SeismicPro

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SeismicPro is a framework for accelerating processing of pre-stack seismic data with deep learning models.

Main features:

• Load pre-stack data in SEG-Y format at any exploration stage in a highly efficient manner
• Utilize stacking velocities, times of first breaks, and other types of auxiliary data from multiple geological frameworks
• Transform seismic data by both general and complex task-specific methods in a massively parallel way
• Combine processing functions into concise and readable pipelines
• Define a wide range of neural network architectures from vanilla UNet to sophisticated EfficientNets with simple and intuitive configurations in just a few lines of code

Installation

SeismicPro module is in the beta stage. Your suggestions and improvements via issues are very welcome.

SeismicPro is compatible with Python 3.8+ and tested on Ubuntu 20.04 and Windows Server 2019.

Note that the Benchmark module and Research may not work on Windows due to dependency issues. Use it with caution.

Installation as a python package

With pip:

pip3 install git+https://github.com/gazprom-neft/SeismicPro.git

With pipenv:

pipenv install git+https://github.com/gazprom-neft/SeismicPro.git#egg=SeismicPro

Installation as a project repository

When cloning a repo from GitHub use --recursive flag to make sure that batchflow submodule is also cloned.

git clone --recursive https://github.com/gazprom-neft/SeismicPro.git

Getting Started

SeismicPro provides a simple interface to work with pre-stack data.

import seismicpro

A single SEG-Y file can be represented by a Survey instance that stores a requested subset of trace headers and allows for gather loading:

survey = seismicpro.Survey(path_to_file, header_index='FieldRecord', header_cols='offset')

header_index argument specifies how individual traces are combined into gathers: in this example, we consider common source gathers. Both header_index and header_cols correspond to names of trace headers in segyio.

All loaded headers are stored in headers attribute as a pd.DataFrame indexed by passed header_index:

survey.headers.head()

A randomly selected gather can be obtained by calling sample_gather method:

gather = survey.sample_gather()

Let's take a look at it being sorted by offset:

gather.sort(by='offset').plot()

Moreover, processing methods can be combined into compact pipelines like the one below which performs automatic stacking velocity picking and gather stacking:

stacking_pipeline = (dataset
    .pipeline()
    .load(src="raw")
    .sort(src="raw", by="offset")
    .mute(src="raw", dst="muted_raw", muter=muter)
    .calculate_semblance(src="muted_raw", dst="raw_semb",
                         velocities=SEMBLANCE_VELOCITY_RANGE, win_size=8)
    .calculate_stacking_velocity(src="raw_semb", dst="velocity",
                                 start_velocity_range=START_VELOCITY_RANGE,
                                 end_velocity_range=END_VELOCITY_RANGE,
                                 n_times=N_TIMES, n_velocities=N_VELOCITIES)
    .get_central_gather(src="raw")
    .apply_nmo(src="raw", stacking_velocity="velocity")
    .mute(src="raw", muter=muter, fill_value=np.nan)
    .stack(src="raw")
    .dump(src="raw", path=STACK_TRACE_PATH)
)

stacking_pipeline.run(BATCH_SIZE, n_epochs=1)

Tutorials

You can get more familiar with the framework and its functionality by reading SeismicPro tutorials.

Citing SeismicPro

Please cite SeismicPro in your publications if it helps your research.

Khudorozhkov R., Illarionov E., Broilovskiy A., Kalashnikov N., Podvyaznikov D., Arefina A., Kuvaev A., SeismicPro library for seismic data processing and ML models training and inference. 2019.

@misc{seismicpro_2019,
  author       = {R. Khudorozhkov and E. Illarionov and A. Broilovskiy and N. Kalashnikov and D. Podvyaznikov and A. Arefina and A. Kuvaev},
  title        = {SeismicPro library for seismic data processing and ML models training and inference},
  year         = 2019
}