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Title: A machine-learning-based method of detecting and picking the first P-wave arrivals of acoustic emiss

Journal Article · · Geophysical J.
DOI:https://doi.org/10.1093/gji/ggac148· OSTI ID:1900732
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Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Organization:
DOE - Office Of Science; FOREIGN; National Science Foundation (NSF)
OSTI ID:
1900732
Journal Information:
Geophysical J., Vol. 230, Issue (3)
Country of Publication:
United States
Language:
ENGLISH

References (12)

Automatic phase pickers: Their present use and future prospects journal December 1982
Wavelet transform methods for phase identification in three-component seismograms journal December 1997
Automatic picking of P and S phases using a neural tree journal January 2006
A procedure for the modeling of non-stationary time series journal December 1978
Multi-component autoregressive techniques for the analysis of seismograms journal June 1999
Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking journal August 2020
P Wave Arrival Picking and First-Motion Polarity Determination With Deep Learning journal June 2018
Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings journal June 1999
A laboratory nanoseismological study on deep-focus earthquake micromechanics journal July 2017
An artificial neural network approach for broadband seismic phase picking journal June 1999
An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks journal November 2017
Passive seismic imaging of subwavelength natural fractures: theory and 2-D synthetic and ultrasonic data tests journal December 2018

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