The increasing volume of seismic data from long-term continuous monitoring motivates the
development of algorithms based on convolutional neural network (CNN) for faster and …

Seismic phase detection, identification and first-onset picking are basic but essential
routines to analyse earthquake data. As both the number of seismic stations, globally and …

We developed a hybrid algorithm using both convolutional and recurrent neural networks
(CNNs and RNNs, respectively) to pick phases from archived continuous waveforms in two …

To optimally monitor earthquake‐generating processes, seismologists have sought to lower
detection sensitivities ever since instrumental seismic networks were started about a century …

When a seismometer network records an earthquake, operators will manually review the
waveforms and identify the wave phases, a task known as phase picking. Manual phase …

We here present one lightweight phase picking network (LPPN) to pick P/S phases from
continuous seismic recordings. It first classifies the phase type for a segment of waveform …

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

We develop a capsule neural network (CapsPhase) for seismic data classification and
picking. CapsPhase consists of several layers, eg, convolutional, primary capsule, and digit …

Over the past two decades, the amount of available seismic data has increased significantly,
fueling the need for automatic processing to use the vast amount of information contained in …

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 …