04
2.0005120E+02 2.0005120E+02 3.1997250E+02 3.6003740E+02 3.44147E-01 9.65742E-04
2.0005120E+02 2.0005120E+02 3.6003740E+02 3.9996560E+02 2.01333E-01 9.86865E-04
2.0005120E+02 2.0005120E+02 3.9996560E+02 4.4003060E+02 1.33335E-01 9.96834E-04
2.0005120E+02 2.0005120E+02 4.4003060E+02 4.8000910E+02 9.48005E-02 9.99009E-04
2.0005120E+02 2.0005120E+02 4.8000910E+02 5.2002370E+02 7.09190E-02 1.00247E-03
2.3997940E+02 2.3997940E+02 2.8004430E+02 3.1997250E+02 2.12067E+00 9.34999E-04
2.3997940E+02 2.3997940E+02 3.1997250E+02 3.6003740E+02 7.09848E-01 9.35352E-04
2.3997940E+02 2.3997940E+02 3.6003740E+02 3.9996560E+02 3.42699E-01 9.65413E-04
2.3997940E+02 2.3997940E+02 3.9996560E+02 4.4003060E+02 2.02026E-01 9.86174E-04
2.3997940E+02 2.3997940E+02 4.4003060E+02 4.8000910E+02 1.33136E-01 9.95279E-04
2.3997940E+02 2.3997940E+02 4.8000910E+02 5.2002370E+02 9.45157E-02 1.00240E-03
2.8004430E+02 2.8004430E+02 3.1997250E+02 3.6003740E+02 2.13373E+00 9.35649E-04
2.8004430E+02 2.8004430E+02 3.6003740E+02 3.9996560E+02 7.08432E-01 9.35089E-04
2.8004430E+02 2.8004430E+02 3.9996560E+02 4.4003060E+02 3.44471E-01 9.64835E-04
2.8004430E+02 2.8004430E+02 4.4003060E+02 4.8000910E+02 2.01999E-01 9.84601E-04
2.8004430E+02 2.8004430E+02 4.8000910E+02 5.2002370E+02 1.32835E-01 9.99076E-04
3.1997250E+02 3.1997250E+02 3.6003740E+02 3.9996560E+02 2.12099E+00 9.34856E-04
3.1997250E+02 3.1997250E+02 3.9996560E+02 4.4003060E+02 7.10315E-01 9.34739E-04
3.1997250E+02 3.1997250E+02 4.4003060E+02 4.8000910E+02 3.43760E-01 9.63341E-04
3.1997250E+02 3.1997250E+02 4.8000910E+02 5.2002370E+02 2.01164E-01 9.88844E-04
3.6003740E+02 3.6003740E+02 3.9996560E+02 4.4003060E+02 2.13448E+00 9.35322E-04
3.6003740E+02 3.6003740E+02 4.4003060E+02 4.8000910E+02 7.10352E-01 9.33343E-04
3.6003740E+02 3.6003740E+02 4.8000910E+02 5.2002370E+02 3.42786E-01 9.67954E-04
3.9996560E+02 3.9996560E+02 4.4003060E+02 4.8000910E+02 2.12513E+00 9.33618E-04
3.9996560E+02 3.9996560E+02 4.8000910E+02 5.2002370E+02 7.06317E-01 9.38257E-04
4.4003060E+02 4.4003060E+02 4.8000910E+02 5.2002370E+02 2.12106E+00 9.38871E-04
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
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simpegDCIP/Dev/Inv2D/sensitivity.txt
+31 -31
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@@ -1,31 +1,31 @@
54 30
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1.58950E-12 4.90315E-12 1.52058E-11 4.81290E-11 1.54892E-10 4.92158E-10 1.48457E-09 4.11495E-09 1.00828E-08 2.10364E-08 3.65723E-08 5.35501E-08 6.86734E-08 8.03668E-08 8.87004E-08 9.53366E-08 1.01530E-07 1.07113E-07 1.11978E-07 1.16213E-07 1.20313E-07 1.24048E-07 1.27529E-07 1.30871E-07 1.33566E-07 1.35817E-07 1.37443E-07 1.37416E-07 1.34589E-07 1.28556E-07 1.20187E-07 1.10027E-07 9.93245E-08 8.89174E-08 8.02069E-08 7.22966E-08 6.48124E-08 5.76182E-08 5.10210E-08 4.45613E-08 3.87486E-08 3.32266E-08 3.13200E-08 2.93243E-08 2.10711E-08 1.14919E-08 4.96240E-09 1.79802E-09 5.81484E-10 1.76586E-10 5.29378E-11 1.63214E-11 5.20298E-12 1.67939E-12
