Related papers: California Earthquake Dataset for Machine Learning…
We introduce a modular software framework designed to integrate distributed acoustic sensing (DAS) data into operational earthquake monitoring systems. Building on the infrastructure of the Advanced National Seismic System (ANSS) and the…
Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed…
Earthquakes can be detected by matching spatial patterns or phase properties from 1-D seismic waves. Current earthquake detection methods, such as waveform correlation and template matching, have difficulty detecting anomalous earthquakes…
Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P- and…
Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective…
Distributed Acoustic Sensing (DAS) enables high-resolution and long-duration monitoring of marine acoustic and seismic activity by turning existing fiber-optic cables into dense sensor arrays. However, extracting diverse signals from…
The Earthquake Network research project implements a crowdsourced earthquake early warning system based on smartphones. Smartphones, which are made available by the global population, exploit the Internet connection to report a signal to a…
Earthquakes are a major threat to nations worldwide. Earthquake detection is an important scientific challenge, not only for its social impacts, but also since it reflects the actual degree of understanding of the physical processes…
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical…
The rapid proliferation of deep-learning-based detection and association methods has greatly expanded automatically generated earthquake catalogs, but has also introduced false detections, mis-associated arrivals, and poorly constrained…
In recent years, AI and deep learning earthquake detectors, combined with an increasing number of dense seismic networks deployed worldwide, have further contributed to the creation of massive seismic catalogs, significantly lowering their…
Earthquake early warning systems play crucial roles in reducing the risk of seismic disasters. Previously, the dominant modeling system was the single-station models. Such models digest signal data received at a given station and predict…
The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less…
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 to learn this pattern? In this study, we leverage the large…
This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a…
Earthquake forecasting remains a significant scientific challenge, with current methods falling short of achieving the performance necessary for meaningful societal benefits. Traditional models, primarily based on past seismicity and…
Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory…
Point processes have been dominant in modeling the evolution of seismicity for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most popular. Recent advances in machine learning have constructed highly flexible point…
Predicting region-wide structural responses under seismic shaking is essential for enhancing the effectiveness of earthquake engineering task forces such as earthquake early warning and regional seismic risk and resilience assessments.…
Accurate prediction of structural failure modes under seismic excitations is essential for seismic risk and resilience assessment. Traditional simulation-based approaches often result in imbalanced datasets dominated by non-failure or…