29 citations · 42 across the 18 of their papers we have counts for
18 papers
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…
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…
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…
High Magnitude Earthquake Identification Using an Anomaly Detection Approach on HR GNSS Data
Javier Quintero Arenas, Claudia Quinteros Cartaya, Andrea Padilla Lafarga +4
Earthquake early warning systems are crucial for protecting areas that are subject to these natural disasters. An essential part of these systems is the detection procedure. Tradit…
When linear inversion fails: neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice
Abolfazl Komeazi, Georg Rümpker, Johannes Faber +2
In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice. We employ ray tracing to simulate the propagation of s…
SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning
Wei Li, Megha Chakraborty, Claudia Quinteros Cartaya +4
Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstr…
Testing the Potential of Deep Learning in Earthquake Forecasting
Jonas Koehler, Wei Li, Johannes Faber +2
Reliable earthquake forecasting methods have long been sought after, and so the rise of modern data science techniques raises a new question: does deep learning have the potential…
Exploring a CNN Model for Earthquake Magnitude Estimation using HR-GNSS data
Claudia Quinteros Cartaya, Jonas Koehler, Wei Li +2
High rate Global Navigation Satellite System (HR GNSS) data can be highly useful for earthquake analysis as it provides continuous high-rate measurements of ground motion. This dat…
Real-time Earthquake Monitoring using Deep Learning: a case study on Turkey Earthquake Aftershock Sequence
Wei Li, Jonas Koehler, Megha Chakraborty +3
Seismic phase picking and magnitude estimation are essential components of real time earthquake monitoring and earthquake early warning systems. Reliable phase picking enables the…
CREIME: A Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation
Megha Chakraborty, Darius Fenner, Wei Li +5
The detection and rapid characterisation of earthquake parameters such as magnitude are of prime importance in seismology, particularly in applications such as Earthquake Early War…
Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification
Wei Li, Yu Sha, Kai Zhou +4
Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With impr…
A study on the effect of input data length on deep learning based magnitude classifier
Megha Chakraborty, Wei Li, Johannes Faber +3
The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estima…
AWESAM: A Python Module for Automated Volcanic Event Detection Applied to Stromboli
Darius Fenner, Georg Ruempker, Wei Li +5
Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive majo…
EPick: Multi-Class Attention-based U-shaped Neural Network for Earthquake Detection and Seismic Phase Picking
Wei Li, Megha Chakraborty, Darius Fenner +5
Earthquake detection and seismic phase picking not only play a crucial role in travel time estimation of body waves(P and S waves) but also in the localisation of the epicenter of…
Sunda-arc seismicity: continuing increase of high-magnitude earthquakes since 2004
Nishtha Srivastava, Jonas Koehler, F. Alejandro Nava +9
Spatial and temporal data for earthquakes with magnitude M greater than or equal to 6.5 can provide crucial information about the seismic history and potential for large earthquake…