pyavo.seismodel package

Submodules

pyavo.seismodel.angle_stack module

Functions to import segy data and compute AVO attributes from Near and Far Stacks.

pyavo.seismodel.angle_stack.avo_attributes(horizon_data: str, near_stack: xarray.core.dataarray.DataArray, far_stack: xarray.core.dataarray.DataArray, theta_near: int, theta_far: int, TWT_slice: tuple, XL_slice: tuple, inline: int, well_XL=None, well_display=True, robust=True, interpolation='spline16', add_colorbar=True, cmap='jet_r') → dict

Computes intercept, gradient values and display images of AVO attributes & crossplot.

Parameters

  • horizon_data – file path, any valid string path to a txt file is acceptable.

  • near_stack – DataArray of Near angle stack

  • far_stack – DataArray of Far angle stack

  • theta_near – Incidence angle of near stack

  • theta_far – Incidence angle of far stack

  • TWT_slice – Two-way time slice

  • XL_slice – Crossline time slice

  • inline – Inline number

  • well_XL – Well crossline number to define well position

Returns

c: Intercept, m: Gradient

pyavo.seismodel.angle_stack.nf_attributes(horizon_data: str, near_stack: xarray.core.dataarray.DataArray, far_stack: xarray.core.dataarray.DataArray, TWT_slice: tuple, XL_slice: tuple, inline: int, well_XL=None, well_display=True, robust=True, interpolation='spline16', add_colorbar=True, cmap='jet_r')

Display images of near and far angle stack attributes. Allowed file extension for horizon_data is .txt. Both near and far stack data are read as DataArray to enhance computation speed.

Parameters

  • horizon_data – file path, any valid string path to a txt file is acceptable.

  • near_stack – DataArray of Near angle stack

  • far_stack – DataArray of Far angle stack

  • TWT_slice – Two-way time slice

  • XL_slice – Crossline time slice

  • inline – Inline number

  • well_XL – Well crossline number to define well position

  • well_display – Show well trajectory on plot, vertical

Reference:

Avseth, P., Mukerji, T., & Mavko, G., 2010. Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk.Cambridge university press.

pyavo.seismodel.angle_stack.nfstack(horizon_data: str, near_stack: xarray.core.dataarray.DataArray, far_stack: xarray.core.dataarray.DataArray, inline: int, robust=True, interpolation='spline16', cmap='RdBu', well_XL=None, well_display=True)

Display images of Near and Far angle stacks from DataArray. Allowed file extension for horizon_data is .txt. Both near and far stack data are read as DataArray to enhance computation speed.

Parameters

  • horizon_data – file path, any valid string path to a txt file is acceptable.

  • near_stack – DataArray of Near angle stack

  • far_stack – DataArray of Far angle stack

  • inline – Inline number

  • well_XL – Well crossline number to define well position

  • well_display – Show well trajectory on plot, vertical

pyavo.seismodel.angle_stack.read_segy(filepath: str, byte_il=14, byte_xl=21, ignore_geometry=False) → xarray.core.dataarray.DataArray

Read a segyfile into a Data array.

Parameters

  • filepath – filepath, any valid string path is acceptable

  • byte_il – inline byte

  • byte_xl – crossline byte

  • ignore_geometry – Opt out on building geometry information, useful for e.g. shot organised files. Defaults to False.

Returns

data_array: xarray

pyavo.seismodel.rpm module

Functions to create rock physics models and apply them to well log analysis to quantify seismic responses.

Using equations http://www.subsurfwiki.org/wiki/Elastic_modulus from Mavko, G, T Mukerji and J Dvorkin (2003), The Rock Physics Handbook, Cambridge University Press

pyavo.seismodel.rpm.cp_model(k_min: float, mu_min: float, phi: float, cp=0.4) → tuple

Build rock physics models using critical porosity concept.

