Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

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 a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

A nearly 20-year hiatus in major seismic activity in southern California ended on 4 July 2019
with a sequence of intersecting earthquakes near the city of Ridgecrest, California. This …

This article provides an overview of current applications of machine learning (ML) in
seismology. ML techniques are becoming increasingly widespread in seismology, with …

Earthquakes follow a well-known power-law size relation, with smaller events occurring
much more often than larger events. Earthquake catalogs are thus dominated by small …

The vibrant evolutionary patterns made by earthquake swarms are incompatible with
standard, effectively two-dimensional (2D) models for general fault architecture. We …

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 …