Seismic Interpretation

Seismic interpretation, the process of extracting geological information from seismic data, is undergoing a transformation driven by machine learning. Current research focuses on improving the accuracy and efficiency of deep learning models for tasks like facies classification and structure detection, employing techniques like active learning and data augmentation to address challenges such as limited labeled data and model generalization. These advancements leverage novel architectures incorporating attention mechanisms and manifold learning to enhance the identification of subtle geological features and improve the interpretability of model predictions. Ultimately, these improvements promise more accurate and cost-effective subsurface characterization for applications in hydrocarbon exploration and reservoir management.

Papers

June 1, 2024

Effective Data Selection for Seismic Interpretation through Disagreement
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
Deep Learning Deep Learning Algorithm Data Selection Opinion Disagreement Seismic Image Seismic Interpretation

February 24, 2023

Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation
Ryan Benkert, Oluwaseun Joseph Aribido, Ghassan AlRegib
Deep Neural Network Anti Forgetting Novel Approach Seismic Data Neural Manifold Explainable Machine Learning Pipeline Seismic Interpretation

January 9, 2023

Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions
Dmitry Ivlev
Machine Learning Underwater Environment Seismic Data Geophysical Data Reservoir Pressure Well Data Seismic Interpretation

December 15, 2022

Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation
Ahmad Mustafa, Ghassan AlRegib
Active Learning Full State Reconstruction Manifold Learning Omni Recon Seismic Interpretation Seismic Facies

January 17, 2022

A novel attention model for salient structure detection in seismic volumes
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib
Saliency Map SALient Object Detection Seismic Data Seismic Image Seismic Interpretation

Seismic Interpretation

Papers

Effective Data Selection for Seismic Interpretation through Disagreement

Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation

Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions

Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation

A novel attention model for salient structure detection in seismic volumes