1.58891E-12 4.89928E-12 1.51802E-11 4.79591E-11 1.53824E-10 4.85864E-10 1.45041E-09 3.94853E-09 9.40709E-09 1.89249E-08 3.17015E-08 4.50560E-08 5.66103E-08 6.53893E-08 7.15943E-08 7.65311E-08 8.11474E-08 8.53086E-08 8.89241E-08 9.20735E-08 9.50492E-08 9.77370E-08 1.00190E-07 1.02418E-07 1.04104E-07 1.05384E-07 1.06136E-07 1.05667E-07 1.03183E-07 9.84578E-08 9.20245E-08 8.43071E-08 7.61562E-08 6.81542E-08 6.13110E-08 5.51605E-08 4.94592E-08 4.41531E-08 3.93360E-08 3.47725E-08 3.06746E-08 2.68872E-08 2.57252E-08 2.46582E-08 1.85027E-08 1.05451E-08 4.71506E-09 1.74801E-09 5.72776E-10 1.75197E-10 5.27311E-11 1.62921E-11 5.19876E-12 1.67876E-12
1.58827E-12 4.89514E-12 1.51529E-11 4.77777E-11 1.52687E-10 4.79205E-10 1.41484E-09 3.78017E-09 8.75566E-09 1.70124E-08 2.75437E-08 3.81314E-08 4.70354E-08 5.36955E-08 5.83552E-08 6.20545E-08 6.55010E-08 6.85931E-08 7.12535E-08 7.35414E-08 7.56650E-08 7.75223E-08 7.91739E-08 8.05786E-08 8.15652E-08 8.21870E-08 8.24076E-08 8.17238E-08 7.96184E-08 7.58909E-08 7.09495E-08 6.50352E-08 5.87910E-08 5.26221E-08 4.72515E-08 4.25020E-08 3.81203E-08 3.41978E-08 3.06561E-08 2.73895E-08 2.45082E-08 2.19183E-08 2.12681E-08 2.08152E-08 1.62339E-08 9.65181E-09 4.46862E-09 1.69632E-09 5.63605E-10 1.73721E-10 5.25105E-11 1.62608E-11 5.19424E-12 1.67809E-12
1.58759E-12 4.89072E-12 1.51238E-11 4.75849E-11 1.51482E-10 4.72219E-10 1.37806E-09 3.61136E-09 8.13279E-09 1.52870E-08 2.39926E-08 3.24375E-08 3.93587E-08 4.44468E-08 4.79826E-08 5.07678E-08 5.33363E-08 5.56226E-08 5.75716E-08 5.91987E-08 6.06720E-08 6.19199E-08 6.29631E-08 6.38010E-08 6.42875E-08 6.45039E-08 6.43838E-08 6.36335E-08 6.18485E-08 5.89180E-08 5.50790E-08 5.05282E-08 4.57155E-08 4.09438E-08 3.67433E-08 3.30464E-08 2.96743E-08 2.67556E-08 2.41302E-08 2.17722E-08 1.97437E-08 1.79871E-08 1.77035E-08 1.76343E-08 1.42440E-08 8.81477E-09 4.22478E-09 1.64327E-09 5.54000E-10 1.72159E-10 5.22761E-11 1.62275E-11 5.18942E-12 1.67737E-12
1.58687E-12 4.88603E-12 1.50929E-11 4.73809E-11 1.50213E-10 4.64914E-10 1.34027E-09 3.44323E-09 7.54145E-09 1.37358E-08 2.09492E-08 2.77291E-08 3.31415E-08 3.70599E-08 3.97529E-08 4.18628E-08 4.37786E-08 4.54635E-08 4.68806E-08 4.80110E-08 4.90171E-08 4.98100E-08 5.04292E-08 5.08629E-08 5.10292E-08 5.09892E-08 5.06660E-08 4.99203E-08 4.84220E-08 4.60877E-08 4.30914E-08 3.95527E-08 3.58186E-08 3.21121E-08 2.88166E-08 2.59348E-08 2.33366E-08 2.11365E-08 1.91904E-08 1.74615E-08 1.60319E-08 1.48579E-08 1.48174E-08 1.50010E-08 1.24991E-08 8.03634E-09 3.98560E-09 1.58919E-09 5.43998E-10 1.70516E-10 5.20283E-11 1.61921E-11 5.18431E-12 1.67660E-12
