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 …

Seismology is among the ancient sciences that concentrate on earthquake disaster
management (EQDM), which directly impact human life and infrastructure resilience. Such a …

The ever‐increasing networks and quantity of seismic data drive the need for seamless and
automatic workflows for rapid and accurate earthquake detection and location. In recent …

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

While computer science has seen remarkable advancements in foundation models, they
remain underexplored in geoscience. Addressing this gap, we introduce a workflow to …

Short term prediction of earthquake magnitude, time, and location is currently not possible.
In some cases, however, documented observations have been retrospectively considered …

After decades of low but continuing activity, applications of machine learning (ML) in solid
Earth geoscience have exploded in popularity. This special collection provides a snapshot …

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

Earthquake monitoring is vital for understanding the physics of earthquakes and assessing
seismic hazards. A standard monitoring workflow includes the interrelated and …