Results 51 to 60 of about 5,315,268 (243)

Characterization of the seismic environment at the Sanford Underground Laboratory, South Dakota [PDF]

, 2010
An array of seismometers is being developed at the Sanford Underground Laboratory, the former Homestake mine, in South Dakota to study the properties of underground seismic fields and Newtonian noise, and to investigate the possible advantages of ...
, , A Sajeva, Acernese F, Acernese F DeSalvo R Harms J Sajeva A Shaffner L Trancynger T, Bartos I, Beccaria M, Blackburn L, Bormann P, Caddey S W, Carter J A, Chatterji S, Creighton T, D S Rabeling, F Acernese, F Barone, G Mueller, Gutenberg B, Harjes H-P, Harms J DeSalvo R Dorsher S Mandic V, Hild S, I Bartos, J F J van den Brand, J Harms, J Heise, L Naticchioni, Lück H, M Beker, M Coughlin, N Christensen, Pavlis G L, Peterson J, R DeSalvo, Robertson N A, S Dorsher, S Kandhasamy, S Márka, Smith J R for the LSC, T O'Keefe, T Trancynger, Tatsumi D, Tolman H L, V Mandic, V Wand, Withers M M, Young I R   +45 more
core   +4 more sources

Research on classification and identification of mine microseismic signals based on deep learning method

矿业科学学报, 2022
In order to accurately identify mine microseismic signals, this paper proposes a VGG4-CNN deep learning network model suitable for identifying mine microseismic signals. The model is written in Python language and built based on the PyTorch deep learning
Zhao Hongbao, Liu Rui, Liu Yihong, Zhang Yixiao, Gu Tao   +4 more
doaj   +1 more source

An improved method for hydrofracture induced microseismic event detection and phase picking [PDF]

, 2010
The ability to detect small microearthquakes and identify their P and S phase arrivals is a key issue in hydrofracture downhole monitoring because of the low signal-to-noise ratios. We apply an array-based waveform correlation approach (matched filter)
Ay, Erkan, Kuleli, Huseyin Sadi, Song, Fuxian, Toksoz, M. Nafi, Zhang, Haijiang   +4 more
core   +1 more source

Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10

Applied Sciences, 2023
Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams.
Yunpeng Zhang, Nan Li, Lihong Sun, Jincheng Qiu, Xiaokai Huang, Yan Li   +5 more
doaj   +1 more source

Rock falls impacting railway tracks. Detection analysis through an artificial intelligence camera prototype [PDF]

, 2017
During the last few years, several approaches have been proposed to improve early warning systems for managing geological risk due to landslides, where important infrastructures (such as railways, highways, pipelines, and aqueducts) are exposed elements.
Fantini, Andrea, Fiorucci, Matteo, Martino, Salvatore   +2 more
core   +3 more sources

Research on Automatic Classification of Coal Mine Microseismic Events Based on Data Enhancement and FCN-LSTM Network

Applied Sciences, 2023
Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning.
Guojun Shang, Li Li, Liping Zhang, Xiaofei Liu, Dexing Li, Gan Qin, Hao Li   +6 more
doaj   +1 more source

Seismic arrival enhancement through the use of noise whitening [PDF]

, 2017
A constant feature in seismic data, noise is particularly troublesome for passive seismic monitoring where noise commonly masks microseismic events. We propose a statistics-driven noise suppression technique that whitens the noise through the calculation
Belouchrani, Bensen, Birnie, Claire Birnie, Doug Angus, Forghani-Arani, Forghani-Arani, Gambino, Gutenberg, Hom, Kit Chambers, Maxwell, Maxwell, Oye, Roach, Roux, Scharf, Schorlemmer, Staněk, Wang   +19 more
core   +1 more source

Automatic picking method of microseismic first arrival time based on improved support vector machine

Gong-kuang zidonghua, 2023
The microseismic first arrival time picking is an important prerequisite for the high-precision positioning of the microseismic source. The traditional manual picking method is inefficient.
LI Tieniu, HU Binxin, LI Huakun, GENG Wencheng, HAO Pengcheng, JI Xubo, SUN Zengrong, ZHU Feng, ZHANG Hua, YANG Chengquan   +9 more
doaj   +1 more source

A Nonparametric Method for Automatic Denoising of Microseismic Data

Shock and Vibration, 2018
Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data.
Pingan Peng, Liguan Wang
doaj   +1 more source

Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques

Applied Sciences, 2023
Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal ...
Hongmei Shu, Ahmad Yahya Dawod
semanticscholar   +1 more source