1.58610E-12 4.88107E-12 1.50603E-11 4.71658E-11 1.48880E-10 4.57317E-10 1.30166E-09 3.27695E-09 6.98303E-09 1.23431E-08 1.83405E-08 2.38040E-08 2.80697E-08 3.10989E-08 3.31663E-08 3.47671E-08 3.61961E-08 3.74314E-08 3.84575E-08 3.92180E-08 3.98899E-08 4.03649E-08 4.06799E-08 4.08545E-08 4.08086E-08 4.06209E-08 4.01910E-08 3.94864E-08 3.82121E-08 3.63497E-08 3.39797E-08 3.12109E-08 2.82853E-08 2.53866E-08 2.27959E-08 2.05359E-08 1.85297E-08 1.68564E-08 1.54010E-08 1.41263E-08 1.31165E-08 1.23460E-08 1.24704E-08 1.28067E-08 1.09771E-08 7.31531E-09 3.75232E-09 1.53441E-09 5.33631E-10 1.68794E-10 5.17672E-11 1.61548E-11 5.17890E-12 1.67579E-12
1.58512E-12 4.87472E-12 1.50186E-11 4.68920E-11 1.47194E-10 4.47848E-10 1.25475E-09 3.08292E-09 6.36498E-09 1.08805E-08 1.57098E-08 1.99540E-08 2.31733E-08 2.54133E-08 2.69097E-08 2.80622E-08 2.90589E-08 2.99044E-08 3.05811E-08 3.10414E-08 3.14108E-08 3.16196E-08 3.16985E-08 3.16619E-08 3.14660E-08 3.11744E-08 3.07171E-08 3.00556E-08 2.90340E-08 2.76024E-08 2.58240E-08 2.37484E-08 2.15650E-08 1.93973E-08 1.74427E-08 1.57316E-08 1.42435E-08 1.30235E-08 1.19896E-08 1.10946E-08 1.04280E-08 9.96784E-09 1.02067E-08 1.06612E-08 9.42074E-09 6.53460E-09 3.48450E-09 1.46851E-09 5.20778E-10 1.66623E-10 5.14351E-11 1.61071E-11 5.17198E-12 1.67475E-12
1.58363E-12 4.86502E-12 1.49551E-11 4.64773E-11 1.44663E-10 4.33877E-10 1.18759E-09 2.81853E-09 5.57329E-09 9.11915E-09 1.26972E-08 1.56846E-08 1.78663E-08 1.93341E-08 2.02858E-08 2.09972E-08 2.15994E-08 2.20798E-08 2.24290E-08 2.26273E-08 2.27370E-08 2.27335E-08 2.26204E-08 2.24397E-08 2.21461E-08 2.18095E-08 2.13707E-08 2.08063E-08 2.00501E-08 1.90589E-08 1.78486E-08 1.64501E-08 1.49762E-08 1.35198E-08 1.21885E-08 1.10287E-08 1.00379E-08 9.22945E-09 8.58507E-09 8.03397E-09 7.65762E-09 7.45346E-09 7.76782E-09 8.29188E-09 7.61710E-09 5.56277E-09 3.12692E-09 1.37537E-09 5.01918E-10 1.63370E-10 5.09326E-11 1.60345E-11 5.16142E-12 1.67316E-12
1.58141E-12 4.85070E-12 1.48616E-11 4.58696E-11 1.40996E-10 4.14087E-10 1.09643E-09 2.48216E-09 4.64309E-09 7.20396E-09 9.60426E-09 1.14882E-08 1.27913E-08 1.36300E-08 1.41450E-08 1.45113E-08 1.47982E-08 1.50116E-08 1.51299E-08 1.51487E-08 1.50949E-08 1.49706E-08 1.47783E-08 1.45469E-08 1.42449E-08 1.39198E-08 1.35534E-08 1.31194E-08 1.26012E-08 1.19622E-08 1.12139E-08 1.03604E-08 9.46161E-09 8.57875E-09 7.76550E-09 7.06418E-09 6.46424E-09 6.00014E-09 5.63703E-09 5.34314E-09 5.17020E-09 5.13844E-09 5.46559E-09 5.98487E-09 5.74767E-09 4.46541E-09 2.68469E-09 1.25110E-09 4.75450E-10 1.58676E-10 5.01973E-11 1.59277E-11 5.14581E-12 1.67081E-12
