void HarmonicModelling::createJacobian( const RVector & model ) {
//!! jacobian = transpose( A );
RMatrix * jacobian = dynamic_cast < RMatrix * > ( jacobian_ );
if ( jacobian->rows() != nt_ || jacobian->cols() != np_ ) {
jacobian->resize( nt_, np_ );
for ( size_t i = 0 ; i < np_ ; i++ ){
for ( size_t j = 0 ; j < nt_ ; j++ ){
(*jacobian)[ j ][ i ] = A_[ i ][ j ];
}
}
}
}
int main( int argc, char *argv [] )
{
std::string dataFile( NOT_DEFINED );
double errPerc = 3.0, lambda = 20.0, lambdaIP = 1.0, maxDepth = 0.0;
int nlay = 30, maxIter = 20;
bool verbose = false, lambdaOpt = false, isBlocky = false, isRobust = false, optimizeChi1 = false;
OptionMap oMap;
oMap.setDescription("DC1dsmooth - smooth 1d dc resistivity inversion");
oMap.add( verbose, "v" , "verbose" , "Verbose output." );
oMap.add( lambdaOpt, "O" , "OptimizeLambda", "Optimize model smoothness using L-curve." );
oMap.add( isRobust, "R" , "RobustData" , "Robust (L1) data weighting." );
oMap.add( isBlocky, "B" , "BlockyModel" , "Blocky (L1) model constraints." );
oMap.add( optimizeChi1, "C" , "OptimizeChi1" , "Optimize lambda subject to chi^2=1." );
oMap.add( maxIter, "i:", "maxIter" , "Maximum iteration number." );
oMap.add( lambda, "l:", "lambda" , "Regularization strength lambda." );
oMap.add( lambdaIP, "r:", "lambdaIP" , "Regularization strength lambda for IP." );
oMap.add( errPerc, "e:", "error" , "Error percentage" );
oMap.add( nlay, "n:", "nLay" , "Number of layers" );
oMap.add( maxDepth, "d:", "maxDepth" , "Maximum depth" );
oMap.addLastArg( dataFile, "Datafile" );
oMap.parse( argc, argv );
/*! Data: read data file from column file */
RMatrix abmnr;
loadMatrixCol( abmnr, dataFile ); //! read data
RVector ab2( abmnr[ 0 ] ); //! first column
RVector mn2( abmnr[ 1 ] ); //! second column
RVector rhoa( abmnr[ 2 ] ); //! third column
/*! Define discretization according to AB/2 */
if ( maxDepth == 0.0 ) {
maxDepth = max( ab2 ) / 2; //! rule of thumb
std::cout << "Maximum depth estimated to " << maxDepth << std::endl;
}
RVector thk( nlay - 1, maxDepth / ( nlay - 1 ) );
thk *= ( maxDepth / sum( thk ) );
RVector model( nlay, median( rhoa ) );
/*! Transformations: log for app. resisivity and thickness, logLU for resistivity */
// TransLogLU< RVector > transRho( lbound, ubound );
TransLog< RVector > transRho;
TransLog< RVector > transRhoa;
/*! Forward operator, transformations and constraints */
DC1dRhoModelling f( thk, ab2, mn2, false );
f.region( 0 )->setTransModel( transRho );
/*! Error estimation */
double current = 0.1, errVolt = 0;//1e-4;
RVector voltage( rhoa * f( RVector( 3, 1.0 ) ) * current );
RVector error = errVolt / voltage + errPerc / 100.0;
vcout << "error min/max = " << min( error ) << "/" << max( error ) << std::endl;
/*! Set up inversion with full matrix, data and forward operator */
RInversion inv( rhoa, f, verbose );
inv.setTransData( transRhoa );
inv.setRelativeError( error );
inv.setLambda( lambda );
inv.setModel( model );
inv.setBlockyModel( isBlocky );
inv.setRobustData( isRobust );
inv.setOptimizeLambda( lambdaOpt );
inv.setMaxIter( maxIter );
inv.setDeltaPhiAbortPercent( 0.5 );
inv.stopAtChi1( false );
if ( optimizeChi1 ) {
model = inv.runChi1( 0.1 );
} else {
model = inv.run();
}
if ( verbose ) std::cout << "model = " << model << std::endl;
save( model, "resistivity.vec" );
save( thk, "thickness.vec" );
save( inv.response(), "response.vec" );
if ( abmnr.cols() > 3 ) {
if ( verbose ) std::cout << "Found ip values, doing ip inversion" << std::endl;
//! imaginary apparent resistivity
RVector rhoai( rhoa * sin( abmnr[ 3 ] / 1000 ) );
//! linear modelling operator using the amplitude jacobian
Mesh mesh( createMesh1D( thk.size() ) );
