PyHydroGeophysX.forward package#

Submodules#

PyHydroGeophysX.forward.em1d module#

Qt-free 1D FDEM/TDEM forward helpers.

PyHydroGeophysX.forward.em1d.fdem_forward(model: ~typing.Dict[str, ~typing.Any], geom: ~typing.Dict[str, ~typing.Any], log: ~typing.Callable[[str], None] = <function noop>) → Dict[str, Any][source]#

1D FDEM forward response (secondary field, real/imag per frequency).

PyHydroGeophysX.forward.em1d.model_arrays(model: Dict[str, Any]) → tuple[ndarray, ndarray, ndarray][source]#

Return (thicknesses, resistivity, conductivity) from a model dict.

PyHydroGeophysX.forward.em1d.model_depth_profile(thicknesses: ndarray, resistivity: ndarray, pad: float = 20.0) → tuple[ndarray, ndarray][source]#

Step profile (depth, resistivity) for plotting a layered model.

PyHydroGeophysX.forward.em1d.tdem_forward(model: ~typing.Dict[str, ~typing.Any], geom: ~typing.Dict[str, ~typing.Any], log: ~typing.Callable[[str], None] = <function noop>) → Dict[str, Any][source]#

1D TDEM forward response (dB/dt or H per time channel).

PyHydroGeophysX.forward.ert3d module#

Synthetic 3D ERT forward modeling on a built mesh (Qt-free, worker-safe).

Given a 3D mesh + electrode layout (as produced by mesh3d_builder.generate_mesh()) and a resistivity model, this generates synthetic 3D ERT apparent-resistivity data. It mirrors examples/Ex_3D_ERT_forward.py: build an ERT data container with 3D geometric factors (core.mesh_3d.create_3d_ert_data_container()), run pygimli’s ERTModelling response via forward.ert_forward.ERTForwardModeling, add noise, and save a pygimli .dat file plus a resistivity VTK.

Nothing here imports PySide6, so it is safe to call from a worker thread. pygimli and the geophysics helpers are imported lazily inside run_ert3d_forward().

PyHydroGeophysX.forward.ert3d.run_ert3d_forward(mesh: Any, electrodes: Any, scheme: str = 'dd', background_res: float = 100.0, marker_res: Dict[int, float] | None = None, noise: float = 0.03, seed: int = 42, output_dir: str = '.', log: Callable[[str], None] | None = None) → Dict[str, Any][source]#

Run a 3D ERT forward simulation and save synthetic data.

Parameters:

  • mesh (pygimli.Mesh) – Forward mesh (e.g. from mesh3d_builder.generate_mesh).

  • electrodes (pandas.DataFrame) – Electrode positions with x, y, z columns.

  • scheme (str) – Measurement scheme (dd, wa, slm, wb …).

  • background_res (float) – Resistivity (ohm-m) applied to every cell by default.

  • marker_res (dict, optional) – Per-region resistivity override, keyed by mesh cell marker.

  • noise (float) – Relative Gaussian noise added to the synthetic apparent resistivity.

  • seed (int) – Random seed for the noise.

  • output_dir (str) – Directory under which an ert3d_forward folder is written.

  • log (callable, optional) – Progress callback taking one string.

PyHydroGeophysX.forward.ert_forward module#

Forward modeling utilities for Electrical Resistivity Tomography (ERT).

class PyHydroGeophysX.forward.ert_forward.ERTForwardModeling(mesh: pygimli.Mesh, data: pygimli.DataContainer | None = None)[source]#

Bases: object

Class for forward modeling of Electrical Resistivity Tomography (ERT) data.

create_synthetic_data(xpos: ndarray, ypos: ndarray | None = None, mesh: pygimli.Mesh | None = None, res_models: ndarray | None = None, schemeName: str = 'wa', noise_level: float = 0.05, absolute_error: float = 0.0, relative_error: float = 0.05, save_path: str | None = None, show_data: bool = False, seed: int | None = None, xbound: float = 100, ybound: float = 100) → Tuple[pygimli.DataContainer, pygimli.Mesh][source]#

Create synthetic ERT data using forward modeling.

This method simulates an ERT survey by placing electrodes, creating a measurement scheme, performing forward modeling to generate synthetic data, and adding noise.

Parameters:

  • xpos – X-coordinates of electrodes

  • ypos – Y-coordinates of electrodes (if None, uses flat surface)

  • mesh – Mesh for forward modeling

  • res_models – Resistivity model values

  • schemeName – Name of measurement scheme (‘wa’, ‘dd’, etc.)

  • noise_level – Level of Gaussian noise to add

  • absolute_error – Absolute error for data estimation

  • relative_error – Relative error for data estimation

  • save_path – Path to save synthetic data (if None, does not save)

  • show_data – Whether to display data after creation

  • seed – Random seed for noise generation

  • xbound – X boundary extension for mesh

  • ybound – Y boundary extension for mesh

Returns:

Tuple of (synthetic ERT data container, simulation mesh)

forward(resistivity_model: ndarray, log_transform: bool = True) → ndarray[source]#

Compute forward response for a given resistivity model.

Parameters:

  • resistivity_model – Resistivity model values

  • log_transform – Whether resistivity_model is log-transformed

Returns:

Forward response (apparent resistivity)

forward_and_jacobian(resistivity_model: ndarray, log_transform: bool = True) → Tuple[ndarray, ndarray][source]#

Compute forward response and Jacobian matrix.

