Machine learning in earthquake seismology
SM Mousavi, GC Beroza - Annual Review of Earth and …, 2023 - annualreviews.org
Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …
predictive capability by learning relationships embedded in data. ML methods are becoming …
Intermittent criticality multi‐scale processes leading to large slip events on rough laboratory faults
We discuss data of three laboratory stick‐slip experiments on Westerly Granite samples
performed at elevated confining pressure and constant displacement rate on rough fracture …
performed at elevated confining pressure and constant displacement rate on rough fracture …
[HTML][HTML] Explainable machine learning for labquake prediction using catalog-driven features
S Karimpouli, D Caus, H Grover… - Earth and Planetary …, 2023 - Elsevier
Recently, Machine learning (ML) has been widely utilized for laboratory earthquake
(labquake) prediction using various types of data. This study pioneers in time to failure (TTF) …
(labquake) prediction using various types of data. This study pioneers in time to failure (TTF) …
An adaptable random forest model for the declustering of earthquake catalogs
F Aden‐Antoniów, WB Frank… - Journal of Geophysical …, 2022 - Wiley Online Library
Earthquake catalogs are essential to analyze the evolution of active fault systems. The
background seismicity rate, or rate of earthquakes that are not directly triggered by other …
background seismicity rate, or rate of earthquakes that are not directly triggered by other …
Unsupervised clustering of catalogue-driven features for characterizing temporal evolution of labquake stress
S Karimpouli, G Kwiatek… - Geophysical Journal …, 2024 - academic.oup.com
Earthquake forecasting poses significant challenges, especially due to the elusive nature of
stress states in fault systems. To tackle this problem, we use features derived from seismic …
stress states in fault systems. To tackle this problem, we use features derived from seismic …
On catching the preparatory phase of damaging earthquakes: an example from central Italy
How, when and where large earthquakes are generated remain fundamental unsolved
scientific questions. Intercepting when a fault system starts deviating from its steady behavior …
scientific questions. Intercepting when a fault system starts deviating from its steady behavior …
Memory guided Aquila optimization algorithm with controlled search mechanism for seismicity analysis of earthquake prone regions
De-clustering the seismic catalog is one of the crucial processes in determining the
probability of exceeding ground motions at particular locations. Removing dependent …
probability of exceeding ground motions at particular locations. Removing dependent …
Recent advances in earthquake seismology using machine learning
Given the recent developments in machine-learning technology, its application has rapidly
progressed in various fields of earthquake seismology, achieving great success. Here, we …
progressed in various fields of earthquake seismology, achieving great success. Here, we …
The preparatory process of the 2023 Mw 7.8 Türkiye earthquake
To verify the existence of a preparatory process for the 6 February 2023, Mw 7.8
Kahramanmaraş earthquake, southern Türkiye, we analyze the temporal evolution of …
Kahramanmaraş earthquake, southern Türkiye, we analyze the temporal evolution of …
Spatiotemporal evolution of microseismicity seismic source properties at the Irpinia near‐Fault Observatory, southern Italy
We estimate the source parameters of small‐magnitude earthquakes that occurred during
2008–2020 in the Irpinia faults area (southern Italy). We apply a spectral decomposition …
2008–2020 in the Irpinia faults area (southern Italy). We apply a spectral decomposition …