seismic_deep_learning and seismic-deeplearning
These are competing implementations of similar deep learning approaches for seismic interpretation, with Microsoft's version being more mature and widely adopted (461 vs 253 stars), so users would typically choose one or the other rather than use both together.
About seismic_deep_learning
thilowrona/seismic_deep_learning
A couple of python scripts to interpret geological structures from geophysical images using deep learning
This project helps geophysicists and exploration geologists automatically identify and map geological structures like faults, salt bodies, and horizons from seismic reflection data. You input 2D or 3D seismic images, and it outputs precise maps of these subsurface features. It's designed for anyone working with seismic data who wants to automate structural interpretation.
About seismic-deeplearning
microsoft/seismic-deeplearning
Deep Learning for Seismic Imaging and Interpretation
Provides extensible ML pipelines with state-of-the-art segmentation models (UNet, SEResNET, HRNet) for facies classification and seismic facies segmentation on 2D/3D rectangular seismic volumes. Built on PyTorch/TensorFlow with modular, swappable model configurations and SEGY-to-numpy data conversion utilities. Integrates with Azure Machine Learning for distributed training and includes Docker containerization, Jupyter notebooks, and pip-installable `cv_lib` and `interpretation` utilities for end-to-end seismic workflows.
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