Automated fault network extraction in complex tectonic regimes: A hybrid machine learning and structural attributes approach

Automated fault network extraction in complex tectonic regimes: A hybrid machine learning and structural attributes approach

Interpreting seismic faults is crucial for prospect generation, reservoir modeling, and CO2 storage assessment. However, identifying faults in complex tectonic regimes remains challenging, particularly in regions that have experienced multiple phases of tectonic activity. Despite advancements in str...

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Bibliographic Details
Main Authors: Muhammad Khan, Andy Anderson Bery, Yasir Bashir, Sya'rawi Muhammad Husni Sharoni, Syed Sadaqat Ali
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Applied Computing and Geosciences
Subjects:
Fault-Net
Transfer learning
Fault likelihood
Data conditioning
Fault probability cube
Poseidon
Online Access:http://www.sciencedirect.com/science/article/pii/S2590197425000461
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