Deep Learning for Seismic Imaging and Interpretation
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Updated
Sep 18, 2020 - Python
Deep Learning for Seismic Imaging and Interpretation
Unsupervised (Self-Supervised) Clustering of Seismic Signals Using Deep Convolutional Autoencoders
Earthquake source parameters from P- and S-wave displacement spectra
Julia language support for geophysical time series data
Preprocessing seismic data: download, format changing, and archiving
3D Fault Segmentation by U-Net
The repository has the PyTorch codes to reproduce the results for our recently accepted paper, "Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network", in SEG Technical Program Expanded Abstracts, 2019.
🔭 Read, parse seismic data from AnyShake Explorer, stream or archive to database
A tool for exchanging data between SEG-Y format and NumPy array inside Python environment
Machine Learning/ Deep Learning processing Seismic data
🌏 Read voltage counts from three geophones, pack & send data to AnyShake Observer
Automatic Microseismic Denoising and Onset Detection using customized thresholding.
GMG: An open source geophysical modelling GUI
List of Seedlink, Earthworm, Winston Wave Server DataCenters. Used in: https://www.src.com.au/ and https://github.com/xspanger3770/GlobalQuake
Using convolutional autoencoders to remove random noise from seismic data.
Seismic inversion
Seismic monitoring experiment investigating hydraulic fracturing within the Kaybob-Duvernay horizon in Alberta. Managed by the University of Calgary.
Detecting Earthquake P-Waves using popular STA/LTA Algorithm with visualization and estimations of Seismometer Trajectory in 3D, S-Wave arrival time and much more.
This repository contains MATLAB scripts and sample data for applying denoising method presented in: "Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data"
Our project revolutionizes seismic image interpretation with advanced deep learning, using Convolutional Neural Networks (CNNs) to automate workflows and improve accuracy in subsurface exploration. Trained on synthetic seismic data from coal mines, our algorithm excels in identifying faults, folds, and flat layers.
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