LinearModelling fIP( mesh, f.jacobian(), verbose );
fIP.region( 0 )->setTransModel( transRho );
//! IP (imaginary resistivity) inversion using fIP
RInversion invIP( rhoai, fIP, verbose );
invIP.setAbsoluteError( rhoai / abmnr[ 3 ] * 1.0 );
invIP.setBlockyModel( isBlocky );
invIP.setRobustData( isRobust );
invIP.setLambda( lambdaIP );
invIP.setRecalcJacobian( false );
invIP.stopAtChi1( false );
RVector ipModel( nlay, median( rhoai ) );
invIP.setModel( ipModel );
ipModel = invIP.run();
RVector phase = atan( ipModel / model ) * 1000.;
save( phase, "phase.vec" );
RVector aphase = atan( invIP.response() / rhoa ) * 1000.;
save( aphase, "aphase.vec" );
}
return EXIT_SUCCESS;
}
int main( int argc, char *argv [] ){
bool lambdaOpt = false, isRobust = false, isBlocky = false, doResolution = false;
bool useAppPar = false, useTan = false, useWater = false, optimizeChi1 = false;
double lambda = 1, lbound = 0, ubound = 0, errbabs = 20;
int maxIter = 10, verboseCount = 0, ctype = 0;
std::string matFile( "A.mat" ), bFile( "b.vec" );
OptionMap oMap;
oMap.setDescription("Description. InvLinearMat - Linear Inversion with given matrix and vector\n");
oMap.addLastArg( bFile, "Data file" );
oMap.add( verboseCount, "v" , "verbose", "Verbose/debug/dosave mode (multiple use)." );
oMap.add( lambdaOpt, "O" , "OptimizeLambda", "Optimize model smoothness using L-curve." );
oMap.add( optimizeChi1, "C" , "OptimizeChi1", "Optimize lambda subject to chi^2=1." );
oMap.add( doResolution, "D" , "doResolution", "Do resolution analysis." );
oMap.add( isRobust, "R" , "RobustData", "Robust (L1) data weighting." );
oMap.add( isBlocky, "B" , "BlockyModel", "Blocky (L1) model constraints." );
oMap.add( useTan, "T" , "useTan", "Use (co-)Tan instead of Log for LU." );
oMap.add( useAppPar, "A" , "useAppPar", "Use apparent parameter transformation." );
oMap.add( useWater, "W" , "useWater", "Use water content transformation." );
oMap.add( matFile, "m:", "matFile", "Matrix file [A.mat]" );
oMap.add( lambda, "l:", "lambda", "Regularization strength lambda[100]." );
oMap.add( lbound, "b:", "lbound", "Lower parameter bound[0]" );
oMap.add( ubound, "u:", "ubound", "Upper parameter bound[0-inactive]" );
oMap.add( errbabs, "e:", "error", "Absolute error level" );
oMap.add( maxIter, "i:", "maxIter", "Maximum Iteration number" );
oMap.parse( argc, argv );
bool verbose = ( verboseCount > 0 ), debug = ( verboseCount > 1 ), dosave = ( verboseCount > 2 );
RMatrix A;
if ( ! loadMatrixSingleBin( A, matFile ) ) { std::cerr << "Did not find A.mat!" << std::endl; return EXIT_OPEN_FILE; }
RVector b; load( b, bFile );
size_t nModel( A.cols() );
dcout << "size(A) = " << A.rows() << "x" << nModel << "size(b) = " << b.size() << std::endl;
RVector Asum( A * RVector( nModel, 1.0 ) );
RVector xapp( b / Asum );
DEBUG save( xapp, "xapp.vec" );
dcout << "apparent x: min/max = " << min( xapp ) << "/" << max( xapp ) << std::endl;
Mesh mesh( createMesh1D( nModel ) );
DEBUG mesh.save( "mesh1d.bms" );
mesh.showInfos();
for ( size_t i = 0; i < mesh.cellCount(); i ++ ) mesh.cell( i ).setAttribute( 2.0 + i );
mesh.createNeighbourInfos();
LinearModelling f( mesh, A, verbose );
f.regionManager().region( 0 )->setConstraintType( ctype );
Trans < RVector > * transData;
Trans < RVector > * transModel;
if ( useAppPar ) {
if ( useTan ) {
if ( ubound <= lbound ) ubound = lbound + 1.0;
transData = new TransNest< RVector >( *new TransCotLU< RVector >( lbound, ubound ),
*new TransLinear< RVector >( RVector( xapp / b ) ) );
transModel = new TransCotLU< RVector >( lbound, ubound );
} else {
transData = new TransNest< RVector >( *new TransLogLU< RVector >( lbound, ubound ),
*new TransLinear< RVector >( RVector( xapp / b ) ) );
