seismiQB
is a framework for deep learning research on 3d-cubes of seismic data. It allows to
sample
andload
crops ofSEG-Y
cubes for training neural networks- convert
SEG-Y
cubes toHDF5
-format for even fasterload
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
andelastic_transform
- segment horizons and interlayers using
UNet
andTiramisu
- 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
git clone --recursive https://github.com/gazprom-neft/seismiqb.git
Working with SEG-Y cubes with various indexing headers (e.g. pre-stack and post-stack).
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.
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.
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.
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.
Enlarge picked (possibly by other models) horizons to cover more area.
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.
Application of a model, trained on a set of cubes, to a completely unseen data.
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
}