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 discipline of seismology is based on observations of ground motion that are inherently
undersampled in space and time. Our basic understanding of earthquake processes and our …

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 …

A new generation of earthquake catalogs developed through supervised machine-learning
illuminates earthquake activity with unprecedented detail. Application of unsupervised …

Earthquake phase association algorithms aggregate picked seismic phases from a network
of seismometers into individual sesimic events and play an important role in earthquake …

We propose to implement a compact convolutional transformer (CCT) for picking the
earthquake phase arrivals (EQCCT). The proposed method consists of two branches, with …

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

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 …

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase
detection/picking, association, and location tasks. In recent years, the accuracy of these …