Parameters:

  • resistivity_model – Resistivity model values

  • log_transform – Whether resistivity_model is log-transformed

Returns:

Tuple of (forward response, Jacobian matrix)

get_coverage(resistivity_model: ndarray, log_transform: bool = True) → ndarray[source]#

Compute coverage (resolution) for a given resistivity model.

Parameters:

  • resistivity_model – Resistivity model values

  • log_transform – Whether resistivity_model is log-transformed

Returns:

Coverage values for each cell

set_data(data: pygimli.DataContainer) → None[source]#

Set ERT data for forward modeling.

Parameters:

data – ERT data container

set_mesh(mesh: pygimli.Mesh) → None[source]#

Set mesh for forward modeling.

Parameters:

mesh – PyGIMLI mesh

PyHydroGeophysX.forward.ert_forward.ertforandjac(fob: Any, rhomodel: Any, xr: Any) → Any[source]#

Forward model and Jacobian for ERT.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • rhomodel (pg.RVector) – Resistivity model.

  • xr (np.ndarray) – Log-transformed model parameter.

Returns:

Log-transformed forward response. J (np.ndarray): Jacobian matrix.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.ert_forward.ertforandjac2(fob: Any, xr: Any, mesh: Any) → Any[source]#

Alternative ERT forward model and Jacobian using log-resistivity values.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • xr (np.ndarray) – Log-transformed model parameter.

  • mesh (pg.Mesh) – Mesh for the forward model.

Returns:

Log-transformed forward response. J (np.ndarray): Jacobian matrix.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.ert_forward.ertforward(fob: Any, mesh: Any, rhomodel: Any, xr: Any) → Any[source]#

Forward model for ERT.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • mesh (pg.Mesh) – Mesh for the forward model.

  • rhomodel (pg.RVector) – Resistivity model vector.

  • xr (np.ndarray) – Log-transformed model parameter (resistivity).

Returns:

Log-transformed forward response. rhomodel (pg.RVector): Updated resistivity model.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.ert_forward.ertforward2(fob: Any, xr: Any, mesh: Any) → Any[source]#

Simplified ERT forward model.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • xr (np.ndarray) – Log-transformed model parameter.

  • mesh (pg.Mesh) – Mesh for the forward model.

Returns:

Log-transformed forward response.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.fdem_forward module#

Forward modeling utilities for Frequency-Domain Electromagnetic (FDEM) data.

Uses SimPEG’s frequency-domain module for 1D layered-earth simulations.

class PyHydroGeophysX.forward.fdem_forward.FDEMForwardModeling(thicknesses: ndarray, survey_config: FDEMSurveyConfig | None = None, survey: simpeg.electromagnetics.frequency_domain.Survey | None = None)[source]#

Bases: object

Forward modeling of Frequency-Domain EM data using SimPEG.

Supports 1D layered-earth conductivity models.

forward(conductivity: ndarray) → ndarray[source]#

Compute FDEM response for a given conductivity model.

forward_with_noise(conductivity: ndarray, noise_level: float = 0.05, seed: int | None = None) → Tuple[ndarray, ndarray, ndarray][source]#

Compute noisy and clean FDEM responses with data uncertainties.

static hydro_to_fdem(water_content: ndarray, porosity: ndarray, layer_thicknesses: ndarray, **petro_params)[source]#

Convert hydrological properties to FDEM response via petrophysics.

class PyHydroGeophysX.forward.fdem_forward.FDEMSurveyConfig(source_location: ndarray = None, source_radius: float = 10.0, receiver_location: ndarray = None, receiver_orientation: str = 'z', receiver_component: str = 'secondary', frequencies: ndarray = None, waveform_type: str = 'dipole')[source]#

Bases: object

Configuration for FDEM survey geometry.

frequencies: ndarray = None#
receiver_component: str = 'secondary'#
receiver_location: ndarray = None#
receiver_orientation: str = 'z'#
source_location: ndarray = None#
source_radius: float = 10.0#
waveform_type: str = 'dipole'#

PyHydroGeophysX.forward.gravmag module#

Analytic gravity and magnetic forward operators.

PyHydroGeophysX.forward.gravmag.forward_bodies(xobs: ~numpy.ndarray, yobs: ~numpy.ndarray, kind: str, bodies: ~typing.List[~typing.Dict[str, ~typing.Any]], field: ~typing.Dict[str, ~typing.Any] | None = None, log: ~typing.Callable[[str], None] = <function noop>) → ndarray[source]#

Sum the anomaly of a list of bodies. kind = ‘gravity’ or ‘magnetics’.

PyHydroGeophysX.forward.gravmag.gravity_prism(xobs: ndarray, yobs: ndarray, body: Dict[str, Any]) → ndarray[source]#

Vertical gravity (mGal) of a right rectangular prism (Nagy 1966). z down.

PyHydroGeophysX.forward.gravmag.gravity_sphere(xobs: ndarray, yobs: ndarray, body: Dict[str, Any]) → ndarray[source]#

Vertical gravity (mGal) of a buried sphere. z positive down, obs at z=0.

PyHydroGeophysX.forward.gravmag.magnetic_dipole(xobs: ndarray, yobs: ndarray, body: Dict[str, Any], field: Dict[str, Any]) → ndarray[source]#

Total-field magnetic anomaly (nT) of an induced/magnetized sphere (a dipole).