transModel = new TransLogLU< RVector >( lbound, ubound );
}
} else {
transData = new Trans< RVector >( );
transModel = new Trans< RVector >( );
}
/*! set up inversion */
RInversion inv( b, f, *transData, *transModel, verbose, dosave );
inv.setRecalcJacobian( false ); //! no need since it is linear
inv.setLambda( lambda );
inv.setOptimizeLambda( lambdaOpt );
inv.setRobustData( isRobust );
inv.setBlockyModel( isBlocky );
inv.setMaxIter( maxIter );
inv.setDeltaPhiAbortPercent( 1.0 );
RVector error( errbabs / b);
vcout << "error: min/max = " << min( error ) << "/" << max( error ) << std::endl;
inv.setError( error );
RVector x( nModel, mean( xapp ) );
inv.setModel( x );
inv.setReferenceModel( x );
if ( optimizeChi1 ) { x = inv.runChi1(); } else { x = inv.run(); }
vcout << "x = " << x << std::endl;
save( x, "x.vec" );
if ( doResolution ) {
RVector resolution( nModel );
RVector resMDiag ( nModel );
RMatrix resM;
for ( size_t iModel = 0; iModel < nModel; iModel++ ) {
resolution = inv.modelCellResolution( iModel );
resM.push_back( resolution );
resMDiag[ iModel ] = resolution[ iModel ];
}
save( resMDiag, "resMDiag.vec" );
save( resM, "resolution.matrix" );
}
delete transData;
delete transModel;
return EXIT_SUCCESS;
}
int main( int argc, char *argv [] )
{
bool lambdaOpt = false, doResolution = false, useTan = false, optimizeChi1 = false;
double lambda = 10.0, lambdaIP = 1.0, lbound = 0.0, ubound = 0.0, errPerc = 3.0;
int maxIter = 10, nlay = 3, verboseCount = 0, linCount = 0;
std::string modelFile( NOT_DEFINED ), dataFileName;
OptionMap oMap;
oMap.setDescription("Description. DC1dInv - 1D block inversion of dc resistivity data\n");
oMap.addLastArg( dataFileName, "Data file" );
oMap.add( verboseCount, "v" , "verbose", "Verbose/debug/dosave mode (multiple use)." );
oMap.add( lambdaOpt, "O" , "OptimizeLambda", "Optimize model smoothness using L-curve." );
oMap.add( doResolution, "D" , "doResolution", "Do resolution analysis." );
oMap.add( optimizeChi1, "C" , "OptimizeChi1", "Optimize lambda subject to chi^2=1." );
oMap.add( useTan, "T" , "useTan", "Use (co-)Tan instead of Log for LU." );
oMap.add( linCount, "L" , "dataLin", "Use linear trafo for data, 2x also for model." );
oMap.add( maxIter, "i:", "maxIter", "Maximum iteration number." );
oMap.add( lambda, "l:", "lambda", "Regularization strength lambda." );
oMap.add( lambdaIP, "r:", "lambdaIP", "Regularization strength lambda for IP." );
oMap.add( errPerc, "e:", "error", "Error percentage" );
oMap.add( nlay, "n:", "nlay", "Number of layers" );
oMap.add( lbound, "b:", "lbound", "Lower Resistivity bound" );
oMap.add( ubound, "u:", "ubound", "Upper Resistivity bound" );
oMap.add( modelFile, "m:", "modelFile", "Model file for pure modelling. file: thick_i rho_i\\n" );
oMap.parse( argc, argv );
bool verbose = ( verboseCount > 0 ), debug = ( verboseCount > 1 ), dosave = ( verboseCount > 2 );
bool dataLin = ( linCount > 0 ), modelLin = ( linCount > 1 );
/*! Data: read data file from column file */
RMatrix abmnr;
loadMatrixCol( abmnr, dataFileName ); //! read data
RVector ab2( abmnr[ 0 ] ); //! first column
RVector mn2( abmnr[ 1 ] ); //! second column
RVector rhoa( abmnr[ 2 ] ); //! third column
/*! Define discretization according to AB/2 */
double maxDep = max( ab2 ) / 2; //! rule of thumb
std::cout << "Maximum depth estimated to " << maxDep << std::endl;
DC1dModelling f( nlay, ab2, mn2, debug );
/*! Error estimation */
double current = 0.1, errVolt = 0;//1e-4;
RVector voltage( rhoa * f( RVector( 3, 1.0 ) ) * current );
RVector error = errVolt / voltage + errPerc / 100.0;
vcout << "error min/max = " << min( error ) << "/" << max( error ) << std::endl;
