Results 111 to 120 of about 953 (196)

Deep learning detects uncataloged low-frequency earthquakes across regions

Seismica
Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize.
Jannes Münchmeyer, Sophie Giffard-Roisin, Marielle Malfante, William Frank, Piero Poli, David Marsan, Anne Socquet   +6 more
doaj   +1 more source

Automatic picking of direct P, S seismic phases and fault zone head waves [PDF]

, 2014
We develop a set of algorithms for automatic detection and picking of direct P and S waves, as well as fault zone head waves (FZHW), generated by earthquakes on faults that separate different lithologies and recorded by local seismic networks. The S-wave
Ross, Z. E., Ben-Zion, Y.
core  

Stochastic determination of arrival time and initial polarity of seismic waveform

Earth, Planets and Space
In this study, we have developed and implemented a new technology capable of probabilistically selecting phase arrival times and determining the initial polarity of seismic waveforms without the requirement of prior information.
Weilai Pei, Jiancang Zhuang, Shiyong Zhou   +2 more
doaj   +1 more source

Deep learning for seismic phase detection and picking in the aftershock zone of 2008 M_W 7.9 Wenchuan Earthquake [PDF]

, 2019
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 more reliable phase detection and picking.
Li, Chenyu, Fang, Lihua, Li, Zefeng, McClellan, James, Yao, DongDong, Zhu, Lijun, Peng, Zhigang   +6 more
core  

Development of an instantaneous frequency estimation pipeline for compressional and shear wave arrival picking : application to quarry blast data

, 2019
Many seismology applications such as earthquake hypocenter determination, source mechanism analysis, and hydrocarbon reservoir imaging, require accurate compressional and shear wave arrival time picking.
Sigiuk, Ahmed Hasin Issa
core  

Hybrid Validation of a Quality-Controlled, Waveform-Centered AI Framework with Optional Multi-Sensor Support for Seismic Monitoring. [PDF]

Sensors (Basel)
Abdykadyrov A, Alipuly Y, Mamadiyarov M, Tashev B, Yerkinova A, Tazhen K.   +5 more
europepmc   +1 more source

Seismic phase picking in China Seismic Array using a deep convolutional neuron network*

Earthquake Science, 2020
Long Zhang, Lihua Fang, Weilai Wang, Zuoyong Lv   +3 more
openaire   +1 more source

U-Trans: a foundation model for seismic waveform representation and enhanced downstream earthquake tasks. [PDF]

Sci Rep
Saad OM, Chen Y, Alkhalifah T.
europepmc   +1 more source

Efficient seismic data denoising via multi-scale attention network with depthwise separable and residual dilated convolutions. [PDF]

Sci Rep
Yao Z, Hao L, Qin L, Chen J, Li W.
europepmc   +1 more source

Robust Phase Association and Simultaneous Arrival Picking for Downhole Microseismic Data Using Constrained Dynamic Time Warping. [PDF]

Sensors (Basel)
Wang T, Li L, Wen S, Lv Y, Yu Z, He C.
europepmc   +1 more source