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 …

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

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 …

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 …

Seismic phase association connects earthquake arrival‐time measurements to their
causative sources. Effective association must determine the number of discrete events, their …

In this study, we build a multi‐station phase‐picking model named EdgePhase by
integrating an Edge Convolutional module with a state‐of‐the‐art single‐station phase …

ABSTRACT The 2020–2023 southwestern Puerto Rico seismic sequence, still ongoing in
2023, is remarkable for its multiple‐fault rupture complexity and elevated aftershock …

In recent years, artificial intelligence technology has exhibited great potential in seismic
signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled …

Seismic wave arrival time measurements form the basis for numerous downstream
applications. State‐of‐the‐art approaches for phase picking use deep neural networks to …

Optimized deep learning (DL)-based workflows can improve the efficiency and accuracy of
earthquake detection and location processes. This article introduces a six-step automated …