Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Earthquake signal detection and seismic phase picking are challenging tasks in the
processing of noisy data and the monitoring of microearthquakes. Here we present a global …

Seismology is a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

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 …

本文利用深度学习算法PhaseNet, 震相关联算法REAL 以及多种定位算法快速地构建了2021
年5 月21 日漾濞MS 6.4 主震前3 天至后7 天的高分辨率地震目录, 揭示了漾濞地震序列的精细 …

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 …

The two principle earthquakes of the July 2019 Ridgecrest, California, earthquake
sequence, MW 6.4 and 7.1, and their immediate foreshocks and thousands of aftershocks …

Abstract On 21 May 2021, a magnitude (MS) 6.4 earthquake with significant foreshocks
struck Yangbi, Yunnan. We utilized a deep-learning algorithm-PhaseNet, an earthquake …

Low-frequency information is important in reducing the nonuniqueness of absolute
impedance inversion and for quantitative seismic interpretation. In traditional model-driven …