pygimli.physics.em

Frequency-domain (FD) or time-domain (TD) semi-analytical 1D solutions

Overview

Functions

importMaxminData(filename[, verbose])	Import function reading in positions, data, frequencies, geometry.
readusffile(filename[, data])	Read data from single USF (universal sounding file) file.
rhoafromB(B, t, Tx[, current])	Apparent resistivity from B-field TEM
rhoafromU(U, t, Tx[, current, Rx])	Apparent resistivity curve from classical TEM (U or dB/dt)

Classes

FDEM([x, freqs, coilSpacing, inphase, ...])	Class for managing Frequency Domain EM data and their inversions.
HEMmodelling(nlay, height[, f, r])	HEM Airborne modelling class based on the BGR RESOLVE system.
TDEM([filename])	TEM class mainly for holding data etc.
TDEMBlockModelling	alias of VMDTimeDomainModelling
TDEMOccamModelling	alias of TDEMSmoothModelling
TDEMSmoothModelling(thk, **kwargs)	Occam-style (smooth) inversion.
VMDTimeDomainModelling(times, txArea[, rxArea])	Vertical magnetic dipole (VMD) modelling.

Functions

pygimli.physics.em.importMaxminData(filename, verbose=False)

Import function reading in positions, data, frequencies, geometry.

pygimli.physics.em.readusffile(filename, data=None)

Read data from single USF (universal sounding file) file.

data = readusffile( filename ) data = readusffile( filename, data ) will append to data

pygimli.physics.em.rhoafromB(B, t, Tx, current=1)

Apparent resistivity from B-field TEM

\[\rho_a = ( (A_{Tx}*I*\mu_0 ) / (30B) )^2/3 * 4e-7 / t\]

pygimli.physics.em.rhoafromU(U, t, Tx, current=1.0, Rx=None)

Apparent resistivity curve from classical TEM (U or dB/dt)

rhoafromU(U/I, t, TXarea[, RXarea])

\[\rho_a = ( A_{Rx} *A_{Tx} * \mu_0 / 20 / (U/I) )^2/3*t^{-5/3}*4e-7\]

Classes

class pygimli.physics.em.FDEM(x=None, freqs=None, coilSpacing=None, inphase=None, outphase=None, filename=None, scaleFreeAir=False)

Bases: object

Class for managing Frequency Domain EM data and their inversions.

FOP(nlay=2, useHEM=1)

Forward modelling operator using a block discretization.

Parameters:

nlay (int) – Number of blocks

FOP2d(nlay)

2d forward modelling operator.

FOPsmooth(zvec)

Forward modelling operator using fixed layers (smooth inversion).

Parameters:

zvec (array)

__init__(x=None, freqs=None, coilSpacing=None, inphase=None, outphase=None, filename=None, scaleFreeAir=False)

Initialize data class and load data. Provide filename or data.

If filename is given, data is loaded, overwriting settings.

Parameters:

• x (array) – Array of measurement positions

• freq (array) – Measured frequencies

• coilSpacing (float) – Distance between 2 two coils

• inphase (array) – real part of \(|amplitude| * \exp^{i phase}\)

• outphase (array) – imaginary part of \(|amplitude| * \exp^{i phase}\)

• filename (str) – Filename to read from. Supported: .xyz (MaxMin), *.txt (Emsys)

• scaleFreeAir (bool) – Scale inphase and outphase data by free air (primary) solution

datavec(xpos=0)

Extract data vector (stack in and out phase) for given pos/no.

deactivate(fr)

Deactivate a single frequency.

error(xpos=0)

Return error as vector.

errorvec(xpos=0, minvalue=0.0)

Extract error vector for a give position or sounding number.

freq()

Return active (i.e., non-deactivated) frequencies.

importEmsysAsciiData(filename)

Import data from emsys text export file.

columns: no, pos(1-3), separation(4), frequency(6), error(8), inphase (9-11), outphase (12-14), reads: positions, data, frequencies, error and geometry

importIPXData(filename, verbose=False)

Import MaxMin IPX format with pos, data, frequencies & geometry.

importMaxMinData(filename, verbose=False)

Import MaxMin ASCII export (*.txt) data.

inv2D(nlay, lam=100.0, resL=1.0, resU=1000.0, thkL=1.0, thkU=100.0, minErr=1.0)

2d LCI inversion class.

invBlock(xpos=0, nlay=2, noise=1.0, show=True, stmod=30.0, lam=1000.0, lBound=0.0, uBound=0.0, verbose=False, **kwargs)

Create and return Gimli inversion instance for block inversion.

Parameters:

• xpos (array) – position vector

• nLay (int) – Number of layers of the model to be determined OR vector of layer numbers OR forward operator

• noise (float) – Absolute data err in percent

• stmod (float or pg.Vector) – Starting model

• lam (float) – Global regularization parameter lambda.

