PyFWI package

Submodules

PyFWI.acquisition module

PyFWI.acquisition.AcqParameters(ns, rec_dis, offsetx, depth, dh, sdo, acq_type)

A function to define the acquisition based on user’s demand

Parameters

INPA (dictionary) –

A dictionnary containing required parameters for iversion, at least:

  • ns: Number of sources

  • rec_dis: Distance between receivers

  • offsetx: Length of acquisition in x-direction

  • depth: Depth of acquisition

  • dh: spatial sampling rate

  • sdo: Spatial differentiation order

  • acq_type: Type of acquisition (0: crosswell, 1: surface, 2: both)

Returns

  • src_loc (float32) – Location of sources

  • rec-loc (float32) – Location of receivers

class PyFWI.acquisition.Source(src_loc, dh, dt)

Bases: object

A class for defining different types of sources.

Parameters

  • src_loc (float32) – location of sources.

  • dh (float) – Spatial sampling rate.

  • dt (float) – Temporal sampling rate

Ricker(fdom)

Amethod to generate Ricker wavelet.

Parameters

fdom (float32) – Dominant frequency of wavelet

delta()

A method to generate spike.

Parameters

fdom (float32) – Dominant frequency of wavelet

PyFWI.acquisition.SurfaceSeismic(ns, rec_dis, offsetx, offsetz, dh, sdo)

A function to design a surface seismic acquisition

Parameters

  • ns (int) – Number of sources

  • rec_dis (float32) – Distance between receivers

  • offsetx (flloat32) – Length of survey in x-direction

  • offsetz (float32) – Depth of survey

  • dh (float32) – Spatial sampling rate

  • sdo ({2, 4, 8}) – Spatial order of finite difference method

Returns

  • src_loc (float32) – Location of sources

  • rec_loc (float32) – Location of receivers

PyFWI.acquisition.acquisition_plan(ns, nr, src_loc, rec_loc, acq_type, n_well_rec, dh)

acquisition_plan generates the matrix of acquisition plan

[extended_summary]

Parameters

  • ns ([type]) – [description]

  • nr ([type]) – [description]

  • src_loc ([type]) – [description]

  • rec_loc ([type]) – [description]

  • acq_type ([type]) – [description]

  • n_well_rec ([type]) – [description]

  • dh ([type]) – [description]

Returns

[description]

Return type

[type]

PyFWI.acquisition.crosswell(ns, rec_dis, offsetx, offsetz, dh, sdo)

A function to design a crosswell acquisition

Parameters

  • ns (int) – Number of sources

  • rec_dis (float32) – Distance between receivers

  • offsetx (flloat32) – Length of survey in x-direction

  • offsetz (float32) – Depth of survey

  • dh (float32) – Sampling rate

  • sdo ({2, 4, 8}) – Spatial order of finite difference method

Returns

  • src_loc (float32) – Location of sources

  • rec_loc (float32) – Location of receivers

PyFWI.acquisition.discretized_acquisition_plan(data_guide, dh, npml=0)

discretized_acquisition_plan discretizes the matrix of acquisition plan

[extended_summary]

Parameters

  • data_guide ([type]) – [description]

  • dh ([type]) – [description]

  • npml (int, optional) – [description]. Defaults to 0.

Returns

[description]

Return type

[type]

PyFWI.fwi module

PyFWI.fwi_tools module

PyFWI.model_dataset module

class PyFWI.model_dataset.Circular(name)

Bases: object

Hu_circles(vintage, smoothing)

Hu_circles a model including porosity, clay content, and saturation.

This method creates a model including porosity, clay content, and saturation. It is used in a paper published in 2021 in Geophysics by Qi Qu and his collegues. If you use non-default values, new model with the same structure and new values will be generated.

Parameters

  • rho (dictionary, optional) – A dictionary containing the density of quartz, clay, water, and hydrocarbon. Defaults to None.

  • prop_back (dictionary, optional) – A dictionary containing background properties (porosity, clay content, and saturation). Defaults to None.