1.57821E-12 4.83005E-12 1.47275E-11 4.50077E-11 1.35884E-10 3.87464E-10 9.80797E-10 2.09079E-09 3.66076E-09 5.34934E-09 6.80005E-09 7.84937E-09 8.52489E-09 8.92596E-09 9.15528E-09 9.29909E-09 9.39372E-09 9.44105E-09 9.43160E-09 9.36547E-09 9.24765E-09 9.09194E-09 8.90132E-09 8.68884E-09 8.44083E-09 8.18171E-09 7.91033E-09 7.61230E-09 7.28447E-09 6.90447E-09 6.47730E-09 6.00148E-09 5.50293E-09 5.01528E-09 4.57004E-09 4.17981E-09 3.85320E-09 3.60655E-09 3.42320E-09 3.29240E-09 3.22949E-09 3.27928E-09 3.56146E-09 4.01201E-09 4.04400E-09 3.36363E-09 2.18874E-09 1.09718E-09 4.40259E-10 1.52171E-10 4.91566E-11 1.57748E-11 5.12334E-12 1.66741E-12
1.57472E-12 4.80753E-12 1.45815E-11 4.40718E-11 1.30402E-10 3.59753E-10 8.67406E-10 1.73954E-09 2.86458E-09 3.97924E-09 4.87029E-09 5.47425E-09 5.83505E-09 6.03122E-09 6.13240E-09 6.18115E-09 6.20219E-09 6.18906E-09 6.14358E-09 6.06078E-09 5.94583E-09 5.80765E-09 5.65055E-09 5.47965E-09 5.29105E-09 5.09863E-09 4.90405E-09 4.69615E-09 4.47374E-09 4.22547E-09 3.95454E-09 3.65804E-09 3.35731E-09 3.06364E-09 2.80519E-09 2.58113E-09 2.39607E-09 2.26585E-09 2.17193E-09 2.11785E-09 2.10552E-09 2.18419E-09 2.41750E-09 2.76815E-09 2.87518E-09 2.52170E-09 1.76182E-09 9.50010E-10 4.04102E-10 1.45229E-10 4.80283E-11 1.56082E-11 5.09883E-12 1.66370E-12
1.57079E-12 4.78239E-12 1.44204E-11 4.30598E-11 1.24668E-10 3.32376E-10 7.64180E-10 1.44923E-09 2.26144E-09 3.00501E-09 3.55480E-09 3.89857E-09 4.08383E-09 4.17161E-09 4.20532E-09 4.21146E-09 4.19776E-09 4.16384E-09 4.10792E-09 4.03021E-09 3.92961E-09 3.81810E-09 3.69412E-09 3.56215E-09 3.42170E-09 3.28062E-09 3.14203E-09 2.99840E-09 2.84766E-09 2.68664E-09 2.51462E-09 2.32952E-09 2.14331E-09 1.96297E-09 1.80420E-09 1.66905E-09 1.55804E-09 1.48285E-09 1.43087E-09 1.40698E-09 1.41266E-09 1.48362E-09 1.66516E-09 1.94105E-09 2.08115E-09 1.91364E-09 1.42255E-09 8.19712E-10 3.68975E-10 1.38035E-10 4.68132E-11 1.54248E-11 5.07148E-12 1.65953E-12
1.56512E-12 4.74630E-12 1.41914E-11 4.16479E-11 1.16931E-10 2.97469E-10 6.42932E-10 1.13906E-09 1.67060E-09 2.11175E-09 2.40641E-09 2.56852E-09 2.64086E-09 2.66279E-09 2.66085E-09 2.64572E-09 2.61925E-09 2.58095E-09 2.53055E-09 2.46776E-09 2.39261E-09 2.31100E-09 2.22374E-09 2.13153E-09 2.03740E-09 1.94323E-09 1.85331E-09 1.76173E-09 1.66792E-09 1.57192E-09 1.47185E-09 1.36560E-09 1.26024E-09 1.15860E-09 1.06969E-09 9.94949E-10 9.34257E-10 8.94176E-10 8.69436E-10 8.60516E-10 8.72528E-10 9.26468E-10 1.05476E-09 1.25373E-09 1.39246E-09 1.34869E-09 1.07545E-09 6.71222E-10 3.24980E-10 1.28415E-10 4.51248E-11 1.51646E-11 5.03225E-12 1.65350E-12
1.55681E-12 4.69387E-12 1.38637E-11 3.96797E-11 1.06634E-10 2.54394E-10 5.07934E-10 8.30009E-10 1.13582E-09 1.36061E-09 1.49056E-09 1.54756E-09 1.56120E-09 1.55401E-09 1.53928E-09 1.51984E-09 1.49455E-09 1.46328E-09 1.42599E-09 1.38268E-09 1.33340E-09 1.28064E-09 1.22573E-09 1.16839E-09 1.11150E-09 1.05585E-09 1.00216E-09 9.49155E-10 8.95782E-10 8.43307E-10 7.89282E-10 7.33418E-10 6.78756E-10 6.26367E-10 5.80748E-10 5.42997E-10 5.12632E-10 4.93142E-10 4.82690E-10 4.80856E-10 4.91764E-10 5.27533E-10 6.08125E-10 7.37192E-10 8.49099E-10 8.68361E-10 7.47390E-10 5.12048E-10 2.71939E-10 1.15768E-10 4.27842E-11 1.47930E-11 4.97529E-12 1.64465E-12