PyHydroGeophysX.forward.srt_forward module#

Forward modeling utilities for Seismic Refraction Tomography (SRT).

class PyHydroGeophysX.forward.srt_forward.SeismicForwardModeling(mesh: pygimli.Mesh, scheme: pygimli.DataContainer | None = None)[source]#

Bases: object

Class for forward modeling of Seismic Refraction Tomography (SRT) data.

classmethod create_synthetic_data(sensor_x: ndarray, surface_points: ndarray | None = None, mesh: pygimli.Mesh = None, velocity_model: ndarray | None = None, slowness: bool = False, shot_distance: float = 5, noise_level: float = 0.05, noise_abs: float = 1e-05, save_path: str | None = None, show_data: bool = False, verbose: bool = False, seed: int | None = None) → Tuple[pygimli.DataContainer, pygimli.Mesh][source]#

Create synthetic seismic data using forward modeling.

This method simulates a seismic survey by placing geophones along a surface, creating a measurement scheme, and performing forward modeling to generate synthetic travel time data.

Parameters:

  • sensor_x – X-coordinates of geophones

  • surface_points – Surface coordinates for placing geophones [[x,y],…] If None, geophones will be placed on flat surface

  • mesh – Mesh for forward modeling

  • velocity_model – Velocity model values

  • slowness – Whether velocity_model is slowness (1/v)

  • shot_distance – Distance between shots

  • noise_level – Level of relative noise to add

  • noise_abs – Level of absolute noise to add

  • save_path – Path to save synthetic data (if None, does not save)

  • show_data – Whether to display data after creation

  • verbose – Whether to show verbose output

  • seed – Random seed for noise generation

Returns:

Tuple of (synthetic seismic data container, simulation mesh)

static draw_first_picks(ax, data, tt=None, plotva=False, **kwargs)[source]#

Plot first arrivals as lines.

Parameters:

  • ax (matplotlib.axes) – axis to draw the lines in

  • data (:gimliapi:`GIMLI::DataContainer`) – data containing shots (“s”), geophones (“g”) and traveltimes (“t”)

  • tt (array, optional) – traveltimes to use instead of data(“t”)

  • plotva (bool, optional) – plot apparent velocity instead of traveltimes

Returns:

ax – the modified axis

Return type:

matplotlib.axes

forward(velocity_model: ndarray, slowness: bool = True) → ndarray[source]#

Compute forward response for a given velocity model.

Parameters:

  • velocity_model – Velocity model values (or slowness if slowness=True)

  • slowness – Whether velocity_model is slowness (1/v)

Returns:

Forward response (travel times)

set_mesh(mesh: pygimli.Mesh) → None[source]#

Set mesh for forward modeling.

Parameters:

mesh – PyGIMLI mesh

set_scheme(scheme: pygimli.DataContainer) → None[source]#

Set seismic data scheme for forward modeling.

Parameters:

scheme – Seismic data scheme

PyHydroGeophysX.forward.tdem_forward module#

Forward modelling for time-domain electromagnetic (TDEM) soundings.

The layered-earth response comes from SimPEG. On top of it this module applies the instrument model that turns a modelled decay into the numbers a receiver records: convolution with the transmitter’s turn-off waveform, a cascade of first-order receiver low-pass stages, superposition of the bipolar transmitter train, and integration of each gate over its recorded window. Each of those is a standard step, and their parameters are read from the acquisition files rather than assumed; see PyHydroGeophysX.data_processing.em1d.

The whole instrument model is linear in the modelled response, so it is built once as a matrix and applied to the forward and the Jacobian alike. A dataset that does not describe an instrument keeps SimPEG’s direct receiver path.

PyHydroGeophysX.forward.tdem_forward.GATE_SHAPE_NAMES = {1: 'tukey', 2: 'square'}#

GateShape values, as the acquisition files record them.

Only the shapes this module implements appear. A file asking for one that does not is refused rather than quietly given the nearest thing, because a wrong window in the forward has nothing downstream to catch it. See PyHydroGeophysX.forward.em1d._gate_window_name().

PyHydroGeophysX.forward.tdem_forward.GATE_WINDOWS: Tuple[str, ...] = ('tukey', 'square', 'centre')#

How a modelled decay becomes one gate value.

A recorded gate is an integral of the transient over a window, so the model of it is the same integral. The acquisition files name the window: GateShape selects its shape and GateShapePar1 gives the fraction of the width that is cosine-tapered, so the usual 0.667 leaves a flat top over the middle third.

tukey is that window. The integral runs in linear time, and the response between the samples of its own grid is interpolated with the local cubic Hermite rule described in _local_log_hermite_matrix(). Reading GateShapePar1 as the tapered fraction rather than as the flat top changes the result by 1.5 to 4 percent; responses stored in two surveys pick the reading used here.

Two things separate it from a flat average of the window, and they pull the same way. The taper draws weight in from the edges, and the window is symmetric in linear time while the gate centre a file records is the geometric mean of open and close, so it sits slightly late relative to it. Neither rule is trying to reproduce the centre value.

square is the same integral with no taper, for a survey whose files ask for the plain window. centre reads the response at the gate centre and is the fallback for data that records no gate windows at all.

class PyHydroGeophysX.forward.tdem_forward.TDEMForwardModeling(thicknesses: ndarray, survey_config: TDEMSurveyConfig | None = None, survey: simpeg.electromagnetics.time_domain.Survey | None = None)[source]#

Bases: object

Class for forward modeling of Time-Domain Electromagnetic (TDEM) data.