  • prop_circle (dictionary, optional) – A dictionary containing properties in the circles (porosity, clay content, and saturation). Defaults to None.

  • nz (int, optional) – Number of saples in z-direction (rows). Defaults to 100.

  • nx (int, optional) – Number of saples in x-direction (column). Defaults to 100.

  • r (int, optional) – Radius of the circles. Defaults to 8.

  • monitor (bool, optional) – Specify if you are looking for monitor model. Defaults to False.

Returns

A dictionary containing the created model.

Return type

model (dictionary)

Reference:

Hu, Q., S. Keating, K. A. Innanen, and H. Chen, 2021, Direct updating of rock-physics properties using elastic full-waveform inversion: Geophysics, 86, 3, MR117-MR132, doi: 10.1190/GEO2020-0199.1.

louboutin(vintage, smoothing)

louboutin Generate perturbation model based on only vp.

[extended_summary]

Returns

[description]

Return type

[type]

perturbation_dv(vintage, smoothing)

perturbation_dv creates perturbation model in different locations

perturbation_dv creates perturbation model in different locations based on vp, vs, density

Returns

[description]

Return type

[type]

yang(vintage, smoothing)

Yang et al., 2018 for Truncated Newton method.

[extended_summary]

Returns

[description]

Return type

[type]

class PyFWI.model_dataset.Laminar(name)

Bases: object

Hu_laminar(vintage, smoothing)
dupuy(vintage, smoothing)
class PyFWI.model_dataset.ModelGenerator(name)

Bases: PyFWI.model_dataset.Circular, PyFWI.model_dataset.Laminar

marmousi(vintage, smoothing)
show(property=['vp'])
PyFWI.model_dataset.add_anomaly(model, anomaly, x, z, dx, dz, height, type='circle')

add_anomaly adds anomaly to the previously created model.

This mathod add an anomally to the Earth mode that is already createad.

Parameters

  • model (float) – The previously created model.

  • anomaly (float) – The properties of the anomaly

  • x ([type]) – x-location of the anomaly

  • z ([type]) – z-location of the anomaly

  • width ([type]) – Width of the anomaly

  • height ([type]) – Height of the anomaly

  • type (str, optional) – The shape of the anomaly. Defaults to “circle”.

Returns

The new model.

Return type

model (dict)

PyFWI.model_dataset.add_circle(model, circle_prop, r, cx, cz)

add_circle adds a circle to the model

This function generates a circle in the model.

Parameters

  • model (float) – Already created model.

  • circle_prop (float) – Property of the circle.

  • r (int) – Radius of the circle

  • cx (int) – x_location of the center

  • cz (int) – z-location of the center

Returns

Return the model.

Return type

model(dict)

PyFWI.model_dataset.add_layer(model, property, lt, lb, rt=None, rb=None)

add_layer add alyer to the model

This function add a layer to the mdoel

Parameters

  • model (dict) – Already created model.

  • property (dict) – Property of the new layer

  • lt (array, int) – Sample number ([x ,z]) of the top of the layer in the most left part

  • lb (array, int) – Sample number ([x ,z]) of the bottom of the layer in the most left part

  • rt (array, int) – Sample number ([x ,z]) of the top of the layer in the most right part

  • rb (array, int) – Sample number ([x ,z]) of the bottom of the layer in the most right part

  • #TODO – to develop for dipping layers

Returns

Return the model.

Return type

model(dict)

PyFWI.model_dataset.background(size, params)

add_layer genearte a layer of property.

This method generates one layer with property “bp”

Parameters

bp (dict) – Background property

PyFWI.model_dataset.model_resizing(model0, bx=None, ex=None, bz=None, ez=None, ssr=(1, 1))
PyFWI.model_dataset.model_smoother(model, smooting_value)
PyFWI.model_dataset.pcs_perturbation(rho=None, prop_back=None, prop_circle=None, nz=100, nx=100, r=8, monitor=False)

pcs_perturbation a model including porosity, clay content, and saturation.

