Deep Learning for Seismic Imaging and Interpretation
-
Updated
Sep 18, 2020 - Python
Deep Learning for Seismic Imaging and Interpretation
Deep-learning inversion: A next-generation seismic velocity model building method
A python code for running VELEST (1D velocity calculation using travel time inversion)
Earthquake source parameters from P- and S-wave displacement spectra
Seismic Velocity Modeling using Deep Transfer Learning
Seismic inversion using a neural network regulariser implemented as an ExternalOperator in Firedrake
Seismic refraction - New implementation of the Sardine software
Analysis and Visualization toolkit for plaNetary Inferences
A seismological code in Python: this is a package for full waveform ambient seismic noise inversion for sources or structure.
Locate seismic events on a simplified 2-d model of the earth. This is a challenge problem and data set for PPLs (Probabilistic Programming Languages) or generative models in general. Probabilistic inference techniques can be tested and published on this challenge.
Tools used for performing Simultaneous inversion for surface wave phase velocity and earthquake centroid parameters
The first global synthetic dataset for physics-ML seismic wavefield modeling and full-waveform inversion
Add a description, image, and links to the seismic-inversion topic page so that developers can more easily learn about it.
To associate your repository with the seismic-inversion topic, visit your repo's landing page and select "manage topics."