This class provides functionality for 1D layered Earth TDEM forward modeling using SimPEG’s time_domain module.

Example

>>> # Define layer model
>>> thicknesses = np.array([10.0, 30.0])
>>> conductivity = np.array([0.01, 0.1, 0.001])  # S/m
>>>
>>> # Create forward modeler
>>> fwd = TDEMForwardModeling(thicknesses=thicknesses)
>>>
>>> # Compute response
>>> response = fwd.forward(conductivity)
forward(conductivity: ndarray, log_input: bool = False) → ndarray[source]#

Compute forward response for a given conductivity model.

Parameters:

  • conductivity – Conductivity values for each layer (S/m)

  • log_input – If True, conductivity is log-transformed

Returns:

Forward response (magnetic flux density, T)

forward_with_noise(conductivity: ndarray, noise_level: float = 0.05, seed: int | None = None, log_input: bool = False) → Tuple[ndarray, ndarray, ndarray][source]#

Compute forward response with added Gaussian noise.

Parameters:

  • conductivity – Conductivity values for each layer (S/m)

  • noise_level – Relative noise level (default 5%)

  • seed – Random seed for reproducibility

  • log_input – If True, conductivity is log-transformed

Returns:

Tuple of (noisy_data, clean_data, uncertainties)

get_times() → ndarray[source]#

Get the time channels from the survey.

property n_data: int#

Number of data points.

sensitivity(conductivity: ndarray) → ndarray[source]#

Analytic d(response)/d(conductivity), averaged over the gate windows.

The same reduction the forward applies has to be applied to the Jacobian, or the two describe different data.

class PyHydroGeophysX.forward.tdem_forward.TDEMSurveyConfig(source_location: ndarray = None, source_radius: float = 10.0, source_current: float = 1.0, source_turns: int = 1, source_moment: float | None = None, waveform_times: ndarray | None = None, waveform_currents: ndarray | None = None, gate_open: ndarray | None = None, gate_close: ndarray | None = None, gate_window: str = 'centre', gate_window_par: float = 0.667, waveform_period: float | None = None, waveform_repetitions: int = 3, analog_points_per_decade: int = 150, analog_model_points_per_decade: int = 40, analog_lowpass: dict | None = None, instrument_points_per_decade: int = 10, instrument_model_points_per_decade: int = 10, gate_quadrature_order: int = 8, receiver_location: ndarray = None, receiver_orientation: str = 'z', receiver_type: str = 'b', times: ndarray = None, waveform_type: str = 'step_off')[source]#

Bases: object

Configuration for TDEM survey geometry.

source_location#

[x, y, z] location of source center (m)

Type:

numpy.ndarray

source_radius#

Radius of circular loop source (m)

Type:

float

source_current#

Peak current amplitude (A)

Type:

float

receiver_location#

[x, y, z] location of receiver (m)

Type:

numpy.ndarray

receiver_orientation#

Component to measure (‘x’, ‘y’, or ‘z’)

Type:

str

times#

Time channels for measurement (s)

Type:

numpy.ndarray

waveform_type#

Type of waveform (‘step_off’, ‘ramp_off’, ‘custom’)

Type:

str

analog_lowpass: dict | None = None#

Analog receiver electronics parsed from a GEX file. The supported fields are receiver_damping / receiver_cutoff_hz for the receiver-coil two-pole filter and tib_order / tib_cutoff_hz for the transmitter-interface-board low-pass filter.

analog_model_points_per_decade: int = 40#

Resolution of the grid SimPEG is asked to model, in samples per decade. Kept separate because SimPEG’s setup cost grows with it while the filter’s accuracy does not depend on it.

analog_points_per_decade: int = 150#

Resolution of the internal grid the analog filter is integrated on, in samples per decade of time. See _analog_sampling().

gate_close: ndarray | None = None#
gate_open: ndarray | None = None#

Gate windows, for averaging the response over each one instead of reading it at the gate centre.

gate_quadrature_order: int = 8#

Gauss points per smooth panel of the gate window. Eight integrates it to a few parts per million, negligible beside one SimPEG call.

gate_window: str = 'centre'#

How a modelled decay becomes a gate value. See _gate_sampling().

gate_window_par: float = 0.667#

Total cosine-taper fraction of the gate window, as stored in the project’s GateShapePar1. 0.667 leaves a 0.333 flat top.

instrument_model_points_per_decade: int = 10#

SimPEG step-response density, in samples per decade. Ten matches the response grid and avoids asking SimPEG for redundant samples; raise it independently for convergence checks.

instrument_points_per_decade: int = 10#

Response grid density, in samples per decade. Ten matches the grid the instrument’s own processing works on, with local interpolation between those nodes.

receiver_location: ndarray = None#
receiver_orientation: str = 'z'#
receiver_type: str = 'b'#
source_current: float = 1.0#
source_location: ndarray = None#
source_moment: float | None = None#

Transmitter moment in A m^2, overriding current * turns * area. Set it to 1.0 for data already normalized by the transmitter moment, which is how TEMcompany instruments report dB/dt (V/A/m^4): dividing the measurement by the moment and then modelling with that moment counts it twice.