This function creates a model including porosity, clay content, and saturation. It is used in a paper published in 2021 in Geophysics by Qi Qu and his collegues. If you use non-default values, new model with the same structure and new values will be generated.

Parameters

  • rho (dictionary, optional) – A dictionary containing the density of quartz, clay, water, and hydrocarbon. Defaults to None.

  • prop_back (dictionary, optional) – A dictionary containing background properties (porosity, clay content, and saturation). Defaults to None.

  • prop_circle (dictionary, optional) – A dictionary containing properties in the circles (porosity, clay content, and saturation). Defaults to None.

  • nz (int, optional) – Number of saples in z-direction (rows). Defaults to 100.

  • nx (int, optional) – Number of saples in x-direction (column). Defaults to 100.

  • r (int, optional) – Radius of the circles. Defaults to 8.

  • monitor (bool, optional) – Specify if you are looking for monitor model. Defaults to False.

Returns

A dictionary containing the created model.

Return type

model (dictionary)

Reference:

Hu, Q., S. Keating, K. A. Innanen, and H. Chen, 2021, Direct updating of rock-physics properties using elastic full-waveform inversion: Geophysics, 86, 3, MR117-MR132, doi: 10.1190/GEO2020-0199.1.

PyFWI.optimization module

PyFWI.processing module

PyFWI.rock_physics module

class PyFWI.rock_physics.Density

Bases: object

static effective_density(phi, rho_f, rho_s)
static fluid(r_hydro, rho_w, sw)

fluid [summary]

[extended_summary]

Parameters

  • r_hydro ([type]) – [description]

  • rho_w ([type]) – [description]

  • sw ([type]) – [description]

Returns

Density of fluid

Return type

rho_f [type]

gardner(vp, units='metric')

gardner method to estimate the density

This mdethod estimate density of a model based on P-wave velocity. It uses the Gardner’s eqution.

Parameters

  • vp (float) – P-wave velocity

  • units (str, optional) – Specify the system of the units fo measurements (Metric or Imperial) . Defaults to “metric”.

Returns

density

Return type

rho

static matrix(rho_clay, cc, rho_q, **kwargs)

matrix [summary]

[extended_summary]

Parameters

  • rho_clay ([type]) – [description]

  • cc ([type]) – [description]

  • rho_q ([type]) – [description]

Returns

[description]

Return type

[type]

rho_from_pcs(rho_c, rho_q, rho_w, rho_g, cc, sw, phi)

This function calculate density from Porosity, clay content, and water Saturation

rho_c:

Density of clay

rho_q:

Density of quartz

rho_w:

Density of water

rho_g:

Density of gas

cc:

clay content

sw:

water saturation

phi:

Porosity

rho: float

Effective density

PyFWI.rock_physics.Han(phi, cc, a1=5.5, a2=6.9, a3=2.2, b1=3.4, b2=4.7, b3=1.8)

Han estimates velocity based on porosity and clasy content

Han found empirical regressions relating ultrasonic (laboratory) velocities to porosity and clay content

Parameters

  • phi ([type]) – [porosity

  • cc ([type]) – clay content

  • a1 (float, optional) – Constant value for Vp. Defaults to 5.77.

  • a2 (float, optional) – Constant value for Vp. Defaults to 6.94.

  • a3 (float, optional) – Constant value for Vp. Defaults to 1.728.

  • b1 (float, optional) – Constant value for Vs. Defaults to 5.77.

  • b2 (float, optional) – Constant value for Vs. Defaults to 6.94.

  • b3 (float, optional) – Constant value for Vs. Defaults to 1.728.

Returns

P-wave velocity (km/s) vs = S-wave velocity (km/s)

Return type

vp

References

  1. Hu et al, 2021, Direct updating of rock-physics properties using elastice full-waveform inversion

  2. Mavko, G., Mukerji, T., & Dvorkin, J., 2020, The rock physics handbook. Cambridge university press.

class PyFWI.rock_physics.Lamb

Bases: object

vp_rho_mu(rho, vp=None, mu=None)
class PyFWI.rock_physics.Mu

Bases: object

vs_rho(vs, rho=None)

vs_rho generate mu

This function add mu to to the imported model based on S-wave velocity and density.

