Seismic waves from earthquakes and other sources are used to infer the structure and
properties of Earth's interior. The availability of large-scale seismic datasets and the …

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 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 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 …

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

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 Machine‐learning (ML) methods have seen widespread adoption in seismology in
recent years. The ability of these techniques to efficiently infer the statistical properties of …

Robust automatic event detection and location is central to real‐time earthquake monitoring.
With the increase of computing power and data availability, automated workflows that utilize …

Neural phase pickers—neural networks designed and trained to pick seismic phase arrivals—
have proven to be a powerful tool for developing earthquake catalogs. However, these …

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