Parameters

  • k_min – Bulk modulus of mineral (Gpa)

  • mu_min – Shear modulus of mineral (Gpa)

  • phi – Porosity (fraction)

  • cp – critical porosity, default is 40%

Returns

Bulk and Shear modulus of rock frame

Reference

Critical porosity, Nur et al. (1991, 1995) Mavko, G, T. Mukerji and J. Dvorkin (2009), The Rock Physics Handbook: Cambridge University Press. p.353

pyavo.seismodel.rpm.hz_mindlin(k_min, mu_min, phi: numpy.ndarray, cd_num=8.6, cp=0.4, P=10, f=1) → tuple

Computes the bulk modulus and shear modulus of the Hertz-Mindlin rock physics model.

Parameters

  • k_min – bulk modulus of mineral (Gpa)

  • mu_min – shear modulus of mineral (Gpa)

  • phi – porosity (fraction) expressed as vector values

  • cd_num – coordination number

  • cp – critical porosity (random close pack of well-sorted rounded quartz grains)

  • P – Confining pressure/rock stress (Mpa)

  • f – shear modulus correction factor 0 = dry frictionless packing 1 = dry pack with perfect adhesion

Returns

Bulk modulus and Shear modulus

Reference

The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media - Gary M. Mavko, Jack Dvorkin, and Tapan Mukerji

pyavo.seismodel.rpm.plot_log(data: pandas.core.frame.DataFrame, vsh: pandas.core.series.Series, vp: pandas.core.series.Series, vs: pandas.core.series.Series, imp: pandas.core.series.Series, vp_vs: pandas.core.series.Series, phi: pandas.core.series.Series, rho: pandas.core.series.Series, z_init: float, z_final: float, shale_cutoff: float, sand_cutoff: float)

Explore key input logs for Rock physics modeling. Generates a log plot of the shale volume, acoustic impedance, Vp/Vs and a cross plot of Porosity against P-wave velocity and acoustic impedance against Vp/VS.

Parameters

  • data – pandas DataFrame

  • vsh – Shale volume as input from vshale log

  • vp – Compressional wave velocity log

  • vs – Shear wave velocity log

  • imp – Acoustic impedance from acoustic impedance log

  • vp_vs – Vp/Vs ratios

  • phi – porosity values form PHI log

  • rho – density values from RHOB log

  • z_init – minimum depth from depth log

  • z_final – maximum depth from depth log

  • shale_cutoff – Shale cutoff

  • sand_cutoff – Sand cutoff

Returns

log plots(Vp/Vs, AI, Vsh) and cross plots(AI,Vp/Vs & PHI,Vp)

pyavo.seismodel.rpm.plot_rpm(df: pandas.core.frame.DataFrame, k_qtz: float, mu_qtz: float, k_sh: float, mu_sh: float, rho_sh: float, rho_qtz: float, k_br: float, rho_br: float, phi: numpy.ndarray, NG: numpy.ndarray, vsh: pandas.core.series.Series, z_init: float, z_final: float, sand_cut: float, shale_cut: float, P: Union[int, float], cp_ssm: float, cn_ssm: float, cp_stm: float, cn_stm: float, eff_phi: pandas.core.series.Series, vp: pandas.core.series.Series)

Plot soft sand and stiff sand rock physics models for different mineralogies.

Parameters

  • df – Pandas DataFrame

  • k_qtz – Bulk modulus of quartz (GPa)

  • mu_qtz – Shear modulus of quartz (GPa)

  • k_sh – Bulk modulus of shale (Gpa)

  • mu_sh – Shear modulus of shale (Gpa)

  • rho_sh – Density of shale (g/cc)

  • rho_qtz – Density of quartz (g/cc)

  • k_br – Bulk modulus of brine (GPa)

  • rho_br – Density of brine (g/cc)

  • phi – Porosity (fraction)

  • NG – Net-to-Gross

  • vsh – Volume of shale from VSH log

  • z_init – minimum depth withing range of input depth log (ft)

  • z_final – maximum depth withing range of input depth log (ft)

  • sand_cut – sand cutoff

  • shale_cut – shale cutoff

  • P – Confining pressure (Mpa)

