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Generating seismic data and associated labels to train deep learning networks.

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synthoseis

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Generating seismic data and associated labels to train deep learning networks.

Overview

Synthoseis is an open-source, Python-based tool used for generating pseudo-random seismic data, as described in Synthetic seismic data for training deep learning networks.

The goal of synthoseis is to generate realistic seismic data for training a deep learning network to identify features of interest in field-acquired seismic data. Such training data should be plentiful, cover a diverse range of subsurface scenarios and provide quality training labels.

Documentation

Read our documentation: https://sede-open.github.io/synthoseis/datagenerator.html

Installation

We provide an environment.yml to install the required packages for synthoseis.

Resources

Quick Start

Run a model with parameters provided in the example config file

conda activate synthoseis
python main.py --config config/example.json --num_runs 1 --run_id seismic_example

Overview of workflow

Load user-parameters from config file
Build initial horizon at base and deposit layers of random thickness on top until some minimum depth is reached
Choose facies for each layer
Convert stack of horizons into a geologic age model
Generate faults and apply to age model
Identify closures using flood-filling algorithm
Fill closures randomly with fluids
Calculate elastic rock properties
Calculate reflection coefficients for each required incident angle
Apply random noise
Convolve using Butterworth bandpass filter to generate bandlimited seismic reflectivity
Apply geophysical augmentation (such as lateral smoothing, trace integration, amplitude balancing, RMO)

User parameters

An example user-parameter json format file is provided in the config folder, and is used to set parameters for generating a batch of training data.

Key Description
project Name of project. Used to select the rock property model associated with the project
project_folder Output directory for models
work_folder Temporary folder used for storing intermediate data. Deleted on model completion
cube_shape Number of samples in [X, Y, Z] for output data
incident_angles Central angle for the output seismic angle-stacks
digi Vertical sampling rate
infill_factor Initial oversampling multiplier in the Z direction. Due to memory contraints, this is only used to construct the initial, unfaulted horizons
initial_layer_stdev Standard deviation of the depth of initial horizon at the base of the model, random value is chosen between the provided [low, high] range
thickness_min Minimum thickness of layers (in samples)
thickness_max Maximum thickness of layers (in samples)
seabed_min_depth Random thickness layers are "deposited" on top of previous layers. When a horizon's minimum depth is below this value, this becomes the top-most horizon, usually the seabed
signal_to_noise_ratio_db Signal to noise ratio in decibels to control the noise level of the output seismic data. A random value is chosen from a (trimmed) triangular distribution from the provided [left, mode, right] values
bandwidth_low Bandpass low-cut value is chosen at random between the [low, high] values provided
bandwidth_high Bandpass high-cut value is chosen at random between the [low, high] values provided
bandwidth_ord Order of the slope used in the bandpass filter
dip_factor_max A scaling factor applied to the dip of each layer
min_number_faults Minimum number of faults in the model
max_number_faults Maximum number of faults in the model
pad_samples Additional padding in Z-direction to avoid edge effects, given in samples
max_column_height Maximum vertical height of column in a 3D closure
closure_types Used to enable specific closure types, from "simple" (4-way), "faulted" (3-way), or "onlap" (stratigraphic)
min_closure_voxels_simple Oil or gas filled simple closures with fewer voxels than this threshold will be filled with brine
min_closure_voxels_faulted As min_closure_voxels_simple but for faulted closures
min_closure_voxels_onlap As min_closure_voxels_simple but for stratigraphic closures
sand_layer_thickness Average thickness of stacked sand layers a priori, given in number of layers
sand_layer_fraction Average percentage of sand layers in model a priori
extra_qc_plots Switch to turn on/off additional QC output png plots
verbose Switch to turn on/off verbosity
partial_voxels If true, calculate an average property inside voxels that span multiple layers
variable_shale_ng If true, enable net to gross to vary laterally in shale layers, otherwise use a net to gross of 0
basin_floor_fans Switch to turn on/off basin floor fan-shaped features in layers
include_channels Switch to turn on/off channels (deprecated, always false)
include_salt Switch to turn on/off salt bodies
write_to_hdf Write QC and additional volumes to HDF file
broadband_qc_volume Output a bandpassed seismic data with a low-cut of 2Hz and high-cut of 90Hz to simulate broadband seismic
model_qc_volumes Save QC volumes to disk
multiprocess_bp Use multiprocessing to speed up the bandpass operations

Rock properties

An example rock property model is provided in rpm_example.py, and consists of trends as functions of depth for Vp, Vs and Rho for shales and fluid-filled sands.

To add a new rock property model, use the rpm_example.py as a template to create a similar python script containing the new depth trends for each fluid/facies combination, for example rockphysics/new_rpm.py. Create a new config.json file and replace the project value to "new_rpm".

Examples Gallery

Geologic Age

Basin Floor Fans

Salt Bodies

Cross-section through example salt bodies, coloured by lithology, where shale=0, sand=1, salt=2

Faulting Styles

Faulting style is chosen from self branching, stair case, horst graben or relay ramp, as shown from left to right. Alternatively, faults can be entirely random (not shown).

Closures

Example closures, coloured by closure number

Seismic Data

Cross-sections through the centre of some example models, showing the layered earth model coloured by facies on left, and fullstack RAI seismic data on the right. Sand content for models is chosen using a Markov process, values a priori (input) and a posteriori (output) are shown in the facies plot.

Contributing

We welcome all kinds of contributions. The preferred way of submitting a contribution is to either make an issue on GitHub or by forking the project on GitHub and making a pull request.

Citation

@article{doi:10.1190/INT-2021-0193.1,
author = {Tom P. Merrifield and Donald P. Griffith and S. Ahmad Zamanian and Stephane Gesbert and Satyakee Sen and Jorge De La Torre Guzman and R. David Potter and Henning Kuehl},
title = {Synthetic seismic data for training deep learning networks},
journal = {Interpretation},
volume = {10},
number = {3},
pages = {SE31-SE39},
year = {2022},
doi = {10.1190/INT-2021-0193.1},

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