Velocity model optimization for surface microseismic monitoring via amplitude stacking

HaiYu, Jiang; Wang, Zhongren; Zeng, Xin; Lü, Hao; Zhou, Xingyu; Chen, Zubin

doi:10.1016/j.jappgeo.2016.10.032

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Wavelet analysis has been demonstrated to be a useful means to address nonstationary signals, such as earthquakes and electromagnetic radiation [31,32]. However, the characteristic of wavelet nite length will cause the leakage of signals energy, and the analysis results are substantially in uenced by the choice of wavelet basis functions [33].…”

Section: Microseismic Signal Acquisition and Denoisingmentioning

confidence: 99%

“…Based on a study of the characteristics of mine microseismic signals, Vallejos and Mckinnon proposed a neural network model to classify the sources of microseismic events [20]. Jiang et al used velocity model optimization for surface microseismic monitoring via amplitude stacking to enhance the signal-to-noise ratio in the coal-bed gas reservoir in Western China [21]. To investigate the generation mechanism of microseismic signals, most scholars use numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

Wang

Lyu

et al. 2019

Shock and Vibration

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An experiment designed to simulate coal during excavation was conducted. Microseismic signals of coal under vibration conditions during excavation and subsequent waiting time of the coal roadway at different excavation speeds were collected and analyzed. During the excavation and subsequent waiting time, the stress in coal is redistributed, and the concentrated stress is gradually transferred to the deeper section of the coal seam. The Hilbert–Huang transform (HHT) is used to effectively denoise the collected signals. According to the noise-reduced signal, the amplitude and pulse number of the microseismic signals emitted during the excavation process are much larger than those of the waiting time process. During excavation, the energy and event numbers of microseismic signals increase first and then decrease as the excavation speed increases. The faster the excavation speed, the more the energy, and the higher the event numbers of the microseismic signals released during the subsequent waiting time. When the excavation speed is faster, more elastic potential accumulates in the coal seam and the concentration stress is greater. As the concentrated stress moves forward in time without excavation, more coal seams fail, and more microseismic signals are released. The microseismic signal and the stress evolution law can provide a reasonable explanation for the forward movement of the concentrated stress and coal failure during roadway excavation.

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Section: Microseismic Signal Acquisition and Denoisingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

Wang

Lyu

et al. 2019

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…While this velocity model is particularly useful when the layer and tunnel are nearly perpendicular to one another, it is less effective when they form a certain angle or when the layer distribution is more complex, resulting in inconsistencies in sensor group velocity [ 19 ]. In addition, it is essentially still an SSH algorithm, and the introduction of more unknown parameters will lead to instability in the solution process and increase computational complexity [ 20 , 21 ]. In order to generate velocity models under different geological conditions, Ma et al [ 22 ] proposed four different equivalent velocity models.…”

Section: Introductionmentioning

confidence: 99%

An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

Shen

Wang

Jiang

et al. 2023

Sensors

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The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a “source–station” velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the “source–station” velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels.

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Microseismic Data Processing

Akram¹

2020

Understanding Downhole Microseismic Data Analysis

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Velocity model optimization for surface microseismic monitoring via amplitude stacking

Cited by 10 publications

References 21 publications

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

Microseismic Data Processing

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