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¶

class PyFWI.fwi.FWI(d_obs, inpa, src, rec_loc, model_size, n_well_rec, chpr, components)¶

Bases: PyFWI.wave_propagation.wave_propagator

fprime(m0, freq)¶

fprime_single(m0, m_1, m1, freq)¶

lbfgs(m0, ITER, freqs, n_params=1, k0=0, k_end=1)¶

PyFWI.fwi_tools module¶

class PyFWI.fwi_tools.CPML(dh, dt, N, nd=2.0, Rc=0.001, nu0=2.0, nnu=2.0, alpha0=62.83185307179586, nalpha=1.0)¶

Bases: object

pml_prepare(V)¶

psi_reset(TNz, TNx)¶

class PyFWI.fwi_tools.CostFunction(cost_function_type='l2')¶

Bases: object

exponential_cost(dest, dobs)¶: based on https://stats.stackexchange.com/questions/154879/a-list-of-cost-functions-used-in-neural-networks-alongside-applications

l1(dest, dobs)¶

l2(dest0, dobs0)¶

l2_envelope(dest, dobs)¶: based on Wu et al., 2014, Seismic envelope inversion and modulation signal model Geiophysics

l2_hilbert(dest, dobs)¶

l2_intensity(dest, dobs)¶

static list2dict(x)¶

plot_amp_spectrum(case_a, case_b, dt, case_a_label=None, case_b_label=None)¶: to compare two amplitude spectrum

plot_trace(case_a, case_b, case_a_label=None, case_b_label=None)¶: to compare two trace

PyFWI.fwi_tools.adj_cost_preparation(res, fn, freq=False, order=None, axis=None, params_oh=None)¶

PyFWI.fwi_tools.adj_grad_lmd_to_vd(gvp, gvs, grho, lam, mu, rho)¶

grad_lmr_to_vd [summary]

[extended_summary]

Parameters

glam ([type]) – [description]
gmu ([type]) – [description]
grho ([type]) – [description]
lam ([type]) – [description]
mu ([type]) – [description]
rho ([type]) – [description]

Refrences:

Hu et al, 2021, Direct updating of rock-physics properties using elastice full-waveform inversion
Zhou and Lumely, 2021, Central-difference time-lapse 4D seismic full-waveform inversion

PyFWI.fwi_tools.adj_lowpass(x, highcut, fn, order, axis=1)¶

PyFWI.fwi_tools.cost_function(d_est, d_obs)¶

PyFWI.fwi_tools.cost_preparation(dpre, dobs, fn, freq=False, order=None, axis=None, params_oh=None)¶

PyFWI.fwi_tools.cost_seismic(d_pre0, d_obs0, fun, fn=None, freq=False, order=None, axis=None, sourc_weight=False, ns=None, nr=None, params_oh=None)¶

PyFWI.fwi_tools.dict_diff(dict1, dict2, positivity=False)¶

dict_diff subtracts the contents of two dictionaries

This function is used to subtract the parameters of a dictionary with the same parameter in another dictionary.

Parameters

dict1 (dict) – The first dictionary
dict2 (dict) – The second dictionary
positivity (boolean, optional) – A boolean variable to specify if the used wants
False. (to filter out the negative differences. Defaults to) –

Returns

A dictionary containing the common parameters of `dict1` and `dict2`, but their difference.

Return type

dic

PyFWI.fwi_tools.dict_summation(dict1, dict2, division=1.0)¶

dict_summation add the contents of two dictionaries

This function is used to add the parameters of a dictionary with the same parameter in another dictionary.

Parameters

dict1 (dict) – The first dictionary
dict2 (dict) – The second dictionary
division (float, optional) – In cas if user wants to devide the summation to a number (e.g. averaging)
1.0. (of amplifying the result. Defaults to) –

Returns

A dictionary containing the common parameters of `dict1` and `dict2`, but their summation.

Return type

dic

PyFWI.fwi_tools.expand_model(parameter, TNz, TNx, n_pml=10)¶

This function make room around the ‘parameter’ to stick the pml layer.

Parameters

parameter (float) – Matrix of property that we are going to consider pml around.
TNz (int) – Number of samples in z-direction (n_z + 2 * n_pml).
TNx (int) – Number of samples in x-direction (n_x + 2 * n_pml).
n_pml (int, optional = 10) – Number of pml layer

Returns

nu – A matrix with the size of [TNz, TNx] with zero value everywhere excpet in center which consisting the model.

