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https://github.com/wassname/simpeg.git
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Gudni Karl Rosenkjaer
86 lines
4.0 KiB
Python
86 lines
4.0 KiB
Python
# Test script to use simpegMT platform to forward model synthetic data.
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# Import
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import simpegMT as simpegmt, SimPEG as simpeg
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import numpy as np
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# Make a mesh
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M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)],[(100,5,1.6),(100.,10),(100,3,2)]], x0=['C','C',-3529.5360])
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# Setup the model
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conds = [1e-2,1]
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sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-1000,-1000,-400],[1000,1000,-200],conds)
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sig[M.gridCC[:,2]>0] = 1e-8
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sig[M.gridCC[:,2]<-600] = 1e-1
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sigBG = np.zeros(M.nC) + conds[0]
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sigBG[M.gridCC[:,2]>0] = 1e-8
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## Setup the the survey object
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# Receiver locations
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rx_x, rx_y = np.meshgrid(np.arange(-500,501,50),np.arange(-500,501,50))
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rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),np.zeros((np.prod(rx_x.shape),1))))
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# Make a receiver list
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rxList = []
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for loc in rx_loc:
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# NOTE: loc has to be a (1,3) np.ndarray otherwise errors accure
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for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']:
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rxList.append(simpegmt.SurveyMT.RxMT(simpeg.mkvc(loc,2).T,rxType))
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# Source list
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srcList =[]
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for freq in np.logspace(3,-1,5):
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srcList.append(simpegmt.SurveyMT.srcMT(freq,rxList))
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# Survey MT
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survey = simpegmt.SurveyMT.SurveyMT(srcList)
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## Setup the problem object
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problem = simpegmt.ProblemMT.MTProblem(M)
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problem.pair(survey)
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problem.fields(sig,sigBG)
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mtData = survey.projectFields(fields)
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def torecarray(MTdata,returnType='RealImag'):
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'''
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Function that returns a numpy.recarray for a SimpegMT data object.
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'''
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def rec2ndarr(x,dt=float):
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return x.view((dt, len(x.dtype.names)))
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# Define the record fields
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dtRI = [('freq',float),('x',float),('y',float),('z',float),('zxxr',float),('zxxi',float),('zxyr',float),('zxyi',float),('zyxr',float),('zyxi',float),('zyyr',float),('zyyi',float)]
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dtCP = [('freq',float),('x',float),('y',float),('z',float),('zxx',complex),('zxy',complex),('zyx',complex),('zyy',complex)]
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impList = ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']
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for src in MTdata.survey.srcList:
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# Temp array for all the receivers of the source.
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tArrRec = np.array([(src.freq,rx.locs[0,0],rx.locs[0,1],rx.locs[0,2],np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ) for rx in src.rxList],dtype=dtRI)
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# Get the type and the value for the mtdata object as a list
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typeList = [[rx.rxType,MTdata[src,rx][0]] for rx in src.rxList]
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# Insert the values to the temp array
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for nr,(key,val) in enumerate(typeList):
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tArrRec[key][nr] = val
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# Masked array
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mArrRec = np.ma.MaskedArray(rec2ndarr(tArrRec),mask=np.isnan(rec2ndarr(tArrRec))).view(dtype=tArrRec.dtype)
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# Unique freq and loc of the masked array
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uniFLmarr = np.unique(mArrRec[['freq','x','y','z']])
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if 'RealImag' in returnType:
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dt = dtRI
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for uniFL in uniFLmarr:
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mTemp = rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0)
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try:
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outArr = np.concatenate((outArr,simpeg.mkvc(np.concatenate((rec2ndarr(uniFL),mTemp.data)),2).T),axis=0)
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except NameError as e:
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outArr = simpeg.mkvc(np.concatenate((rec2ndarr(uniFL),mTemp.data)),2).T
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elif 'Complex' in returnType:
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# Add the real and imaginary to a complex number
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dt = dtCP
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for uniFL in uniFLmarr:
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mTemp = simpeg.mkvc(rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0),2).T
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dataBlock = np.sum(mTemp.data.reshape((mTemp.shape[0],4,2))*np.array([[[1,1j],[1,1j],[1,1j],[1,1j]]]),axis=2)
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try:
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outArr = np.concatenate((outArr,simpeg.mkvc(np.concatenate((rec2ndarr(uniFL),dataBlock)),2).T),axis=0)
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except NameError as e:
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outArr = simpeg.mkvc(np.concatenate((rec2ndarr(uniFL),dataBlock)),2).T
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# Return
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return outArr.view(dt)
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