What question did this study set out to answer?

This review aims to explore the integration of machine learning techniques in microseismic monitoring and their impacts on data processing.

March 5, 2023Earth-Science ReviewsOpen Access

Machine learning in microseismic monitoring

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Authors

DADenis AnikievKing Fahd University of Petroleum and Minerals CBClaire BirnieKing Abdullah University of Science and Technology UWUmair bin WaheedKing Fahd University of Petroleum and Minerals

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Implication

Review highlights machine learning's role in enhancing data quality and event detection in microseismic monitoring, suggesting significant progress ahead.

Key Points

This review aims to explore the integration of machine learning techniques in microseismic monitoring and their impacts on data processing.
Review of recent developments in microseismic monitoring
Assessment of machine learning applications for event detection and characterization
Analysis of advancements in distributed acoustic sensing (DAS) and real-time monitoring.
Machine learning techniques significantly enhance passive seismic data quality.
ML methods improve detection and localization of microseismic events, including hypocenter location.
Advanced characterizations, such as source mechanism determination and uncertainty analysis, are now feasible with ML.

Cite This Study

Anikiev et al. (2023) studied this question.

synapsesocial.com/papers/69deaad257c7c8340a558ff4 https://doi.org/10.1016/j.earscirev.2023.104371

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Implication

Review highlights machine learning's role in enhancing data quality and event detection in microseismic monitoring, suggesting significant progress ahead.

Machine learning in microseismic monitoring

Discussion

Member takes

Implication

Key Points

Cite This Study

Machine learning in microseismic monitoring

Discussion

Member takes

Implication

Key Points

Cite This Study

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