1.54198E-12 4.60231E-12 1.33101E-11 3.65309E-11 9.14734E-11 1.97948E-10 3.53592E-10 5.19026E-10 6.48549E-10 7.24011E-10 7.53911E-10 7.56065E-10 7.45356E-10 7.30734E-10 7.16546E-10 7.01999E-10 6.85328E-10 6.66543E-10 6.45670E-10 6.22741E-10 5.97809E-10 5.70974E-10 5.43962E-10 5.16439E-10 4.89241E-10 4.63009E-10 4.37312E-10 4.13416E-10 3.89754E-10 3.66164E-10 3.43575E-10 3.20832E-10 2.98728E-10 2.77633E-10 2.58924E-10 2.43134E-10 2.29977E-10 2.20977E-10 2.15568E-10 2.14380E-10 2.18525E-10 2.34094E-10 2.70223E-10 3.35634E-10 4.11503E-10 4.56952E-10 4.36708E-10 3.38796E-10 2.04707E-10 9.75106E-11 3.90793E-11 1.41685E-11 4.87601E-12 1.62890E-12
1.51682E-12 4.45056E-12 1.24272E-11 3.18321E-11 7.10997E-11 1.32444E-10 2.02573E-10 2.60524E-10 2.94781E-10 3.07103E-10 3.05505E-10 2.97545E-10 2.87954E-10 2.79017E-10 2.71555E-10 2.64563E-10 2.56994E-10 2.48859E-10 2.40170E-10 2.30943E-10 2.21197E-10 2.10953E-10 2.00234E-10 1.89617E-10 1.79147E-10 1.68756E-10 1.59329E-10 1.49997E-10 1.41065E-10 1.32554E-10 1.24234E-10 1.16239E-10 1.08611E-10 1.01592E-10 9.51326E-11 8.97107E-11 8.49186E-11 8.14026E-11 7.90430E-11 7.79932E-11 7.85645E-11 8.29863E-11 9.42840E-11 1.17709E-10 1.52894E-10 1.85209E-10 1.98905E-10 1.79842E-10 1.29997E-10 7.35852E-11 3.36214E-11 1.31785E-11 4.71179E-12 1.60217E-12
1.47953E-12 4.23476E-12 1.12546E-11 2.62516E-11 5.04084E-11 7.73305E-11 9.73648E-11 1.06996E-10 1.08568E-10 1.05682E-10 1.01100E-10 9.64088E-11 9.23096E-11 8.89998E-11 8.64434E-11 8.41686E-11 8.17976E-11 7.93324E-11 7.67749E-11 7.41273E-11 7.13921E-11 6.85719E-11 6.56694E-11 6.26876E-11 5.96296E-11 5.64987E-11 5.33181E-11 5.01959E-11 4.74121E-11 4.45974E-11 4.18397E-11 3.93409E-11 3.69115E-11 3.45810E-11 3.25368E-11 3.07619E-11 2.92066E-11 2.78540E-11 2.68739E-11 2.61120E-11 2.54808E-11 2.51913E-11 2.58174E-11 2.88799E-11 3.70936E-11 5.21032E-11 6.75237E-11 7.59799E-11 7.01362E-11 5.00759E-11 2.72698E-11 1.18775E-11 4.47913E-12 1.56258E-12
58 30
2.10977E-14 7.70365E-14 2.95454E-13 1.23025E-12 5.52224E-12 2.53163E-11 1.13341E-10 4.85415E-10 1.98978E-09 7.89733E-09 3.09501E-08 1.23908E-07 5.35096E-07 2.61488E-06 1.57838E-06 1.83293E-05 1.78994E-05 2.55180E-05 3.32683E-05 3.53930E-05 3.56559E-05 3.60301E-05 3.74164E-05 4.49111E-05 4.01519E-05 5.29337E-05 3.79236E-05 4.72394E-05 3.24304E-05 2.91766E-05 2.61961E-05 2.51228E-05 2.34666E-05 2.52460E-05 2.72310E-05 2.14296E-05 2.73163E-05 1.42638E-05 1.41188E-05 1.13910E-05 9.03775E-07 2.18755E-06 6.88270E-07 2.87165E-07 1.26066E-07 5.18750E-08 2.01152E-08 7.34055E-09 2.51070E-09 8.00342E-10 2.36628E-10 6.46591E-11 1.64542E-11 3.99666E-12 9.70928E-13 2.47960E-13 6.71103E-14 1.87797E-14