/*! Transformations: log for app. resisivity and thickness, logLU for resistivity */
// TransLog< RVector > transThk;
// TransLogLU< RVector > transRho( lbound, ubound );
// TransLog< RVector > transRhoa;
Trans< RVector > *transThk, *transRho, *transRhoa;
if ( modelLin ) {
transThk = new Trans< RVector >;
transRho = new Trans< RVector >;
} else {
transThk = new TransLog< RVector >;
if ( useTan ) {
transRho = new TransCotLU< RVector >( lbound, ubound );
} else {
transRho = new TransLogLU< RVector >( lbound, ubound );
}
}
if ( dataLin ) {
transRhoa = new Trans< RVector >;
} else {
transRhoa = new TransLog< RVector >;
}
f.region( 0 )->setTransModel( *transThk );
f.region( 1 )->setTransModel( *transRho );
double paraDepth = max( ab2 ) / 3;
vcout << "Paradepth = " << paraDepth << std::endl;
f.region( 0 )->setStartValue( paraDepth / nlay / 2.0 );
f.region( 1 )->setStartValue( median( rhoa ) );
RVector model;//( f.createDefaultStartModel() );
model = f.createStartVector();
// model[ nlay ] *= 1.5; //! in order to make thicknesses sensitive
DEBUG save( model, "start.vec" );
/*! Set up inversion with full matrix, data and forward operator */
RInversion inv( rhoa, f, verbose, dosave );
inv.setTransData( *transRhoa );
inv.setMarquardtScheme( 0.8 ); //! 0th order local decreasing regularization
inv.setLambda( lambda );
inv.setOptimizeLambda( lambdaOpt );
inv.setMaxIter( maxIter );
inv.setRelativeError( error ); //! error model
inv.setModel( model ); //! starting model
inv.setDeltaPhiAbortPercent( 0.5 );
/*! actual computation: run the inversion */
if ( optimizeChi1 ) {
model = inv.runChi1( 0.1 );
} else {
model = inv.run();
}
RVector thk( nlay - 1 );
RVector res( nlay );
for ( int i = 0 ; i < nlay - 1 ; i++ ) thk[ i ] = model[ i ];
for ( int i = 0 ; i < nlay ; i++ ) res[ i ] = model[ nlay - 1 + i ];
save( res, "resistivity.vec" );
save( thk, "thickness.vec" );
save( inv.response(), "response.vec" );
if ( verbose ) {
RVector cumz( thk );
for ( size_t i = 1 ; i < thk.size() ; i++ ) cumz[i] = cumz[ i-1 ] + thk[i];
cumz.round(0.1);
res.round(0.1);
std::cout << "Res = " << res << std::endl;
std::cout << " z = " << cumz << std::endl;
}
if ( abmnr.cols() > 3 ) { //** IP present
// if ( verbose ) std::cout << "Found ip values, doing ip inversion" << std::endl;
//! imaginary apparent resistivity
RVector rhoai( rhoa * sin( abmnr[ 3 ] / 1000. ) );
//! modelling operator from fixed layer
Mesh ipMesh( createMesh1D( nlay ) );
DC1dRhoModelling fIP( thk, ab2, mn2, verbose );
fIP.region( 0 )->setTransModel( *transRho );
//! IP (imaginary resistivity) inversion using fIP
RInversion invIP( rhoai, fIP, verbose );
invIP.setAbsoluteError( rhoai / abmnr[ 3 ] * 1.0 );
//! take jacobian from real valued problems
invIP.setModel( res );
invIP.checkJacobian(); //! force jacobian calculation
invIP.setRecalcJacobian( false );
invIP.setLambda( lambdaIP );
invIP.setLocalRegularization( true ); //! Marquardt method
invIP.setModel( model ); //! starting model
invIP.stopAtChi1( false );
RVector ipModel( nlay, median( rhoai ) );
invIP.setModel( ipModel );
ipModel = invIP.run();
RVector phase = atan( ipModel / res ) * 1000.;
save( phase, "phase.vec" );
RVector aphase = atan( invIP.response() / rhoa ) * 1000.;
save( aphase, "aphase.vec" );
std::cout << " ip = " << phase << std::endl;
}
if ( doResolution ) {
size_t nModel( 2 * nlay - 1 );
RVector resolution( nModel );
RVector resMDiag ( nModel );
RMatrix resM;
for ( size_t iModel = 0; iModel < nModel; iModel++ ) {
resolution = inv.modelCellResolution( iModel );
resM.push_back( resolution );
resMDiag[ iModel ] = resolution[ iModel ];
}
save( resMDiag, "resMDiag.vec" );
save( resM, "resM" );
vcout << "diagRM = " << resMDiag << std::endl;
}
return EXIT_SUCCESS;
}