source_radius: float = 10.0#
source_turns: int = 1#
times: ndarray = None#
waveform_currents: ndarray | None = None#
waveform_period: float | None = None#

Half-period of the bipolar transmitter cycle, in seconds. Set it to model the earlier pulses of the train the way the reference implementation does.

waveform_repetitions: int = 3#

How many earlier half-cycles the repetition sums, three in the reference.

waveform_times: ndarray | None = None#

Turn-off waveform as (times, currents) nodes. A real ramp is not a step, and the earliest gates of a ground system sit only microseconds after it ends, which is exactly where the difference bites.

waveform_type: str = 'step_off'#
PyHydroGeophysX.forward.tdem_forward.create_tdem_survey(times: ndarray, source_radius: float = 10.0, source_current: float = 1.0, source_location: ndarray | None = None, receiver_location: ndarray | None = None, receiver_orientation: str = 'z', waveform_type: str = 'step_off') → simpeg.electromagnetics.time_domain.Survey[source]#

Create a TDEM survey for 1D sounding.

Parameters:

  • times – Time channels (s)

  • source_radius – Loop radius (m)

  • source_current – Peak current (A)

  • source_location – Source center [x, y, z] (m)

  • receiver_location – Receiver position [x, y, z] (m)

  • receiver_orientation – Measurement component (‘x’, ‘y’, ‘z’)

  • waveform_type – Waveform type (‘step_off’, ‘ramp_off’)

Returns:

SimPEG TDEM Survey object

PyHydroGeophysX.forward.tdem_forward.hydro_to_tdem(*args, **kwargs)[source]#

Deprecated alias for simulate_tdem_sounding_from_hydro().

PyHydroGeophysX.forward.tdem_forward.simulate_tdem_sounding_from_hydro(water_content: ndarray, porosity: ndarray, layer_thicknesses: ndarray, sigma_w: float | ndarray = 0.05, m: float | ndarray = 1.5, n: float | ndarray = 2.0, sigma_s: float | ndarray = 0.0, times: ndarray | None = None, source_radius: float = 10.0, noise_level: float = 0.05, seed: int | None = None, verbose: bool = False) → Tuple[ndarray, ndarray, ndarray, ndarray][source]#

Convert hydrological properties to TDEM response.

This function takes water content and porosity from hydrological models and computes the expected TDEM response using petrophysical relationships.

Parameters:

  • water_content – Water content for each layer (-)

  • porosity – Porosity for each layer (-)

  • layer_thicknesses – Thickness of each layer except bottom (m)

  • sigma_w – Pore water conductivity (S/m)

  • m – Cementation exponent

  • n – Saturation exponent

  • sigma_s – Surface conductivity (S/m)

  • times – Time channels (s), default is logspace(-5, -2, 31)

  • source_radius – Loop radius (m)

  • noise_level – Relative noise level for synthetic data

  • seed – Random seed for reproducibility

  • verbose – Print progress information

Returns:

Tuple of (noisy_data, clean_data, uncertainties, conductivity)

Module contents#

Lazy forward-modeling exports with independently optional backends.

class PyHydroGeophysX.forward.ERTForwardModeling(mesh: pygimli.Mesh, data: pygimli.DataContainer | None = None)[source]#

Bases: object

Class for forward modeling of Electrical Resistivity Tomography (ERT) data.

create_synthetic_data(xpos: ndarray, ypos: ndarray | None = None, mesh: pygimli.Mesh | None = None, res_models: ndarray | None = None, schemeName: str = 'wa', noise_level: float = 0.05, absolute_error: float = 0.0, relative_error: float = 0.05, save_path: str | None = None, show_data: bool = False, seed: int | None = None, xbound: float = 100, ybound: float = 100) → Tuple[pygimli.DataContainer, pygimli.Mesh][source]#

Create synthetic ERT data using forward modeling.

This method simulates an ERT survey by placing electrodes, creating a measurement scheme, performing forward modeling to generate synthetic data, and adding noise.

Parameters:

  • xpos – X-coordinates of electrodes

  • ypos – Y-coordinates of electrodes (if None, uses flat surface)

  • mesh – Mesh for forward modeling

  • res_models – Resistivity model values

  • schemeName – Name of measurement scheme (‘wa’, ‘dd’, etc.)

  • noise_level – Level of Gaussian noise to add

  • absolute_error – Absolute error for data estimation

  • relative_error – Relative error for data estimation

  • save_path – Path to save synthetic data (if None, does not save)

  • show_data – Whether to display data after creation

  • seed – Random seed for noise generation

  • xbound – X boundary extension for mesh

  • ybound – Y boundary extension for mesh

Returns:

Tuple of (synthetic ERT data container, simulation mesh)

forward(resistivity_model: ndarray, log_transform: bool = True) → ndarray[source]#

Compute forward response for a given resistivity model.

Parameters:

  • resistivity_model – Resistivity model values

  • log_transform – Whether resistivity_model is log-transformed

Returns:

Forward response (apparent resistivity)

forward_and_jacobian(resistivity_model: ndarray, log_transform: bool = True) → Tuple[ndarray, ndarray][source]#

Compute forward response and Jacobian matrix.