Parameters

  • vs (float or dict) – S-wave velocity. if dict, it has to contain value for density.

  • rho (float, option) – Density

Returns

Shear modulus

Return type

mu

class PyFWI.rock_physics.ShearVelocity

Bases: object

Han(phi, cc, **kwargs)

Han calulates vs based on Han empirical model.

Han calulates vs based on Han empirical model.

Parameters

  • phi ([type]) – Porosity

  • cc ([type]) – Clay content

Returns

S-wave velocity

Return type

vp

poisson_ratio_vs(vp, sigma=0.25)

poisson_ratio_vs calculates the shear velocity.

Calculates the shear velocity based on Poisson’s ration.

Parameters

  • vp (float) – P-wave velocity.

  • sigma (float, optional) – Poisson’s ration. It could be None if parameter “model” has this property. Defaults to None.

Returns

The input model and shear velocity is added.

Return type

vs

PyFWI.rock_physics.biot_gassmann(phi, k_f, k_s, k_d)
PyFWI.rock_physics.delta_biot_gassmann(phi, k_f, k_s, k_d)
PyFWI.rock_physics.drained_moduli(phi, k_s, g_s, cs)
PyFWI.rock_physics.error_lack_of_data()
class PyFWI.rock_physics.p_velocity

Bases: object

Han(phi, cc, **kwargs)

Han calulates vp based on Han empirical model.

Han calulates vp based on Han empirical model.

Parameters

  • phi ([type]) – Porosity

  • cc ([type]) – Clay content

Returns

P-wave velocity

Return type

vp

gardner(units='metric')
lam_mu_rho(lam, mu, rho)
PyFWI.rock_physics.reverse_Han(vp, vs, a1=5.5, a2=6.9, a3=2.2, b1=3.4, b2=4.7, b3=1.8)
PyFWI.rock_physics.voigt_berie(k_l, rho_l, k_g, rho_g, s_g)

PyFWI.seiplot module

PyFWI.seiplot.earth_model(model, keys=[], offset=None, depth=None, **kwargs)

earth_model show the earth model.

This function is provided to show the earth models.

Parameters

  • model (Dictionary) – A dictionary containing the earth model.

  • keys (list, optional) – List of parameters you want to show. Defaults to [].

Returns

The figure class to which the images are added for furthur settings like im.set-clim().

Return type

fig (class)

PyFWI.seiplot.gn_plot(p, grad, nz, nx)
PyFWI.seiplot.seismic_section(ax, data, x_axis=None, t_axis=None, aspect_preserving=False, **kargs)

PyFWI.seismic_io module

PyFWI.seismic_io.load_mat(path)

This function load python dictionary as a .mat file.

Parameters

path (String) – The path to save the data.

PyFWI.seismic_io.load_pkl(file_path)

load_pkl loads pkl file.

load_pkl loads pkl file.

Parameters

file_path (string) – Path of file to be loaded.

Returns

Loaded file.

Return type

output

PyFWI.seismic_io.read_segy(path)

A function to load segy file.

Parameters

path – The path of segy file.

Returns

The data stored in segy.

Return type

data

PyFWI.seismic_io.save_mat(path, **kwargs)

This function save python dictionary as a .mat file.

Parameters

  • path (String) – The path to save the data.

  • unique (Boolean) – If true, it will add current date and time to the name of folder

  • **kwargs (type) – Dictionaries containing the data.

PyFWI.seismic_io.save_pkl(path, **kwargs)

save_pkl saves pkl file.

save_pkl saves file with pkl format. That is better than .mat file for preserving the structure of dictionaries.

Parameters

  • path (string) – path to save the file(s).

  • **kwargs (data) – Variable(s) to be saved.

  • the (A boolean argument with name of "unique" can be given to make) –

  • data. (path based on the) –

PyFWI.wave_propagation module

Module contents