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 …

The deep magmatic architecture of the Hawaiian volcanic system is central to understanding
the transport of magma from the upper mantle to the individual volcanoes. We leverage …

Earthquake monitoring workflows are designed to detect earthquake signals and to
determine source characteristics from continuous waveform data. Recent developments in …

Abstract Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake
monitoring and subsurface imaging. However, its distinct characteristics, such as unknown …

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 …

The Arxan volcanic group is one of the largest active volcanic fields in China. Its last eruption
occurred∼ 2,000 years ago, but it still has a potential of eruption in the future. The spatial …

A high‐precision earthquake catalog is made from continuous waveform data recorded at
360 portable seismographs deployed for one month in the Changbaishan‐Tianchi volcanic …

Given the recent developments in machine-learning technology, its application has rapidly
progressed in various fields of earthquake seismology, achieving great success. Here, we …

In May 2018, the Mayotte island, located in the Indian Ocean, was affected by an
unprecedented seismic crisis, followed by anomalous on-land surface displacements in July …

The application of deep‐learning‐based seismic phase pickers has surged in recent years.
However, the efficacy of these models when applied to monitoring volcano seismicity has yet …