Results 31 to 40 of about 953 (196)
Benchmarking seismic phase associators: Insights from synthetic scenarios
SeismicaReliable seismicity catalogs are fundamental for seismological analysis. Following phase picking, phase association groups arrivals into sets with consistent origins (i.e., events), determines event counts, and identifies outlier picks.
Jorge Puente Huerta +3 more
doaj +4 more sources
Earth and Space ScienceAlthough deep learning has advanced seismic analysis, accurately identifying seismic phases in noisy or complex waveform environments remains challenging.
Jiashu Guo +3 more
doaj +2 more sources
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
doaj +1 more source
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
CRPN: A cascaded classification and regression DNN framework for seismic phase picking*
Earthquake Science, 2020 Ziye Yu, Risheng Chu, Weitao Wang
exaly +2 more sources
An array-assisted deep learning approach to seismic phase-picking [PDF]
Chinese Science Bulletin, 2020 Rapid increase of seismic data poses challenges to seismic data processing, in which phase-picking is a crucial procedure and accordingly attracts attention from seismologists. Among the breakthroughs in deep learning methods for seismic data processing, seismologists have especially proposed many seismic phase-picking methods based on deep learning ...
Fang Liu +4 more
openaire +1 more source
First Arrival Picking of Seismic Data Based on Trace Envelope
IEEE Access, 2019 We introduce a new method for first arrival traveltime picking based on the maximum difference between adjacent points of the envelope (MDPE) of a seismic trace.
Abdullah A. Al-Mashhor +3 more
doaj +1 more source
Sensors, 2021 When recording seismic ground motion in multiple sites using independent recording stations one needs to recognize the presence of the same parts of seismic waves arriving at these stations.
Andrey Stepnov +2 more
doaj +1 more source
Advances on the automatic estimation of the P-wave onset time
Annals of Geophysics, 2016 This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers originated during the TOMO-ETNA experiment. Air-gun shots produced by the vessel “Sarmiento de Gamboa” and contemporary passive seismicity occurring in the ...
Luz García +15 more
doaj +1 more source