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 …

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 …

The detection and rapid characterization of earthquake parameters such as magnitude are
important in real‐time seismological applications such as Earthquake Monitoring and …

Earthquake detection and seismic phase picking play a crucial role in the travel-time
estimation of P and S waves, which is an important step in locating the hypocenter of an …

In this article, we show that the real-time earthquake detection and phase picking with
multitask attention network (RED-PAN) can carry out earthquake detection and seismic …

High‐precision seismic phase arrivals are a prerequisite for building reliable velocity models
with travel‐time tomography. There has recently been a growing use of seismic phase …

Given the recent developments in machine-learning technology, its application has rapidly
progressed in various fields of earthquake seismology, achieving great success. Here, we …

Data-driven methods for picking the first-arrival of seismic waves can encounter challenges
with generalization when they are faced with out-of-distribution data that falls outside their …

The polarity of first P-wave arrivals plays a significant role in the effective determination of
focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not …

Accurate and (near) real‐time earthquake monitoring provides the spatial and temporal
behaviors of earthquakes for understanding the nature of earthquakes, and also helps in …