mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 22:08:38 +08:00
rowanc1
91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
import Utils, Survey, Problem, numpy as np, scipy.sparse as sp, gc
|
|
from Utils.SolverUtils import *
|
|
from DataMisfit import _splitForward
|
|
|
|
class BaseInvProblem(object):
|
|
"""BaseInvProblem(forward, reg, **kwargs)"""
|
|
|
|
__metaclass__ = Utils.SimPEGMetaClass
|
|
|
|
beta = 1.0 #: Trade-off parameter
|
|
|
|
debug = False #: Print debugging information
|
|
counter = None #: Set this to a SimPEG.Utils.Counter() if you want to count things
|
|
|
|
reg = None #: Regularization
|
|
dmisfit = None #: DataMisfit
|
|
opt = None #: Optimization program
|
|
|
|
u_current = None #: The most current evaluated field
|
|
m_current = None #: The most current model
|
|
|
|
|
|
def __init__(self, forward, reg, dmisfit, opt, **kwargs):
|
|
Utils.setKwargs(self, **kwargs)
|
|
self.prob, self.survey = _splitForward(forward)
|
|
self.reg = reg
|
|
self.dmisfit = dmisfit
|
|
self.opt = opt
|
|
|
|
@Utils.callHooks('startup')
|
|
def startup(self, m0):
|
|
"""startup(m0)
|
|
|
|
Called when inversion is first starting.
|
|
"""
|
|
if self.debug: print 'Calling InvProblem.startup'
|
|
|
|
if self.reg.mref is None:
|
|
print 'Regularization has not set mref. SimPEG.InvProblem will set it to m0.'
|
|
self.reg.mref = m0
|
|
|
|
self.phi_d = np.nan
|
|
self.phi_m = np.nan
|
|
|
|
self.m_current = m0
|
|
|
|
print 'Setting bfgsH0 to the inverse of the modelObj2Deriv. Done using direct methods.'
|
|
self.opt.bfgsH0 = Solver(self.reg.modelObj2Deriv(self.m_current))
|
|
|
|
@Utils.timeIt
|
|
def evalFunction(self, m, return_g=True, return_H=True):
|
|
"""evalFunction(m, return_g=True, return_H=True)
|
|
"""
|
|
|
|
self.u_current = None
|
|
self.m_current = m
|
|
forward = self.prob
|
|
gc.collect()
|
|
|
|
u = self.prob.fields(m)
|
|
self.u_current = u
|
|
|
|
phi_d = self.dmisfit.dataObj(forward, m, u=u)
|
|
phi_m = self.reg.modelObj(m)
|
|
|
|
self.dpred = self.survey.dpred(m, u=u) # This is a cheap matrix vector calculation.
|
|
|
|
self.phi_d, self.phi_d_last = phi_d, self.phi_d
|
|
self.phi_m, self.phi_m_last = phi_m, self.phi_m
|
|
|
|
f = phi_d + self.beta * phi_m
|
|
|
|
out = (f,)
|
|
if return_g:
|
|
phi_dDeriv = self.dmisfit.dataObjDeriv(forward, m, u=u)
|
|
phi_mDeriv = self.reg.modelObjDeriv(m)
|
|
|
|
g = phi_dDeriv + self.beta * phi_mDeriv
|
|
out += (g,)
|
|
|
|
if return_H:
|
|
def H_fun(v):
|
|
phi_d2Deriv = self.dmisfit.dataObj2Deriv(forward, m, v, u=u)
|
|
phi_m2Deriv = self.reg.modelObj2Deriv(m, v=v)
|
|
|
|
return phi_d2Deriv + self.beta * phi_m2Deriv
|
|
|
|
H = sp.linalg.LinearOperator( (m.size, m.size), H_fun, dtype=m.dtype )
|
|
out += (H,)
|
|
return out if len(out) > 1 else out[0]
|