  • cp_ssm – Critical porosity for soft sand model

  • cn_ssm – Coordination number for soft sand model

  • cp_stm – Critical porosity for stiff sand model

  • cn_stm – Coordination number for stiff sand model

  • eff_phi – effective porosity values from porosity log (fraction)

  • vp – P-wave velocity from VP log (m/s)

Returns

Plot of soft sand and stiff sand models with curves showing varying net-to-gross.

pyavo.seismodel.rpm.plot_rpt(model='soft', fluid='oil', display=True, vsh=0.0, k_sh=15, k_qtz=37, mu_sh=5, mu_qtz=44, rho_sh=2.8, rho_qtz=2.6, k_gas=0.06, k_oil=0.9, k_br=2.8, rho_gas=0.2, rho_oil=0.8, rho_br=1.1, cp=0.4, cd_num=8.6, P=10, f=1)

Plot RPTs showing variations in porosity, mineralogy and fluid content defined by water saturation.

Parameters

  • model – type of rock physics model, ‘soft’ or ‘stiff’. default = ‘soft’

  • fluid – desired fluid after Gassmann’s fluid substitution, default = ‘oil’

  • display – show plot with calculated parameters, default = True

  • vsh – volume of shale, default = 0

  • k_sh – bulk modulus of shale, default = 15GPa

  • k_qtz – bulk modulus of quartz, default = 37GPa

  • mu_sh – shear modulus of shale, default = 5GPa

  • mu_qtz – shear modulus of quartz, default = 44GPa

  • rho_sh – density of shale, default = 2.8g/cc

  • rho_qtz – density of quartz, default = 2.6g/cc

  • k_gas – bulk modulus of gas (GPa), default = 0.06GPa

  • k_oil – bulk modulus of oil (GPa), default = 0.9GPa

  • k_br – bulk modulus of brine (Gpa), default = 2.8GPa

  • rho_gas – density of gas (g/cc), default = 0.2g/cc

  • rho_oil – density of oil (g/cc), default = 0.8g/cc

  • rho_br – density of brine (g/cc) default = 1.1g/cc

  • cp – critical porosity, default = 0.4

  • cd_num – coordination number, default = 8.6

  • P – confining pressure(MPa), default = 10MPa

  • f – shear modulus correction factor, default=1

Returns

plot of RPT with labels, Array of Acoustic impedance and Vp/Vs

Reference:

Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk (Per Avseth, T. Mukerji and G.mavko, 2015)

pyavo.seismodel.rpm.soft_sand(k_min: float, mu_min: float, phi: numpy.ndarray, cd_num=8.6, cp=0.4, P=10, f=1)

Computes the bulk modulus and shear modulus of soft-sand (uncemented) rock physics model.

Parameters

  • k_min – bulk modulus of mineral (Gpa)

  • mu_min – shear modulus of mineral (Gpa)

  • phi – porosity (fraction) expressed as vectors

  • cd_num – coordination number

  • cp – critical porosity (random close pack of well-sorted rounded quartz grains)

  • P – Confining pressure/rock stress (Gpa)

  • f – shear modulus correction factor 0 = dry frictionless packing 1 = dry pack with perfect adhesion

Returns

Bulk modulus and Shear modulus

Reference:

The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media - Gary M. Mavko, Jack Dvorkin, and Tapan Mukerji

pyavo.seismodel.rpm.stiff_sand(k_min: float, mu_min: float, phi: numpy.ndarray, cd_num=8.6, cp=0.4, P=10, f=1)

Computes the bulk modulus and shear modulus of stiff-sand(uncemented) rock physics model.