2.10970E-14 7.70311E-14 2.95407E-13 1.22983E-12 5.51869E-12 2.52863E-11 1.13091E-10 4.83391E-10 1.97377E-09 7.77008E-09 2.98808E-08 1.13537E-07 4.14159E-07 1.18205E-06 9.95504E-07 5.38258E-06 6.73897E-06 9.31340E-06 1.08008E-05 1.20705E-05 1.24454E-05 1.36411E-05 1.36980E-05 1.50656E-05 1.27930E-05 1.29219E-05 1.08995E-05 1.30114E-05 1.09341E-05 8.53132E-06 9.37010E-06 1.10933E-05 8.72551E-06 8.21692E-06 8.93280E-06 9.48019E-06 8.84787E-06 6.46738E-06 4.47509E-06 2.85098E-06 5.85579E-07 7.94459E-07 4.21876E-07 2.17770E-07 1.08519E-07 4.79724E-08 1.93161E-08 7.18643E-09 2.48240E-09 7.95427E-10 2.35825E-10 6.45370E-11 1.64369E-11 3.99431E-12 9.70626E-13 2.47923E-13 6.71058E-14 1.87791E-14
2.10957E-14 7.70202E-14 2.95312E-13 1.22900E-12 5.51160E-12 2.52264E-11 1.12591E-10 4.79354E-10 1.94194E-09 7.51975E-09 2.78479E-08 9.64415E-08 2.83649E-07 5.48744E-07 5.66607E-07 1.66944E-06 2.34143E-06 3.41445E-06 3.79353E-06 4.40489E-06 4.46668E-06 5.11103E-06 4.94171E-06 5.36377E-06 4.61637E-06 4.53519E-06 3.83419E-06 4.13110E-06 3.56072E-06 3.21444E-06 3.61837E-06 4.38490E-06 3.66878E-06 3.27062E-06 2.78267E-06 2.95081E-06 2.62207E-06 1.94194E-06 1.24570E-06 7.71945E-07 3.27468E-07 3.17928E-07 2.32099E-07 1.46040E-07 8.44724E-08 4.15497E-08 1.78621E-08 6.89157E-09 2.42705E-09 7.85686E-10 2.34224E-10 6.42933E-11 1.64022E-11 3.98963E-12 9.70022E-13 2.47849E-13 6.70967E-14 1.87780E-14
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2.09841E-14 7.61107E-14 2.87440E-13 1.16024E-12 4.93061E-12 2.04934E-11 7.65807E-11 2.38294E-10 5.80317E-10 1.08131E-09 1.59745E-09 1.99766E-09 2.24088E-09 2.36536E-09 2.42657E-09 2.46149E-09 2.48313E-09 2.50165E-09 2.51265E-09 2.52688E-09 2.53557E-09 2.54676E-09 2.55823E-09 2.56395E-09 2.57319E-09 2.57917E-09 2.59405E-09 2.61113E-09 2.61948E-09 2.62587E-09 2.62757E-09 2.61095E-09 2.58327E-09 2.53831E-09 2.47386E-09 2.39484E-09 2.29957E-09 2.19964E-09 2.09476E-09 1.99569E-09 1.90615E-09 1.82516E-09 1.74947E-09 1.67481E-09 1.57634E-09 1.42359E-09 1.20095E-09 9.11602E-10 5.93166E-10 3.15761E-10 1.34597E-10 4.66087E-11 1.36774E-11 3.60763E-12 9.20071E-13 2.41674E-13 6.63368E-14 1.86806E-14
2.09688E-14 7.59868E-14 2.86373E-13 1.15101E-12 4.85443E-12 1.99115E-11 7.27371E-11 2.18624E-10 5.09583E-10 9.09958E-10 1.30237E-09 1.59561E-09 1.76889E-09 1.85588E-09 1.89686E-09 1.91857E-09 1.93418E-09 1.94316E-09 1.95072E-09 1.95499E-09 1.95631E-09 1.95992E-09 1.96351E-09 1.96673E-09 1.97030E-09 1.97493E-09 1.97614E-09 1.97954E-09 1.98295E-09 1.98093E-09 1.97369E-09 1.96384E-09 1.94245E-09 1.91252E-09 1.87136E-09 1.81851E-09 1.75836E-09 1.69596E-09 1.63166E-09 1.56999E-09 1.51091E-09 1.45585E-09 1.40269E-09 1.34892E-09 1.27627E-09 1.16272E-09 9.94666E-10 7.71100E-10 5.16629E-10 2.84817E-10 1.25564E-10 4.46484E-11 1.33392E-11 3.55753E-12 9.13368E-13 2.40838E-13 6.62332E-14 1.86673E-14
