activity
20242026
collaborators

7 papers

physics.geo-ph2026

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…

physics.geo-ph2026

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…

physics.geo-ph2026

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…

physics.geo-ph2026

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…

physics.geo-ph2025

Neural Earthquake Forecasting with Minimal Information: Limits, Interpretability, and the Role of Markov Structure

Jonas Koehler, Nishtha Srivastava, Kai Zhou +3

Forecasting earthquake sequences remains a central challenge in seismology, particularly under non-stationary conditions. While deep learning models have shown promise, their abili…

physics.geo-ph2025

A Deep Learning Pipeline for Large Earthquake Analysis using High-Rate Global Navigation Satellite System Data

Claudia Quinteros-Cartaya, Javier Quintero-Arenas, Andrea Padilla-Lafarga +5

Deep learning techniques for processing large and complex datasets have unlocked new opportunities for fast and reliable earthquake analysis using Global Navigation Satellite Syste…