Parameters

  • k_min – bulk modulus of mineral (Gpa)

  • mu_min – shear modulus of mineral (Gpa)

  • phi – porosity (fraction) expressed as vectors

  • cd_num – coordination number

  • cp – critical porosity (random close pack of well-sorted rounded quartz grains)

  • P – Confining pressure/rock stress (Gpa)

  • f – shear modulus correction factor 0 = dry frictionless packing 1 = dry pack with perfect adhesion

Returns

Bulk modulus and Shear modulus

Reference:

The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media - Gary M. Mavko, Jack Dvorkin, and Tapan Mukerji

pyavo.seismodel.rpm.twolayer(n_samples: int, vp1: float, vs1: float, rho1: float, vp2: float, vs2: float, rho2: float)

Display seismic signatures at Near and Far offset traces and AVO curve for a two layer model.

Parameters

  • n_samples – Number of samples

  • vp1 – P-wave velocity in top layer

  • vs1 – S-Wave velocity in top layer

  • rho1 – Density of top layer

  • vp2 – P-wave velocity in bottom layer

  • vs2 – S-wave velocity in bottom layer

  • rho2 – Density of second layer

Reference

Alessandro aadm (2015)

pyavo.seismodel.rpm.vel_sat(k_min: float, rho_min: float, k_fl: float, rho_fl: float, k_frame: float, mu_frame: float, phi: numpy.ndarray) → tuple

Computes velocities and densities of saturated rock using Gassmann’s fluid substitution equations.

Parameters

  • k_min – bulk modulus of mineral (Gpa)

  • rho_min – density of mineral (g/cc)

  • k_fl – bulk modulus of fluid (Gpa)

  • rho_fl – density of fluid (g/cc)

  • k_frame – bulk modulus of rock frame/dry rock (Gpa)

  • mu_frame – shear modulus of rock frame/dry rock (Gpa)

  • phi – porosity expressed as vector values

Returns

k_sat: Bulk modulus rho_sat: Density vp_sat: P-wave velocity vs_sat: S-wave velocity

pyavo.seismodel.rpm.voigt_reuss(mod1: float, mod2: float, vfrac: float) → tuple

Computes the Voigt-Reuss-Hill bounds and average for a 2-component mixture. The Voigt Bound is obtained by assuming the strain field remains constant throughout the material when subjected to an arbitrary average stress field. The Reuss Bound is obtained by assuming the stress field remains constant throughout the material in an arbitrary average strain field.

Parameters

  • mod1 – elastic modulus of first mineral

  • mod2 – elastic modulus of second mineral

  • vfrac – volumetric fraction of first mineral (net-to-gross)

Returns

upper bound, lower bound, Voigt-Ruess-Hill average

Reference

Mavko, G, T. Mukerji and J. Dvorkin (2009), The Rock Physics Handbook: Cambridge University Press.

pyavo.seismodel.rpm.well_rpt(df, z_init: float, z_final: float, sand_cut: float, vsh: pandas.core.series.Series, vp: pandas.core.series.Series, vs: pandas.core.series.Series, rho: pandas.core.series.Series, ip_rpt0: numpy.ndarray, vpvs_rpt0: numpy.ndarray, ip_rpt1: numpy.ndarray, vpvs_rpt1: numpy.ndarray)

Plot Soft and Stiff Sand RPT models and superimpose on well data.

Parameters

  • df – Pandas Dataframe

  • z_init – minimum depth values withing depth log range

  • z_final – maximum depth values within depth log range

  • sand_cut – cut off sand

  • vsh – volume of shale from VSH log

  • vp – P-wave velocity values from VP log

  • vs – S-wave velocity values from VS log

  • rho – density values from RHOB log

  • ip_rpt0 – Acoustic impedance values of first RPT model

  • vpvs_rpt0 – Vp/Vs values of first RPT model

  • ip_rpt1 – Acoustic impedance values of second RPT model

  • vpvs_rpt1 – Vp/Vs values of second RPT model

pyavo.seismodel.tuning_prestack module

Functions to generate a synthetic angle gather from a 3-layer property model to examine pre-stack tuning effects.

pyavo.seismodel.tuning_prestack.avo_inv(rc_zoep, ntrc: int, top: list) → dict

AVO inversion for INTERCEPT and GRADIENT from analytic and convolved reflectivity values. Linear least squares method is used for estimating INTERCEPT and GRADIENT coefficients.

