Distributed acoustic sensing (DAS) is a new, relatively inexpensive technology that is rapidly
demonstrating its promise for recording earthquake waves and other seismic signals in a …

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 …

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

The fast‐growing interests in high spatial resolution of seismic imaging and high temporal
resolution of seismic monitoring pose great challenges for fast, efficient, and stable data …

This article is composed of three independent commentaries about the state of Integrated,
Coordinated, Open, Networked (ICON) principles in the American Geophysical Union …

Towards a deeper consciousness of the human being in preserving the natural environment
of our planet, huge effort is nowadays being exerted by scientists all over the world to use …

Seismoacoustic signals at local distance (<∼ 10 km) are widely used as important
constraints on source parameters for near‐surface events, yet the seismoacoustic wave …

We present a deep‐learning approach for earthquake detection using waveforms from a
seismic array consisting of multiple seismographs. Although automated, deep‐learning …

The ambient seismic field is now routinely used for imaging and monitoring purposes. Most
commonly, applications aim at resolving crustal‐scale features and utilize ocean‐generated …

This paper discusses a range of issues related to the interpretation of time variations of
geophysical fields as a response of the geophysical environment to lunar-solar tidal …