2.09454E-14 7.57979E-14 2.84752E-13 1.13702E-12 4.74022E-12 1.90587E-11 6.73364E-11 1.92690E-10 4.23358E-10 7.15851E-10 9.84416E-10 1.17543E-09 1.28433E-09 1.33740E-09 1.36072E-09 1.37366E-09 1.38170E-09 1.38603E-09 1.38824E-09 1.38961E-09 1.38934E-09 1.38636E-09 1.38592E-09 1.38500E-09 1.38422E-09 1.38242E-09 1.38140E-09 1.37838E-09 1.37342E-09 1.36883E-09 1.36338E-09 1.35244E-09 1.33835E-09 1.32041E-09 1.29731E-09 1.26960E-09 1.23719E-09 1.20568E-09 1.17122E-09 1.13738E-09 1.10308E-09 1.07097E-09 1.03826E-09 1.00449E-09 9.56728E-10 8.81341E-10 7.67589E-10 6.11202E-10 4.25153E-10 2.45424E-10 1.13325E-10 4.18564E-11 1.28411E-11 3.48232E-12 9.03216E-13 2.39566E-13 6.60753E-14 1.86469E-14
2.09108E-14 7.55189E-14 2.82365E-13 1.11653E-12 4.57510E-12 1.78636E-11 6.01919E-11 1.61199E-10 3.28766E-10 5.21342E-10 6.85319E-10 7.95334E-10 8.55596E-10 8.83604E-10 8.96336E-10 9.01100E-10 9.05089E-10 9.06635E-10 9.05490E-10 9.04206E-10 9.02672E-10 9.00632E-10 8.97347E-10 8.94391E-10 8.91395E-10 8.87725E-10 8.84037E-10 8.80037E-10 8.76644E-10 8.72247E-10 8.67593E-10 8.61408E-10 8.53243E-10 8.43119E-10 8.31780E-10 8.19141E-10 8.05053E-10 7.90589E-10 7.74745E-10 7.58303E-10 7.41834E-10 7.25440E-10 7.07958E-10 6.88699E-10 6.60803E-10 6.16357E-10 5.47714E-10 4.49756E-10 3.26890E-10 1.99635E-10 9.78925E-11 3.80917E-11 1.21379E-11 3.37340E-12 8.88339E-13 2.37693E-13 6.58420E-14 1.86167E-14
2.08609E-14 7.51178E-14 2.78952E-13 1.08751E-12 4.34618E-12 1.62833E-11 5.14836E-11 1.26883E-10 2.37483E-10 3.51415E-10 4.40529E-10 4.96560E-10 5.26133E-10 5.39834E-10 5.45471E-10 5.48055E-10 5.48928E-10 5.49105E-10 5.48609E-10 5.47664E-10 5.45653E-10 5.43620E-10 5.41185E-10 5.38281E-10 5.35527E-10 5.32721E-10 5.30023E-10 5.27420E-10 5.24486E-10 5.21435E-10 5.18074E-10 5.14296E-10 5.09988E-10 5.05242E-10 5.00290E-10 4.94940E-10 4.89122E-10 4.82913E-10 4.76207E-10 4.69507E-10 4.62366E-10 4.54849E-10 4.46247E-10 4.36230E-10 4.21774E-10 3.98482E-10 3.61719E-10 3.06960E-10 2.34017E-10 1.52243E-10 8.02186E-11 3.33742E-11 1.11971E-11 3.22187E-12 8.67248E-13 2.35014E-13 6.55066E-14 1.85732E-14
2.08063E-14 7.46799E-14 2.75226E-13 1.05588E-12 4.10047E-12 1.46624E-11 4.33038E-11 9.84238E-11 1.71345E-10 2.41225E-10 2.93085E-10 3.23866E-10 3.39113E-10 3.45566E-10 3.47854E-10 3.48259E-10 3.47667E-10 3.46488E-10 3.44663E-10 3.42661E-10 3.40400E-10 3.37886E-10 3.35655E-10 3.33321E-10 3.31283E-10 3.29417E-10 3.27625E-10 3.25927E-10 3.24332E-10 3.22834E-10 3.21587E-10 3.20352E-10 3.19045E-10 3.17572E-10 3.15846E-10 3.14096E-10 3.12169E-10 3.10226E-10 3.07899E-10 3.05414E-10 3.02345E-10 2.98473E-10 2.93792E-10 2.88282E-10 2.80223E-10 2.67035E-10 2.45746E-10 2.13114E-10 1.68043E-10 1.14967E-10 6.46730E-11 2.87978E-11 1.02204E-11 3.05890E-12 8.44285E-13 2.32092E-13 6.51403E-14 1.85256E-14
