Evidence map›Paper›PMID 41305172›Full record

ArticleSensors (Basel, Switzerland)2025

High-Precision Waveform Stacking Location Method for Microseismic Events Based on S-Transform.

Hongpeng Zhao, Jiulong Cheng, Grzegorz Lizurek, Chuanpeng Wang, Yan Li, Dengke He, Zhongzhong Xu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hongpeng ZhaoCollege of Earth Science and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.ORCID 0000-0001-9176-5698
Jiulong ChengCollege of Earth Science and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.ORCID 0000-0001-6470-6406
Grzegorz LizurekInstitute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland.ORCID 0000-0002-4801-4888
Chuanpeng WangCCTEG Coal Mining Research Institute, Beijing 100013, China.
Yan LiCCTEG Coal Mining Research Institute, Beijing 100013, China.
Dengke HeCollege of Earth Science and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
Zhongzhong XuCollege of Earth Science and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.ORCID 0000-0003-1331-7181

Funding

China Scholarship Council 202406430096Fundamental Research Funds for the Central Universities BBJ2024045National Natural Science Foundation of China 42474189
6 · The paper itself

Abstract

The waveform stacking location method achieves microseismic source localization by computing characteristic functions (CFs) and stacking multi-channel data, without phase picking. It has been widely applied in geotechnical engineering. However, the low signal-to-noise ratio (SNR) caused by weak event energy and ambient noise often degrades localization accuracy. To enhance the localization precision and stability under low SNR conditions, this study employs the Stockwell transform (S-transform) to convert noisy time-domain data into the time-frequency domain. By analyzing the energy distribution of microseismic signal and noise in the time-frequency domain, frequency and time coefficients are introduced to enhance the energy of microseismic signal. Event location is achieved through the computation of CFs and multiple-cross-correlation stacking. Comparison of the location results when computing the CFs by the new method, the short-term average to long-term average ratio (STA/LTA) method, and the envelope (Env) method under varying noise levels demonstrates the superior noise resistance and improved localization accuracy of the new method. Finally, the effectiveness of the new method is validated using real seismic data collected from a coal mine.

Indexed as

characteristic functionsfrequency and time coefficientsgeotechnical engineeringmicroseismic source localizationStockwell transform

Identifiers

PMID41305172
PMCPMC12656627

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