Parameters:

  • resistivity_model – Resistivity model values

  • log_transform – Whether resistivity_model is log-transformed

Returns:

Tuple of (forward response, Jacobian matrix)

get_coverage(resistivity_model: ndarray, log_transform: bool = True) → ndarray[source]#

Compute coverage (resolution) for a given resistivity model.

Parameters:

  • resistivity_model – Resistivity model values

  • log_transform – Whether resistivity_model is log-transformed

Returns:

Coverage values for each cell

set_data(data: pygimli.DataContainer) → None[source]#

Set ERT data for forward modeling.

Parameters:

data – ERT data container

set_mesh(mesh: pygimli.Mesh) → None[source]#

Set mesh for forward modeling.

Parameters:

mesh – PyGIMLI mesh

class PyHydroGeophysX.forward.FDEMForwardModeling(thicknesses: ndarray, survey_config: FDEMSurveyConfig | None = None, survey: simpeg.electromagnetics.frequency_domain.Survey | None = None)[source]#

Bases: object

Forward modeling of Frequency-Domain EM data using SimPEG.

Supports 1D layered-earth conductivity models.

forward(conductivity: ndarray) → ndarray[source]#

Compute FDEM response for a given conductivity model.

forward_with_noise(conductivity: ndarray, noise_level: float = 0.05, seed: int | None = None) → Tuple[ndarray, ndarray, ndarray][source]#

Compute noisy and clean FDEM responses with data uncertainties.

static hydro_to_fdem(water_content: ndarray, porosity: ndarray, layer_thicknesses: ndarray, **petro_params)[source]#

Convert hydrological properties to FDEM response via petrophysics.

class PyHydroGeophysX.forward.FDEMSurveyConfig(source_location: ndarray = None, source_radius: float = 10.0, receiver_location: ndarray = None, receiver_orientation: str = 'z', receiver_component: str = 'secondary', frequencies: ndarray = None, waveform_type: str = 'dipole')[source]#

Bases: object

Configuration for FDEM survey geometry.

frequencies: ndarray = None#
receiver_component: str = 'secondary'#
receiver_location: ndarray = None#
receiver_orientation: str = 'z'#
source_location: ndarray = None#
source_radius: float = 10.0#
waveform_type: str = 'dipole'#
class PyHydroGeophysX.forward.SeismicForwardModeling(mesh: pygimli.Mesh, scheme: pygimli.DataContainer | None = None)[source]#

Bases: object

Class for forward modeling of Seismic Refraction Tomography (SRT) data.

classmethod create_synthetic_data(sensor_x: ndarray, surface_points: ndarray | None = None, mesh: pygimli.Mesh = None, velocity_model: ndarray | None = None, slowness: bool = False, shot_distance: float = 5, noise_level: float = 0.05, noise_abs: float = 1e-05, save_path: str | None = None, show_data: bool = False, verbose: bool = False, seed: int | None = None) → Tuple[pygimli.DataContainer, pygimli.Mesh][source]#

Create synthetic seismic data using forward modeling.

This method simulates a seismic survey by placing geophones along a surface, creating a measurement scheme, and performing forward modeling to generate synthetic travel time data.

Parameters:

  • sensor_x – X-coordinates of geophones

  • surface_points – Surface coordinates for placing geophones [[x,y],…] If None, geophones will be placed on flat surface

  • mesh – Mesh for forward modeling

  • velocity_model – Velocity model values

  • slowness – Whether velocity_model is slowness (1/v)

  • shot_distance – Distance between shots

  • noise_level – Level of relative noise to add

  • noise_abs – Level of absolute noise to add

  • save_path – Path to save synthetic data (if None, does not save)

  • show_data – Whether to display data after creation

  • verbose – Whether to show verbose output

  • seed – Random seed for noise generation

Returns:

Tuple of (synthetic seismic data container, simulation mesh)

static draw_first_picks(ax, data, tt=None, plotva=False, **kwargs)[source]#

Plot first arrivals as lines.

Parameters:

  • ax (matplotlib.axes) – axis to draw the lines in

  • data (:gimliapi:`GIMLI::DataContainer`) – data containing shots (“s”), geophones (“g”) and traveltimes (“t”)

  • tt (array, optional) – traveltimes to use instead of data(“t”)

  • plotva (bool, optional) – plot apparent velocity instead of traveltimes

Returns:

ax – the modified axis

Return type:

matplotlib.axes

forward(velocity_model: ndarray, slowness: bool = True) → ndarray[source]#

Compute forward response for a given velocity model.

Parameters:

  • velocity_model – Velocity model values (or slowness if slowness=True)

  • slowness – Whether velocity_model is slowness (1/v)

Returns:

Forward response (travel times)

set_mesh(mesh: pygimli.Mesh) → None[source]#

Set mesh for forward modeling.

Parameters:

mesh – PyGIMLI mesh

set_scheme(scheme: pygimli.DataContainer) → None[source]#

Set seismic data scheme for forward modeling.

Parameters:

scheme – Seismic data scheme

class PyHydroGeophysX.forward.TDEMForwardModeling(thicknesses: ndarray, survey_config: TDEMSurveyConfig | None = None, survey: simpeg.electromagnetics.time_domain.Survey | None = None)[source]#

Bases: object

Class for forward modeling of Time-Domain Electromagnetic (TDEM) data.

This class provides functionality for 1D layered Earth TDEM forward modeling using SimPEG’s time_domain module.