Return type

float

PyFWI.fwi_tools.grad_lmd_to_vd(glam, gmu, grho, lam, mu, rho)¶

grad_lmr_to_vd [summary]

[extended_summary]

Parameters

glam ([type]) – [description]
gmu ([type]) – [description]
grho ([type]) – [description]
lam ([type]) – [description]
mu ([type]) – [description]
rho ([type]) – [description]

Refrences:

Hu et al, 2021, Direct updating of rock-physics properties using elastice full-waveform inversion
Zhou and Lumely, 2021, Central-difference time-lapse 4D seismic full-waveform inversion

PyFWI.fwi_tools.grad_pcs2vd(gphi0, gcc0, gsw0, cc, phi, sw)¶

grad_pcs2vd [summary]

[extended_summary]

Parameters

gvp ([type]) – [description]
gvs ([type]) – [description]
grho ([type]) – [description]
cc ([type]) – [description]
rho_c ([type]) – [description]
rho_q ([type]) – [description]
phi ([type]) – [description]
rho_w ([type]) – [description]
rho_g ([type]) – [description]
rho_f ([type]) – [description]

Returns

[description]

Return type

[type]

Refrences:

Hu et al, 2021, Direct updating of rock-physics properties using elastice full-waveform inversion
Zhou and Lumely, 2021, Central-difference time-lapse 4D seismic full-waveform inversion

PyFWI.fwi_tools.grad_vd2pcs(gvp0, gvs0, grho0, cc, phi, sw)¶

grad_vd2pcs [summary]

[extended_summary]

Parameters

gvp ([type]) – [description]
gvs ([type]) – [description]
grho ([type]) – [description]
cc ([type]) – [description]
rho_c ([type]) – [description]
rho_q ([type]) – [description]
phi ([type]) – [description]
rho_w ([type]) – [description]
rho_g ([type]) – [description]
rho_f ([type]) – [description]

Returns

[description]

Return type

[type]

Refrences:

Hu et al, 2021, Direct updating of rock-physics properties using elastice full-waveform inversion
Zhou and Lumely, 2021, Central-difference time-lapse 4D seismic full-waveform inversion

PyFWI.fwi_tools.inpa_generator(vp, sdo, fn, **kwargs)¶

PyFWI.fwi_tools.inpa_loading(path)¶

inpa_loading lad the INPA file

[extended_summary]

Parameters: path ([type]) – [description]
Returns: input of FWI program
Return type: inpa (dict)

PyFWI.fwi_tools.lowpass(x1, highcut, fn, order=1, axis=1, show=False)¶

PyFWI.fwi_tools.modeling_model(model, med_type)¶

PyFWI.fwi_tools.pcs_dict2vec(m0)¶

PyFWI.fwi_tools.pml_counstruction(TNz, TNx, dh, n_pml=10, pml_r=1e-5)¶

PML construction generate two matrices for x- and z-directions with the size of velocity model plus number of pml samples in each direction.

dx_pml and dz_pml are obtained based on Gao et al., 2017, Comparison of artiﬁcial absorbing boundaries for acoustic wave equation modelling.

Parameters

TNz (int) – Number of samples in z-direction (n_z + 2 * n_pml).
TNx (int) – Number of samples in x-direction (n_x + 2 * n_pml).
dh (float) – Spatial ampling rate in x-direction.
n_pml (int, optional = 10) – Number of pml layer
pml_r (float, optional = 1e-5) – Theoretical reﬂection coefﬁcient.

Returns

dx_pml (float) – A matrix with the size of [TNz, TNx] with zero value everywhere excpete inside PML in right and left of model.
dz_pml (float) – A matrix with the size of [TNz, TNx] with zero value everywhere excpet inside PML in above and bottom of model.

References

[1] Gao et al., 2017, Comparison of artiﬁcial absorbing boundaries for acoustic wave equation modelling, Exploration Geophysics, 2017, 48, 76–93.

[2] Araujo and Pestana, 2020, Perfectly matched layer boundary conditions for the second-order acoustic wave equation solved by the rapid expansion method, Geophysical Prospecting, 2020, 68, 572–590.

PyFWI.fwi_tools.pml_delta_calculation(n_pml, dh, pml_r)¶

This function generates delta vector for PML construction function which put this vector around the model matrices.

dx_pml and dz_pml are obtained based on Gao et al., 2017, Comparison of artiﬁcial absorbing boundaries for acoustic wave equation modelling.

Warns

TODO (I have to add dz as well)

Parameters

dh (float) – Sampling rate in x-direction.
n_pml (int, optional = 10) – Number of pml layers
pml_r (float, optional = 1e-5) – Theoretical reﬂection coefﬁcient.

Returns

delta – A vector containing the absorbant value for putting in absorbant layer

Return type

float

References

[1] Gao et al., 2017, Comparison of artiﬁcial absorbing boundaries for acoustic wave equation modelling, Exploration Geophysics, 2017, 48, 76–93.