Parameters

  • rc_zoep – Zoeppritz reflection coefficient values

  • ntrc – Number of traces

  • top – Convolved top reflectivity values

Returns

Zoeppritz and Convolved reflectivities

pyavo.seismodel.tuning_prestack.calc_theta_rc(theta1_min: float, theta1_step: float, vp: list, vs: list, rho: list, ang) → tuple

Computes the reflection coefficients for given incidence angles in a three layer model.

Parameters

  • theta1_min – minimum incidence angle

  • theta1_step – maximum incidence angle

  • vp – Layer P-wave velocities

  • vs – Layer S-wave velocities

  • rho – Density of layers

  • ang – Angle of incidence

Returns

theta1_samp : n-samples of incidence angles, rc_1: Reflection coefficient at layer1 / layer2, rc_2: Reflection coefficient at layer2 / layer3.

pyavo.seismodel.tuning_prestack.int_depth(h_int: list, thickness: float)

Computes depth to the interface for a three layer model.

Parameters

  • h_int – depth to to first interface

  • thickness – thickness

Returns

d_interface: interface depths

pyavo.seismodel.tuning_prestack.layer_index(lyr_times: list, dt=0.0001) → tuple

Calculate array indices corresponding to top/base interfaces.

Parameters

  • lyr_times – Interface times

  • dt – change in time, default is 0.0001. Changing this from 0.00005 can affect the display quality.

Returns

lyr1_indx: array

Layer 1

lyr2_indx: array

Layer 2

pyavo.seismodel.tuning_prestack.n_angles(theta1_min: float, theta1_max: float, theta1_step=1) → int

Computes number of traces for given angles on incidence.

Parameters

  • theta1_min – minimum incidence angle

  • theta1_max – maximum incidence angle

  • theta1_step – angle of incidence steps, default is 1

Returns

n_trace: number of traces

pyavo.seismodel.tuning_prestack.plot_vawig(axhdl, data, t, excursion)

Format the display of synthetic angle gather.

Parameters

  • axhdl – Plot axes

  • data – Synthetic traces generated from convolved zoeppritz.

  • t – Regularly spaced ime samples

  • excursion – Adjust plot width

pyavo.seismodel.tuning_prestack.ray_param(v_int: float, theta: float) → float

Calculates the ray parameter P.

Parameters

  • v_int – Interval velocity

  • theta – Angle of incidence (deg)

Returns

p: Ray parameter

pyavo.seismodel.tuning_prestack.rc_zoep(vp1: float, vs1: float, vp2: float, vs2: float, rho1: float, rho2: float, theta1: float) → numpy.ndarray

Calculate the Reflection & Transmission coefficients using Zoeppritz equations.

Parameters

  • vp1 – P-wave velocity in upper layer

  • vs1 – S-wave velocity in upper layer

  • vp2 – P-wave velocity in lower layer

  • vs2 – S-wave velocity in lower layer

  • rho1 – Density of upper layer

  • rho2 – Density of lower layer

  • theta1 – Angle of incidence of ray

Returns

R: Reflection coefficients

Reference:

The Rock Physics Handbook, Dvorkin et al.

pyavo.seismodel.tuning_prestack.syn_angle_gather(min_time: float, max_time: float, lyr_times: numpy.ndarray, thickness: float, top_ref: list, base_ref: list, vp_dig: numpy.ndarray, vs_dig: numpy.ndarray, rho_dig: numpy.ndarray, syn_zoep: numpy.ndarray, rc_zoep: numpy.ndarray, t: numpy.ndarray, excursion: int)

Plot synthetic angle gather for three layer model displayed in normal polarity and amplitudes extracted along the upper and lower interfaces.