2.07451E-14 7.41930E-14 2.71139E-13 1.02197E-12 3.84833E-12 1.31203E-11 3.63004E-11 7.67401E-11 1.25536E-10 1.69024E-10 1.99343E-10 2.16472E-10 2.24677E-10 2.28134E-10 2.29380E-10 2.29685E-10 2.29480E-10 2.28829E-10 2.27802E-10 2.26478E-10 2.25099E-10 2.23580E-10 2.22175E-10 2.20795E-10 2.19419E-10 2.18098E-10 2.16860E-10 2.15730E-10 2.14711E-10 2.13800E-10 2.12975E-10 2.12211E-10 2.11462E-10 2.10688E-10 2.09896E-10 2.09110E-10 2.08335E-10 2.07441E-10 2.06265E-10 2.04766E-10 2.02887E-10 2.00612E-10 1.97899E-10 1.94757E-10 1.90170E-10 1.82631E-10 1.70277E-10 1.50783E-10 1.22675E-10 8.77021E-11 5.22193E-11 2.47369E-11 9.27205E-12 2.89017E-12 8.19581E-13 2.28881E-13 6.47328E-14 1.84721E-14
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
simpegDCIP/Dev/gen_DCIPsurvey.py
+54 -24
View File
@@ -1,7 +1,6 @@
def gen_DCIPsurvey(endl, mesh, stype, a, n):
def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
from SimPEG import np
import pylab as plt
import re
"""
Load in endpoints and survey specifications to generate Tx, Rx location
@@ -24,14 +23,17 @@ def gen_DCIPsurvey(endl, mesh, stype, a, n):
@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 = np.sqrt( np.sum((endl[1,:] - endl[0,:])**2) )
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
azm = np.arctan(dl_y/dl_x)
nstn = np.floor( dl_len / a )
# Compute discrete pole location along line
@@ -50,22 +52,51 @@ def gen_DCIPsurvey(endl, mesh, stype, a, n):
Tx = []
Rx = []
if re.match(stype,'pdp'):
if not re.match(stype,'gradient'):
for ii in range(0, int(nstn)-1):
indx = np.min([ii+n,nstn])
Tx.append(np.c_[M[ii,:],M[ii,:]])
Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
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,:]])
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'):
@@ -74,11 +105,11 @@ def gen_DCIPsurvey(endl, mesh, stype, a, n):
Tx.append(np.c_[M[0,:],N[-1,:]])
# Get the edge limit of survey area
min_x = endl[0,0] + dl_x * 300.
min_y = endl[0,1] + dl_y * 300.
min_x = endl[0,0] + dl_x * b
min_y = endl[0,1] + dl_y * b
max_x = endl[1,0] - dl_x * 300.
max_y = endl[1,1] - dl_y * 300.
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.
@@ -102,7 +133,6 @@ def gen_DCIPsurvey(endl, mesh, stype, a, n):
lxx = stn_x - lind[ii]*a*dl_y
lyy = stn_y + lind[ii]*a*dl_x
indx = ii*nlin*lind
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]]
@@ -112,7 +142,7 @@ def gen_DCIPsurvey(endl, mesh, stype, a, n):
Rx.append(rx)
else:
print """stype must be either 'pdp' or 'dpdp'. """
print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
simpegDCIP/Dev/plot_pseudoSection.py
+5 -1
View File
@@ -2,6 +2,7 @@ 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
"""
@@ -57,7 +58,10 @@ def plot_pseudoSection(Tx,Rx,data,z0, stype):
#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)
#plt.colorbar()
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)))