• lBound (float) – Lower boundary for the model

• uBound (float) – Upper boundary for the model. 0 means no upper booundary

• verbose (bool) – Be verbose

plotAllData(orientation='horizontal', aspect=1000, outname=None, show=False, figsize=(11, 8), everyx=None)

Plot data along a profile as image plots for IP and OP.

plotData(xpos=0, response=None, error=None, ax=None, marker='bo-', rmarker='rx-', clf=True, addlabel='', nv=2)

Plot data as curves at given position.

plotModelAndData(model, xpos, response, modelL=None, modelU=None)

Plot both model and data in subfigures.

readHEMData(filename, takeevery=1, choosevcp=True)

Read RESOLVE type airborne EM data from .XYZ file.

selectData(xpos=0)

Select sounding at a specific position or by number.

Retrieve inphase, outphase and error(if exist) vector from index or near given position

Parameters:

xpos (int | float) – index (int) or position (float) along profile to choose

Returns:

• IP (array)

• OP (array)

• ERR (array or None (if no error is specified))

showModelAndData(model, xpos=0, response=None, figsize=(8, 6))

Show both model and data with response in subfigures.

class pygimli.physics.em.HEMmodelling(nlay, height, f=None, r=None, **kwargs)

Bases: Block1DModelling

HEM Airborne modelling class based on the BGR RESOLVE system.

__init__(nlay, height, f=None, r=None, **kwargs)

Initialize class with geometry.

Parameters:

• nlay (int) – number of layers

• height (float) – helicopter

• f (array [BGR RESOLVE system 387Hz-133kHz]) – frequency vector

• r (array [BGR RESOLVE system 7.91-7.94]) – distance vector

• scaling (float) – scaling factor or string (ppm=1e6, percent=1e2)

c0 = 299792458.0000066

calc_forward(x, h, rho, d, epr, mur, quasistatic=False)

Calculate forward response.

downward(rho, d, z, epr, mur, lam)

Downward continuation of fields.

ep0 = 8.85418781762e-12

fdefault = array([   387.,   1821.,   8388.,  41460., 133300.])

mu0 = 1.2566370614359173e-06

rdefault = array([7.94, 7.93, 7.93, 7.91, 7.92])

response(model)

Compute response vector by pasting in-phase and out-phase data.

response_mt(par, i=0)

Multi-threaded forward response.

scaling = 1000000.0

vmd_hem(h, rho, d, epr=1.0, mur=1.0, quasistatic=False)

Vertical magnetic dipole (VMD) response.

Parameters:

• h (float) – flight height

• rho (array) – resistivity vector

• d (array) – thickness vector

vmd_total_Ef(h, z, rho, d, epr, mur, tm)

VMD E-phi field (not used actively).

class pygimli.physics.em.TDEM(filename=None)

Bases: object

TEM class mainly for holding data etc.

__init__(filename=None)

Initialize class and (optionally) load data

basename = 'new'

filterData(token, vmin=0, vmax=9e+99)

Filter all sounding data according to criterion.

filterSoundings(token, value)

Filter all values matching a certain token.

gather(token)

Collect item from all soundings.

invert(nr=0, nlay=4, thickness=None, errorFloor=0.05)

Do inversion.

load(filename)

Road data from usf, txt (siroTEM), tem (TEMfast) or UniK file.

plotRhoa(ax=None, ploterror=False, corrramp=False, **kwargs)

Plot all apparent resistivity curves into one window.

plotTransients(ax=None, **kwargs)

Plot all transients into one window

showInfos()

stackAll(tmin=0, tmax=100)

Stack all measurements yielding a new TDEM class instance.

pygimli.physics.em.TDEMBlockModelling

alias of VMDTimeDomainModelling

pygimli.physics.em.TDEMOccamModelling

alias of TDEMSmoothModelling

class pygimli.physics.em.TDEMSmoothModelling(thk, **kwargs)

Bases: MeshModelling

Occam-style (smooth) inversion.

__init__(thk, **kwargs)

Initialize.

Variables:

• fop (pg.frameworks.Modelling)

• data (pg.DataContainer)

• modelTrans ([pg.trans.TransLog()])

Parameters:

**kwargs – fop : Modelling

response(par)

Model response (forward modelling).

class pygimli.physics.em.VMDTimeDomainModelling(times, txArea, rxArea=None, **kwargs)

Bases: VMDModelling

Vertical magnetic dipole (VMD) modelling.

__init__(times, txArea, rxArea=None, **kwargs)

calcEphiT(tMin, tMax, rho, d, rMin, rMax, z, dipm)

Compute radial electric field.

calcRhoa(thk, res)

Compute apparent resistivity response

createStartModel(rhoa, nLayers=None, thickness=None)

Create suitable starting model.

Create suitable starting model based on median apparent resistivity values and skin depth approximation.

response(par)

par = [thicknesses(nLay), res(nlay + 1)]

response_mt(par, i=0)

par = [thicknesses, res]