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215 results on '"Zhu, Lupei"'

26. machine-learning-based method of detecting and picking the first P-wave arrivals of acoustic emission events in laboratory experiments.

Author

Li, Ziyu, Zhu, Lupei, Officer, Timothy, Shi, Feng, Yu, Tony, and Wang, Yanbin

Subjects
*ACOUSTIC emission, *CONVOLUTIONAL neural networks, *SEISMIC event location, *SEISMIC networks
Abstract

Detecting and picking the first P -wave arrivals of seismic events in seismograms is fundamental in observational seismology. Recently, several machine-learning-based algorithms have been developed to incorporate human expertise for picking P -wave arrival times automatically. One shortcoming of these models is that they pick arrival times at individual seismic stations separately, which need to be sorted and associated to identify the seismic event. Also, most of them rely on existence of P -wave arrivals in the seismograms to be picked. Here, we developed a machine-learning-based seismic event detection and P -wave arrival time picking method called MultiNet and applied it to acoustic emission (AE) waveform data recorded in laboratory experiments. The MultiNet uses 2-D waveform images from multichannel AE recordings as the input to a convolutional neural network (CNN) to detect whether there is an AE event in an image and, if so, uses a fully convolutional neural network (FCN) to pick the P -wave arrival time at each channel in the image. We tested the MultiNet using 550 known AE events recorded during syn-deformational phase transformation from olivine to spinel in Mg2GeO4 (an analogue to Mg2SiO4) in a high-pressure experiment. Waveform data of 50 events were used to train the neural networks and the rest of data were used to validate the method. At the optimal image length and detection threshold, the CNN was able to detect all 500 known events plus 48 more events missed previously. Overall, 98.7 per cent of P -wave arrival times picked by the FCN were within 0.5  |$\mu$| s from the manually picked times. The average picking errors at different channels range from 0.01 ± 0.05 to −0.06 ± 0.22  |$\mu$| s. Our method greatly reduces the amount of human labour in picking P -wave arrival times for event location and source moment tensor inversion. It can be easily adapted to process continuous waveform data of a seismic network for earthquake detection and location in real time. [ABSTRACT FROM AUTHOR]

Published
2022
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44. The Significance of Crust Structure and Continental Dynamics Inferred from Receiver Functions in West Yunnan

Author

Wang Qingcai, Zhu Lupei, and HE Chuansong

Subjects
geography, Underplating, Tectonics, geography.geographical_feature_category, Volcano, Oceanic crust, Continental crust, Geology, Crust, Tilted block faulting, Sial, Seismology
Abstract

In our study we collected the teleseismic record of 31 broadband stations and 9 PASSCAL stations in West Yunnan, as well as extracted more than a million receiver functions. Using the waveform model and stacking techniques, we calculated the earth crust thicknesses and Vp/Vs ratios below the stations and obtained 35 valid data points. At the same time, we evenly stacked the receiver functions at the same station and superimposed the two profiles' cross sections of the main tectonic units. The results show a clear difference between the crust thicknesses of different tectonic units. Because of the magma underplatting and delimanition of the lower crust in the role of deep process, the West Yunnan's crust can be divided two kinds—mafic-ultramafic and feldspathic crusts. The research also shows that the mafic-ultramafic crust corresponds to a good background of mineralization. The delamination of the lower crust is one of the leading causes for moderate to strong earthquake prone in central Yunnan. The thinner crust and high velocity ratio as well as the multimodal structure of Ps in the Tengchong volcanic area confirms existence of a deep process of the strong magma underplating. Due to the basic crust structure and nature, it is believed that the Honghe fault is a main suture of the Gondwana and Eurasia continents.

Published
2009
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