Results 151 to 160 of about 1,776,592 (192)

CNN-Transformer for Microseismic Signal Classification

Electronics, 2023
The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar. In order to identify the true microseismic signals with a microseismic monitoring system quickly and accurately, this paper proposes a
Zhihui Wang, Xing-Li Zhang
exaly   +3 more sources

Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

Energies, 2021
It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems.
Sungil Kim
exaly   +2 more sources

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

Applied Sciences (Switzerland), 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 ...
Ahmad Yahya Dawod, Hongmei Shu
exaly   +2 more sources

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Microseismic signal of a spring gravimeter

Measurement Techniques, 2007
It is shown that a spring gravimeter responds to microseisms with a pseudo-gravitational signal. Mathematical description of this signal is provided.
V. P. Dedov, V. M. Dorokhin, A. I. Kalenitskii, B. P. Filimonov   +3 more
openaire   +1 more source

Spectral Evaluation of Microseismic Signals

68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006, 2006
Spectral evaluation is a very important operation in the processing of microseismic signals. In this poster we consider the microseismic signals that can be recorded at a point from the Earth’s surface as a random process (signal) with many realizations and we present a methodology for the evaluation and statistical stabilization of its frequency ...
V. Bardan, D. Zugravescu, L. Asimopolos
openaire   +1 more source

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2021
Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock ...
Jiangfeng Li, Vladimir Stankovic, Stella Pytharouli   +2 more
exaly   +3 more sources

Tracking microseismic signals from the reservoir to surface

The Leading Edge, 2012
Since the launch of commercial microseismic mapping of hydraulic fracturing in the Barnett Shale in 2000, microseismic has become the de facto geophysical technique to characterize stimulated fracture networks in unconventional reservoirs. Effective stimulation of these resources through hydraulic stimulations along multiple intervals of horizontal ...
S.C. Maxwell, D. Raymer, M. Williams, P. Primiero   +3 more
openaire   +1 more source

Research on microseismic signal identification through data fusion

Computers and Geosciences
Xinming Lu, Xing-Li Zhang, Rui-Sheng Jia
exaly   +3 more sources

Weak signal detection using multiscale morphology in microseismic monitoring

Journal of Applied Geophysics, 2016
Abstract Microseismic events caused by hydraulic fracturing are usually very weak. The magnitude range of microseismic signals is usually from − 3 to 1 Mw. Processing techniques such as band-pass filtering, are widely adopted to improve the signal-to-noise (S/N) ratio of microseismic data, while with a degradation of signal quality.
Huijian Li, Yangkang Chen, Xiaoqing Chen
exaly   +2 more sources

Identification of Microseismic Signals of Rock Mass Fracture in Mines

2020 7th International Conference on Information Science and Control Engineering (ICISCE), 2020
The underground monitoring environment is more complicated, and the rock mass rupture signal is often mixed with various noises. How to identify and extract the rock failure signal are the basis of application research on microseismic monitoring technology. In order to solve this problem, a combined analysis method based on qualitative and quantitative
Wu Qingliang, Wu Mengjun, Chen Tian, Feng Li, Zhao Bingchen   +4 more
openaire   +1 more source