Example

>>> # Define layer model
>>> thicknesses = np.array([10.0, 30.0])
>>> conductivity = np.array([0.01, 0.1, 0.001])  # S/m
>>>
>>> # Create forward modeler
>>> fwd = TDEMForwardModeling(thicknesses=thicknesses)
>>>
>>> # Compute response
>>> response = fwd.forward(conductivity)
forward(conductivity: ndarray, log_input: bool = False) → ndarray[source]#

Compute forward response for a given conductivity model.

Parameters:

  • conductivity – Conductivity values for each layer (S/m)

  • log_input – If True, conductivity is log-transformed

Returns:

Forward response (magnetic flux density, T)

forward_with_noise(conductivity: ndarray, noise_level: float = 0.05, seed: int | None = None, log_input: bool = False) → Tuple[ndarray, ndarray, ndarray][source]#

Compute forward response with added Gaussian noise.

Parameters:

  • conductivity – Conductivity values for each layer (S/m)

  • noise_level – Relative noise level (default 5%)

  • seed – Random seed for reproducibility

  • log_input – If True, conductivity is log-transformed

Returns:

Tuple of (noisy_data, clean_data, uncertainties)

get_times() → ndarray[source]#

Get the time channels from the survey.

property n_data: int#

Number of data points.

sensitivity(conductivity: ndarray) → ndarray[source]#

Analytic d(response)/d(conductivity), averaged over the gate windows.

The same reduction the forward applies has to be applied to the Jacobian, or the two describe different data.

class PyHydroGeophysX.forward.TDEMSurveyConfig(source_location: ndarray = None, source_radius: float = 10.0, source_current: float = 1.0, source_turns: int = 1, source_moment: float | None = None, waveform_times: ndarray | None = None, waveform_currents: ndarray | None = None, gate_open: ndarray | None = None, gate_close: ndarray | None = None, gate_window: str = 'centre', gate_window_par: float = 0.667, waveform_period: float | None = None, waveform_repetitions: int = 3, analog_points_per_decade: int = 150, analog_model_points_per_decade: int = 40, analog_lowpass: dict | None = None, instrument_points_per_decade: int = 10, instrument_model_points_per_decade: int = 10, gate_quadrature_order: int = 8, receiver_location: ndarray = None, receiver_orientation: str = 'z', receiver_type: str = 'b', times: ndarray = None, waveform_type: str = 'step_off')[source]#

Bases: object

Configuration for TDEM survey geometry.

source_location#

[x, y, z] location of source center (m)

Type:

numpy.ndarray

source_radius#

Radius of circular loop source (m)

Type:

float

source_current#

Peak current amplitude (A)

Type:

float

receiver_location#

[x, y, z] location of receiver (m)

Type:

numpy.ndarray

receiver_orientation#

Component to measure (‘x’, ‘y’, or ‘z’)

Type:

str

times#

Time channels for measurement (s)

Type:

numpy.ndarray

waveform_type#

Type of waveform (‘step_off’, ‘ramp_off’, ‘custom’)

Type:

str

analog_lowpass: dict | None = None#

Analog receiver electronics parsed from a GEX file. The supported fields are receiver_damping / receiver_cutoff_hz for the receiver-coil two-pole filter and tib_order / tib_cutoff_hz for the transmitter-interface-board low-pass filter.

analog_model_points_per_decade: int = 40#

Resolution of the grid SimPEG is asked to model, in samples per decade. Kept separate because SimPEG’s setup cost grows with it while the filter’s accuracy does not depend on it.

analog_points_per_decade: int = 150#

Resolution of the internal grid the analog filter is integrated on, in samples per decade of time. See _analog_sampling().

gate_close: ndarray | None = None#
gate_open: ndarray | None = None#

Gate windows, for averaging the response over each one instead of reading it at the gate centre.

gate_quadrature_order: int = 8#

Gauss points per smooth panel of the gate window. Eight integrates it to a few parts per million, negligible beside one SimPEG call.

gate_window: str = 'centre'#

How a modelled decay becomes a gate value. See _gate_sampling().

gate_window_par: float = 0.667#

Total cosine-taper fraction of the gate window, as stored in the project’s GateShapePar1. 0.667 leaves a 0.333 flat top.

instrument_model_points_per_decade: int = 10#

SimPEG step-response density, in samples per decade. Ten matches the response grid and avoids asking SimPEG for redundant samples; raise it independently for convergence checks.

instrument_points_per_decade: int = 10#

Response grid density, in samples per decade. Ten matches the grid the instrument’s own processing works on, with local interpolation between those nodes.

receiver_location: ndarray = None#
receiver_orientation: str = 'z'#
receiver_type: str = 'b'#
source_current: float = 1.0#
source_location: ndarray = None#
source_moment: float | None = None#

Transmitter moment in A m^2, overriding current * turns * area. Set it to 1.0 for data already normalized by the transmitter moment, which is how TEMcompany instruments report dB/dt (V/A/m^4): dividing the measurement by the moment and then modelling with that moment counts it twice.

source_radius: float = 10.0#
source_turns: int = 1#
times: ndarray = None#
waveform_currents: ndarray | None = None#
waveform_period: float | None = None#

Half-period of the bipolar transmitter cycle, in seconds. Set it to model the earlier pulses of the train the way the reference implementation does.

waveform_repetitions: int = 3#

How many earlier half-cycles the repetition sums, three in the reference.

waveform_times: ndarray | None = None#

Turn-off waveform as (times, currents) nodes. A real ramp is not a step, and the earliest gates of a ground system sit only microseconds after it ends, which is exactly where the difference bites.

waveform_type: str = 'step_off'#
PyHydroGeophysX.forward.create_tdem_survey(times: ndarray, source_radius: float = 10.0, source_current: float = 1.0, source_location: ndarray | None = None, receiver_location: ndarray | None = None, receiver_orientation: str = 'z', waveform_type: str = 'step_off') → simpeg.electromagnetics.time_domain.Survey[source]#

Create a TDEM survey for 1D sounding.