[2] Araujo and Pestana, 2020, Perfectly matched layer boundary conditions for the second-order acoustic wave equation solved by the rapid expansion method, Geophysical Prospecting, 2020, 68, 572–590.

class PyFWI.fwi_tools.recorder(nt, rec_loc, ns, dh)¶

Bases: object

acquire()¶

PyFWI.fwi_tools.residual(d_est, d_obs)¶

PyFWI.fwi_tools.source_weighting(d_pre, d_obs, ns, nr)¶

PyFWI.fwi_tools.svd_reconstruction(m, begining_component, num_components)¶

PyFWI.fwi_tools.vec2pcs_dict(m0, nz, nx)¶

PyFWI.fwi_tools.vec2vel_dict(m0, nz, nx)¶

PyFWI.fwi_tools.vel_dict2vec(m0)¶

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¶

class PyFWI.optimization.FWI(d_obs, inpa, src, rec_loc, model_size, n_well_rec, chpr, components)¶

Bases: PyFWI.wave_propagation.wave_propagator

fprime(m0, m1, freq)¶

fprime_single(m0, m_1, m1, freq)¶

gauss_newton(m0, iter, freqs)¶

parameter_optimization(m_opt, m1, p, rms, grad, alpha, freq)¶

steepest_descent(m0, iter, freqs)¶

PyFWI.optimization.linesearch(fun, fprime, xk, pk, gk=None, fval_old=None, f_max=50, alpha0=None, show=False, min=1e-08, bond=[- inf, inf], args=())¶

PyFWI.optimization.truncated(FO_waves, W, m0, grad0, m1, iter)¶

PyFWI.processing module¶

class PyFWI.processing.Gain(t, dt, nt)¶

Bases: object

property test¶

time_linear(show_gain=False)¶

time_linear generates a linear gain function related to time

This function generates a linear gain function with time

Parameters: show_gain (bool, optional) – If we need to plot the gain function. Defaults to False.
Returns: A class for applying the processing method.
Return type: GF (class)

class PyFWI.processing.Gain_function(t, dt, nt)¶

Bases: PyFWI.processing.Gain

apply(data, show=False)¶

apply applies the gain function on the data

apply applies the made gain on the data. It can show the original and gained data as well.

Parameters

data (list) – A list containing the seismic datafor different component
show (bool, optional) – The option to let user to ask to show the seismic sections before and after gain. Defaults to False.

Returns

Gained data

Return type

gained_data [list]

class PyFWI.processing.derivatives(order)¶

Bases: object

property c1¶

property c2¶

property c3¶

property c4¶

dot_product_test_derivatives()¶

dt_computation(vp_max, dx, dz=None)¶: ref: Bai et al, 2013

dxm(x, dx)¶

dxp(x, dx)¶

dzm(x, dx)¶

dzp(x, dx)¶

property order¶

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

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

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.

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.

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¶

class PyFWI.wave_propagation.wave_preparation(inpa, src, rec_loc, model_size, n_well_rec=0, chpr=10, components=4)¶

Bases: object

adjoint_buffer_preparing()¶

elastic_buffers(model)¶: Model hast contain vp, vs, and rho

gradient_reading()¶

initial_wavefield_plot(model, plot_type='Forward')¶

A function to initialize the the plot for wave propagation visulizing

Parameters: plot_type (string optional = "Forward") – Specify if we want to show Forward modelloing or Backward.

kernel_caller()¶

This function is used to specify the constants for use in openCl’s files and choose the asked openCl source to run based on the assumptions of problem.

The coeeficients are based on Lavendar, 1988 and Hasym et al., 2014.

Returns: kernel_source – macro plus the openCl’s source.
Return type: str

make_residual(res, s, t)¶

This function reads the inject the residual to residual buffer based on source and the time step.

Parameters

res (list) – list containing the residual of all parameters
s (int) – Number of current acive source.
t (float) – Current time step.

make_seismogram(s, t)¶

This function read the seismogram buffer and put its value in the right place in main seismogram matrix based on source and the time step.

Parameters

s (int) – Number of current acive source.
t (float) – Current time step.

plot_propagation(wave1, t, wave2=None)¶

This function is used to shpw the propagation wave with time.

Parameters

wave1 (float32) – The wave that we are going to show
t (float32) – Time step
wave2 (flaot32, optional = None) – The second wave which is used when we want to show the propagation of backward wave as wave1 and adjoint wave as wave2.

pml_preparation(v_max)¶

class PyFWI.wave_propagation.wave_propagator(inpa, src, rec_loc, model_size, n_well_rec=None, chpr=10, components=4)¶

Bases: PyFWI.wave_propagation.wave_preparation

forward_modeling(model0, show=False, W=None, grad=None)¶

forward_propagator(model, W=None, grad=None)¶

This function is in charge of forward modelling for acoustic case

Parameters: model (dictionary) – A dictionary containing p-wave velocity and density

gradient(res, show=False, Lam=None, grad=None, parameterization='dv')¶

PyFWI package¶

Submodules¶

PyFWI.acquisition module¶

PyFWI.fwi module¶

PyFWI.fwi_tools module¶

PyFWI.model_dataset module¶

PyFWI.optimization module¶

PyFWI.processing module¶

PyFWI.rock_physics module¶

PyFWI.seiplot module¶

PyFWI.seismic_io module¶

PyFWI.wave_propagation module¶

Module contents¶