seismiQB

Installation • Getting Started • Citation

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seismiQB is a framework for research and deployment of deep learning models on post-stack seismic data. It covers all the main stages of model development and its production usage, and the main features are:

• Convert SEG-Y to a compressed and quantized data formats, that take 4x less disk space and load slices up to 40 times faster
• Work (e.g. load, make segmentation masks, QC) with a number of labels: horizons, faults, facies (both 2d and 3d)
• Prepare data for the model inputs, e.g. patches (2d or 3d) of seismic data and corresponding segmentation masks
• Augment model data with a number of geological and geometrical transforms, as well as traditional ML augmentations
• Define even the most complex neural networks with just a few lines of code, or re-use existing PyTorch models
• Optimize train/inference stages with ready-to-use primitives to meet the demands of modern field development
• Export predicted entities (horizon, faults, facies, arrays) to convenient formats (CHARISMA, FAULT_STICKS) for validation by geophysicists

Installation

seismiQB is compatible with Python 3.8+ and well tested on Ubuntu 20.04.

Installation of seismiQB as a Python package should be as easy as running one of the commands below, depending on your environment:

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

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

# developer version (add `--depth 1` if needed)
git clone https://github.com/gazprom-neft/seismiqb.git

Getting started

After installation just import seismiQB into your code. A quick demo of our primitives and methods:

import seismiqb

field = Field('/path/to/cube.sgy')                                    # Initialize field with SEG-Y
field.load_labels('path/to/horizons/*.char', labels_class='horizon')  # Add labeling

# Labels
field.horizons.interpolate()                                          # Fill in small holes
field.horizons.smooth_out()                                           # Smooth out and remove spikes
field.horizons.evaluate()                                             # Compute a quality control metric

# Visualizations
field.geometry.print()                                                # Display key stats about SEG-Y
field.show_slide(index=100, axis=1)                                   # Show 100-th crossline
field.show('horizons:0/metric')                                       # Show QC metric for one horizon

Be sure to check out our tutorials to get more info about the seismiQB primitives and usage.

Citing

Please cite seismiQB 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
}