chenwuperth / seismiqb

Seismic data interpretation with deep learning

SeismiQB

seismiQB is a framework for deep learning research on 3d-cubes of seismic data. It allows to

• sample and load crops of SEG-Y cubes for training neural networks
• convert SEG-Y cubes to HDF5-format for even faster load
• create_masks of different types from horizon labels for segmenting horizons, facies and other seismic bodies
• build augmentation pipelines using custom augmentations for seismic data as well as rotate, noise and elastic_transform
• segment horizons and interlayers using UNet and Tiramisu
• extend horizons from a couple of seismic ilines in spirit of classic autocorrelation tools but with deep learning
• convert predicted masks into horizons for convenient validation by geophysicists

Installation

With pipenv:

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

With pip:

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

After that just import seismiqb:

import seismiqb

To get the developer version, run

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

Turorials

Cube processing

Working with SEG-Y cubes with various indexing headers (e.g. pre-stack and post-stack).

Labeled structures

Our dedicated Horizon class is capable of loading data from multiple geological formats, computing a wealth of statistics of it, and a lot more. We also provide interfaces for other types of geological bodies like faults, facies and labels in pre-stack cubes.

Cubeset

A wrapper aroung geometries and labels, that can generate data from random labeled locations from multiple cubes and apply both geological and computer vision augmentations.

Metrics

In order to evaluate our results (particularly predicted horizons), we developed a few seismic attributes to assess quality of seismic cubes, sparse carcasses and labeled surfaces.

Ready-to-use ML models

Carcass interpolation

This model spreads a very sparse hand-labeled carcass of a horizon to the whole cube spatial area by solving a task of binary segmentation.

Horizon extension

Enlarge picked (possibly by other models) horizons to cover more area.

Interlayer segmentation

Applying the multi-class segmentation model to the task of horizon detection. Note that the model was developed with older seismiQB versions and does not work anymore.

Inter-cube generalization

Application of a model, trained on a set of cubes, to a completely unseen data.

Citing seismiQB

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

Khudorozhkov R., Koryagin A., Tsimfer S., Mylzenova D. SeismiQB library for seismic interpretation with deep learning. 2019.

@misc{seismiQB_2019,
  author       = {R. Khudorozhkov and A. Koryagin and S. Tsimfer and D. Mylzenova},
  title        = {SeismiQB library for seismic interpretation with deep learning},
  year         = 2019
}