Deep Learning for Seismic Imaging and Interpretation

Kaggle | 9th place single model solution for TGS Salt Identification Challenge

Julia Devito inversion.

Teleseismic body wave modeling through stacks of (dipping/anisotropic) layers

An automatic seismology toolset for global P-to-S and S-to-P receiver function imaging

Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.

Seismic inversion

Julia package to perform Kirchhoff migration and demigration

Developing project for shallow seismic structure imaging

Image gather tools

Auto-Correlogram Calculation in seismology

Modeling, inversion and migration focusing on seismic first-arrivals.

Synthetic demonstration of Eikonal tomography

Derisking geological carbon storage from high-resolution time-lapse seismic to explainable leakage detection

FBD: Multi-channel blind deconvolution with focusing constraints

A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification by Siahkoohi, A., Rizzuti, G., and Herrmann, F.J.

Shot-profile prestack depth migration algorithm based on a phase-shift plus interpolation (PSPI) wave propagation method.

[ICLR 2025] This is the official repository for the paper “A Unified Framework for Forward and Inverse Problems in Subsurface Imaging Using Latent Space Translations." This work proposes a generalized framework to solve forward and inverse problems, utilizing the latent space. We also propose an invertible architecture for the OpenFWI dataset.

For Southeast Tibet

Modeling, inversion and migration focusing on seismic first-arrivals.