Results 1 to 10 of about 15,161 (215)

Phase Neural Operator for Multi‐Station Picking of Seismic Arrivals

Geophysical Research Letters, 2023
Seismic wave arrival time measurements form the basis for numerous downstream applications. State‐of‐the‐art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic ...
Hongyu Sun, Zachary E. Ross, Weiqiang Zhu, Kamyar Azizzadenesheli   +3 more
doaj   +6 more sources

Test-Time Augmentations and Quality Controls for Improving Regional Seismic Phase Picking [PDF]

Sensors
Regional seismic phases are essential for imaging Earth’s internal structure. Although extensive regional seismic networks are publicly available worldwide, only a small fraction of recorded phase arrivals are picked for constraining earthquake source ...
Bingyao Han, Lin Tang, Li Ma, Hua Kong, Zhuowei Xiao   +4 more
doaj   +3 more sources

Application of a convolutional neural network for seismic phase picking of mining-induced seismicity. [PDF]

Geophys J Int, 2021
SUMMARYMonitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rockbursting, more safely and efficiently. Unfortunately, processing MIS data usually
Johnson SW, Chambers DJA, Boltz MS, Koper KD.   +3 more
europepmc   +4 more sources

Seismic Phase Picking Algorithm Based on Improved Bi-LSTM

Taiyuan Ligong Daxue xuebao, 2021
In order to solve the problem of traditional waveform detection algorithms such as relying on manual setting of thresholds and low precision of phase picking, a seismic phase picking algorithm based on improved Bi-LSTM was proposed.
Zhenhua HAN, Haoyu GUO, Yu LI, Ling ZHANG, Xiufang FENG   +4 more
doaj   +2 more sources

MFU-Net: a multi-scale fusion U-Net for seismic phase picking [PDF]

Scientific Reports
Seismic phase picking aims to accurately identify and label the arrival times of different types of seismic waves (e.g., P-waves and S-waves) from waveform data, serving as a fundamental step in seismological research and related applications.
Lihua Wu, Jiaquan Yan, Yanming Zhang, Mingquan Hong, Hongcai Zhang, Shirong Liao   +5 more
doaj   +2 more sources

PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning

Earth and Space Science, 2023
Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption.
T. Bornstein, D. Lange, J. Münchmeyer, J. Woollam, A. Rietbrock, G. Barcheck, I. Grevemeyer, F. Tilmann   +7 more
doaj   +7 more sources

Polarization filtering for automatic picking of seismic data and improved converted phase detection [PDF]

Geophysical Journal International, 2001
Data-adaptive polarization filtering is used to improve the detection of converted seismic phases. Both direct waves and mode-converted PS and SP arrivals may be more easily picked on the filtered records. An autopicking routine is applied that cuts the
Adams, Allen, Allen, Ansell, Bannister, Bataille, Boschetti, Calhaem, Dai, Earle, Eberhart-Phillips, Fedorenko, Gledhill, Gledhill, Hasegawa, Helffrich, Jones, Kayal, Klumpen, Nakamura, Park, Patane, Reading, Roberts, Robinson, Ruud, SAC, Samson, Samson, Shearer, Smith, Stephens, Stuart, Thirot, Wagner, Withers, Woodgold, Zhao   +37 more
core   +4 more sources

DTPP:An efficient depthwise separable TCN for seismic phase picking

Artificial Intelligence in Geosciences
With the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years.
Shuai Lv, Yuxiang Peng
doaj   +2 more sources

EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking

Geochemistry, Geophysics, Geosystems, 2022
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‐picking model, EQTransformer.
Tian Feng, Saeed Mohanna, Lingsen Meng
doaj   +2 more sources

Detection and Monitoring of Mining-Induced Seismicity Based on Machine Learning and Template Matching: A Case Study from Dongchuan Copper Mine, China [PDF]

Sensors
The detection and monitoring of mining-induced seismicity are essential for understanding the mechanisms behind earthquakes and mitigating seismic hazards.
Tao Wu, Zhikun Liu, Shaopeng Yan
doaj   +2 more sources