Parameters:

  • times – Time channels (s)

  • source_radius – Loop radius (m)

  • source_current – Peak current (A)

  • source_location – Source center [x, y, z] (m)

  • receiver_location – Receiver position [x, y, z] (m)

  • receiver_orientation – Measurement component (‘x’, ‘y’, ‘z’)

  • waveform_type – Waveform type (‘step_off’, ‘ramp_off’)

Returns:

SimPEG TDEM Survey object

PyHydroGeophysX.forward.ertforandjac(fob: Any, rhomodel: Any, xr: Any) → Any[source]#

Forward model and Jacobian for ERT.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • rhomodel (pg.RVector) – Resistivity model.

  • xr (np.ndarray) – Log-transformed model parameter.

Returns:

Log-transformed forward response. J (np.ndarray): Jacobian matrix.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.ertforandjac2(fob: Any, xr: Any, mesh: Any) → Any[source]#

Alternative ERT forward model and Jacobian using log-resistivity values.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • xr (np.ndarray) – Log-transformed model parameter.

  • mesh (pg.Mesh) – Mesh for the forward model.

Returns:

Log-transformed forward response. J (np.ndarray): Jacobian matrix.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.ertforward(fob: Any, mesh: Any, rhomodel: Any, xr: Any) → Any[source]#

Forward model for ERT.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • mesh (pg.Mesh) – Mesh for the forward model.

  • rhomodel (pg.RVector) – Resistivity model vector.

  • xr (np.ndarray) – Log-transformed model parameter (resistivity).

Returns:

Log-transformed forward response. rhomodel (pg.RVector): Updated resistivity model.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.ertforward2(fob: Any, xr: Any, mesh: Any) → Any[source]#

Simplified ERT forward model.

Parameters:

  • fob (pygimli.ERTModelling) – ERT forward operator.

  • xr (np.ndarray) – Log-transformed model parameter.

  • mesh (pg.Mesh) – Mesh for the forward model.

Returns:

Log-transformed forward response.

Return type:

dr (np.ndarray)

PyHydroGeophysX.forward.forward_bodies(xobs: ~numpy.ndarray, yobs: ~numpy.ndarray, kind: str, bodies: ~typing.List[~typing.Dict[str, ~typing.Any]], field: ~typing.Dict[str, ~typing.Any] | None = None, log: ~typing.Callable[[str], None] = <function noop>) → ndarray[source]#

Sum the anomaly of a list of bodies. kind = ‘gravity’ or ‘magnetics’.

PyHydroGeophysX.forward.gravity_prism(xobs: ndarray, yobs: ndarray, body: Dict[str, Any]) → ndarray[source]#

Vertical gravity (mGal) of a right rectangular prism (Nagy 1966). z down.

PyHydroGeophysX.forward.gravity_sphere(xobs: ndarray, yobs: ndarray, body: Dict[str, Any]) → ndarray[source]#

Vertical gravity (mGal) of a buried sphere. z positive down, obs at z=0.

PyHydroGeophysX.forward.hydro_to_tdem(*args, **kwargs)[source]#

Deprecated alias for simulate_tdem_sounding_from_hydro().

PyHydroGeophysX.forward.magnetic_dipole(xobs: ndarray, yobs: ndarray, body: Dict[str, Any], field: Dict[str, Any]) → ndarray[source]#

Total-field magnetic anomaly (nT) of an induced/magnetized sphere (a dipole).

PyHydroGeophysX.forward.simulate_tdem_sounding_from_hydro(water_content: ndarray, porosity: ndarray, layer_thicknesses: ndarray, sigma_w: float | ndarray = 0.05, m: float | ndarray = 1.5, n: float | ndarray = 2.0, sigma_s: float | ndarray = 0.0, times: ndarray | None = None, source_radius: float = 10.0, noise_level: float = 0.05, seed: int | None = None, verbose: bool = False) → Tuple[ndarray, ndarray, ndarray, ndarray][source]#

Convert hydrological properties to TDEM response.

This function takes water content and porosity from hydrological models and computes the expected TDEM response using petrophysical relationships.

Parameters:

  • water_content – Water content for each layer (-)

  • porosity – Porosity for each layer (-)

  • layer_thicknesses – Thickness of each layer except bottom (m)

  • sigma_w – Pore water conductivity (S/m)

  • m – Cementation exponent

  • n – Saturation exponent

  • sigma_s – Surface conductivity (S/m)

  • times – Time channels (s), default is logspace(-5, -2, 31)

  • source_radius – Loop radius (m)

  • noise_level – Relative noise level for synthetic data

  • seed – Random seed for reproducibility

  • verbose – Print progress information

Returns:

Tuple of (noisy_data, clean_data, uncertainties, conductivity)

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