Picking Regional Seismic Phase Arrival Times with Deep Learning
SeismicaSparse instrumental coverage for much of the Earth requires working with regional seismic phases for effective seismic monitoring. Machine learning phase pickers to date have focused on local earthquake recordings.
Albert Leonardo Aguilar Suarez +1 more
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Application of Neural Network Automatic Event Detection for Reservoir-Triggered Seismicity Monitoring Networks [PDF]
SensorsThis study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations.
Jan Wiszniowski +4 more
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A Lightweight Network for Seismic Phase Picking on Embedded Systems
IEEE AccessPhase picking is a critical task in seismic data processing, where deep learning methods have been applied to enhance its accuracy. While lightweight deep learning networks have been optimized for edge computing devices, there is a lack of networks ...
Yadongyang Zhu +4 more
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EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking
Frontiers in Earth Science, 2022 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 event. The phase-arrival time is usually picked manually. However, its capacity
Wei Li +17 more
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Automatic Horizon Picking Using Multiple Seismic Attributes and Markov Decision Process
Remote Sensing, 2023 Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing.
Chengliang Wu +5 more
doaj +1 more source
Sensors, 2022 First arrival travel time picking is an important step in many seismic data-processing applications. Most first arrival picking methods search for a sudden jump in seismic energy at trace onsets, which is clearly appropriate for minimum-phase data.
Amen Bargees, Abdullatif A. Al-Shuhail
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Seismicity‐Scanning Based on Navigated Automatic Phase‐Picking [PDF]
Journal of Geophysical Research: Solid Earth, 2019 AbstractWe propose a new method, named Seismicity‐Scanning based on Navigated Automatic Phase‐picking (S‐SNAP), that is capable of delineating complex spatiotemporal distributions of seismicity. This novel algorithm takes a cocktail approach that combines source scanning, kurtosis‐based phasepicking, and the maximum intersection location technique into
Fengzhou Tan +3 more
openaire +1 more source
Automatic seismic phase picking and consistent observation error assessment: application to the Italian seismicity [PDF]
Geophysical Journal International, 2006 ISSN:0956 ...
Di Stefano, R. +5 more
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LEQNet: Light Earthquake Deep Neural Network for Earthquake Detection and Phase Picking
Frontiers in Earth Science, 2022 Developing seismic signal detection and phase picking is an essential step for an on-site early earthquake warning system. A few deep learning approaches have been developed to improve the accuracy of seismic signal detection and phase picking.
Jongseong Lim +8 more
doaj +1 more source
Arrival times by Recurrent Neural Network for induced seismic events from a permanent network
Frontiers in Big Data, 2023 We have developed a Recurrent Neural Network (RNN)-based phase picker for data obtained from a local seismic monitoring array specifically designated for induced seismicity analysis. The proposed algorithm was rigorously tested using real-world data from
Petr Kolar +3 more
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