29 citations · 42 across the 18 of their papers we have counts for
4 papers · 1 filter
Probabilistic and Alarm-Based Evaluation of a b-Value-Driven Deep Learning Earthquake Forecast
Jonas Köhler, Wei Li, Johannes Faber +2
We evaluate the forecasting performance of a deep learning model, originally introduced as a pattern-extraction framework, that operates on the spatiotemporal evolution of seismic…
An Interpretable Physics Informed Multi-Stream Deep Learning Architecture for the Discrimination between Earthquake, Quarry Blast and Noise
Nishtha Srivastava, Johannes Faber, Dhruv Aditya Srivastava
The reliable discrimination of tectonic earthquakes from anthropogenic quarry blasts and transient noise remains a critical challenge in single station seismic monitoring. In this…
Detecting Spatiotemporal b-Value Anomalies with a Progressive Deep Learning Architecture
Jonas Köhler, Wei Li, Johannes Faber +2
Identifying systematic patterns in seismicity that precede large earthquakes remains a central challenge in statistical seismology. In this work, we present a methodological framew…
Evaluating the SAIPy Performance using a Local Seismic Network for Volcano-Tectonic Earthquakes Monitoring
Claudia Quinteros-Cartaya, Francisco Javier Núñez-Cornú, Nishtha Srivastava
In this study, we evaluated the performance of SAIPy, an open-source Python package for deep learning-based seismic data analysis, by applying its single-station monitoring tools a…