Parameters

  • min_time – Minimum plot time (s)

  • max_time – Maximum plot time (s)

  • lyr_times – interface times

  • thickness – apparent thickness of second layer

  • top_ref – convolved top reflectivity values

  • base_ref – convolved base reflectivity values

  • vp_dig – P-wave velocity in digital time domain

  • vs_dig – S-wave velocity in digital time domain

  • rho_dig – Density of layers in digital time domain

  • syn_zoep – Synthetic seismogram

  • rc_zoep – Zoeppritz reflectivities

  • t – regularly spaced time samples

  • excursion – Adjust plot width

pyavo.seismodel.tuning_prestack.t_domain(t, vp: list, vs: list, rho: list, lyr1_index: list, lyr2_index: list) → tuple

Create a “digital” time domain version of an input property model.

Parameters

  • t – array of regularly spaced time samples

  • vp – P-wave velocity

  • vs – S-wave velocity

  • rho – Density of layers

  • lyr1_index – Array indices of top interface

  • lyr2_index – Array indices of base interface

Returns

vp_dig: P-wave velocity in digital t-domain, vs_dig: S-wave velocity in digital t-domain, rho_dig: Density in digital t-domain.

pyavo.seismodel.tuning_wedge module

Functions to generate a three layer wedge model using the reflection coefficients and travel times

Reference:

Mavko, G., T. Mukerji, and J. Dvorkin, 2009, The rock physics handbook: Tools for seismic analysis of porous media, 2nd ed.: Cambridge University Press. CrossRef

Shuey, R. T., 1985, A simplification of the Zoeppritz equations: Geophysics, 50, no. 4, 609–614, http://dx.doi.org/10.1190/1.1441936.

Widess, M., 1973, How thin is a thin bed?: Geophysics, 38, no. 6, 1176–1180, http://dx.doi.org/10.1190/1.1440403.

https://wiki.seg.org/wiki/Thin_beds,_tuning,_and_AVO

pyavo.seismodel.tuning_wedge.calc_times(z_int: list, vp: list) → list

Calculate the travel time to a reflector.

Parameters

  • z_int – Depth to a reflector

  • vp – P-Wave Velocity

Returns

t_int: Interface times

Usage

t_int = calc_times(z_int, vp)

pyavo.seismodel.tuning_wedge.get_rc(Vp: list, rho: list) → list

Calculates the Reflection Coefficient at the interface separating two layers. The ratio of amplitude of the reflected wave to the incident wave, or how much energy is reflected. Typical values of R are approximately −1 from water to air, meaning that nearly 100% of the energy is reflected and none is transmitted; ~0.5 from water to rock; and ~0.2 for shale to sand.

Parameters

  • Vp – P-wave velocity (m/s)

  • rho – Layer density (g/cc)

Returns

rc_int: Reflection Coefficient

pyavo.seismodel.tuning_wedge.int_depth(h_int: list, dh_min: float, dh_step: float, mod: int) → list

Computes the depth to an interface.

Parameters

  • h_int – depth to first interface

  • dh_min – minimum thickness of layer 2

  • dh_step – Thickness step from trace-to-trace (usually 1.0m)

  • mod – model traces generated within a thickness or interval

Returns

d_inteface: depth to interface

pyavo.seismodel.tuning_wedge.mod_digitize(rc_int: list, t_int: list, t: list) → list

Digitize a 3 layer model using reflection coefficients and interface times.

Parameters

  • rc_int – reflection coefficients corresponding to interface times

  • t_int – interface times

  • t – regularly sampled time series defining model sampling

Returns

rc: Reflection Coefficient

Usage

rc = digitize_model(rc_int, t_int, t)

pyavo.seismodel.tuning_wedge.n_model(h_min: float, h_max: float, h_step=1) → int

Computes number of traces within an interval or thickness.

Parameters

  • h_min – minimum thickness

  • h_max – maximum thickness

  • h_step – thickness steps, default is 1

Returns

n_trace: number of traces

pyavo.seismodel.tuning_wedge.ricker(sample_rate: float, length: float, c_freq: float)

Generate time and amplitude values for a zero-phase wavelet. The second derivative of the Gaussian function or the third derivative of the normal-probability density function.

Parameters

  • sample_rate – sample rate in seconds

  • length – length of time (dt) in seconds

  • c_freq – central frequency of wavelet (cycles/seconds or Hz).

Returns

wv_time: zero-phase wavelet time, wv_amp: zero-phase wavelet amplitude.

Usage:

time, wavelet = (sample_rate,duration,c_freq)

Reference:

A Ricker wavelet is often used as a zero-phase embedded wavelet in modeling and synthetic seismogram manufacture. Norman H. Ricker (1896–1980), American geophysicist.

pyavo.seismodel.tuning_wedge.syn_seis(ref_coef: list, wav_amp: numpy.ndarray) → list

Generate synthetic seismogram from convolved reflectivities and wavelet.

Parameters

  • ref_coef – Reflection coefficient

  • wav_amp – wavelet amplitude

Returns

smg: array

Synthetic seismogram

pyavo.seismodel.tuning_wedge.time_samples(t_min: float, t_max: float, dt=0.0001) → list

Create regularly sampled time series defining model sampling.

Parameters

  • t_min – Minimum time duration

  • t_max – Maximum time duration

  • dt – Change in time, default = 0.0001

Returns

time

pyavo.seismodel.tuning_wedge.wedge_model(syn_zo: numpy.ndarray, layer_times: numpy.ndarray, t: numpy.ndarray, t_min: float, t_max: float, h_max: float, h_step: float, excursion: int, dt=0.0001)

Plot a three layer wedge model amplitudes and zero offset seismogram.

Parameters

  • syn_zo – Synthetic Seismogram

  • layer_times – travel time to reflectors

  • t – regularly sampled time series defining model sampling

  • t_min – minimum time duration

  • t_max – maximum time duration

  • h_max – maximum depth

  • h_step – depth steps, default is 1

  • excursion – adjust plot width

  • dt – trace parameter, changing this from 0.0001 can affect the display quality.

pyavo.seismodel.wavelet module

Functions to generate a ricker and trapezoidal bandpass wavelet.

Reference:

https://wiki.seg.org/wiki/Practical_aspects_of_frequency_filtering

pyavo.seismodel.wavelet.bandpass(f1: float, f2: float, f3: float, f4: float, phase: float, dt: float, wvlt_length: float) → tuple

Calculate a trapezoidal bandpass wavelet.

f1: Low truncation frequency of wavelet in Hz f2: Low cut frequency of wavelet in Hz f3: High cut frequency of wavelet in Hz f4: High truncation frequency of wavelet in Hz phase: wavelet phase in degrees dt: sample rate in seconds wvlt_length: length of wavelet in seconds

Usage:

t, wvlt = wvlt_ricker(f1, f2, f3, f4, phase, dt, wvlt_length)

Reference

Wes Hamlyn, 2014.

pyavo.seismodel.wavelet.plot_ricker(sample_rate=0.001, length=0.128, c_freq=25)

Computes and display a zero-phase wavelet.

Parameters

  • sample_rate – sample rate in seconds.

  • length – length of time (dt) in seconds.

  • c_freq – central frequency of wavelet (cycles/seconds or Hz)

Usage:

plot_ricker(sample_rate,duration,c_freq)

pyavo.seismodel.wavelet.ricker(sample_rate=0.001, length=0.128, c_freq=25) → tuple

Generate time and amplitude values for a zero-phase wavelet. The second derivative of the Gaussian function or the third derivative of the normal-probability density function. A Ricker wavelet is often used as a zero-phase embedded wavelet in modeling and synthetic seismogram manufacture.

Reference:

Norman H. Ricker (1896–1980), American geophysicist.

Parameters

  • sample_rate – sample rate in seconds (float, int)

  • length – length of time (dt) in seconds (float, int)

  • c_freq – central frequency of wavelet (cycles/seconds or Hz). (float, int)

Returns

ndarray

Usage:

time, wavelet = (sample_rate,duration,c_freq)

Module contents