First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

Guo, Xiaolong

doi:10.1155/2021/5548346

Cited by 9 publications

(3 citation statements)

References 51 publications

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“…In comparative analysis with previous studies, our methodology exhibits superior performance in terms of the mean absolute error (MAE) and mean-squared error (MSE). Prior studies achieved an MAE of 1.21 [25] and an MSE of 0.06 [27], while our study achieved significantly lower values, with an MSE of 0.0031 and an MAE of 0.0177.…”

Section: Discussion and Comparison With Similar Workcontrasting

confidence: 63%

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Choi,

Lee,

Kim

et al. 2024

Electronics

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The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and automated methods using Short-Term Average to Long-Term Average algorithms. However, these approaches encounter significant performance challenges, especially in the presence of real-time background noise. To overcome this limitation, this study proposes a novel P-wave FAP detection method that employs the U-Net model and incorporates spectrogram transformation techniques for seismic signals. Seismic signals, similar to those encountered in South Korea, were generated using the stochastic model simulation program. Synthesized WGN (White Gaussian Noise) was added to replicate background noise. The resulting signals were transformed into 2D spectrogram images and used as input data for the U-Net model, ensuring precise P-wave FAP detection. In the experimental result, it demonstrated strong performance metrics, achieving an MSE of 0.0031 and an MAE of 0.0177, and an RMSE of 0.0195. Additionally, it exhibited precise FAP detection capabilities in image prediction. The developed U-Net-based model exhibited exceptional performance in accurately detecting P-wave FAP in seismic signals with varying amplitudes. Through the developed model, we aim to contribute to the advancement of microseismic monitoring technology used in various industrial fields.

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Section: Discussion and Comparison With Similar Workcontrasting

confidence: 63%

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Choi,

Lee,

Kim

et al. 2024

Electronics

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“…e spatiotemporal characteristics of microseismic activities during the monitoring period were analyzed, and the potentially dangerous areas of rockburst were identified and delineated. Guo [10] proposed a firstarrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of microseismic time-difference source location. Zhang [11] studied the basic characteristics of MS events in heading face based on a running vibration signal acquisition system, including the occurrence position, main frequency range, maximum amplitude (MA) range, event duration, and relationship with geological structure.…”

Section: Introductionmentioning

confidence: 99%

Research on Temporal and Spatial Distribution Characteristics of Microseismic Events of Slip-Type Rockburst

Xu¹,

Song

Song³

et al. 2022

Shock and Vibration

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Based on the microseismic monitoring data of underground cavern of Shuangjiangkou Hydropower Station, the internal relationship between rockburst and microseismic events is analyzed and the occurrence mechanism of slip-type rockburst in underground cavern of Shuangjiangkou Hydropower Station is analyzed through the change of microseismic monitoring b value to warn the risk level of rockburst. The results show that microseismic events are closely related to excavation activities, and the change of b value reflects the rockburst risk level. The smaller the b value of microseismic events is, the more large magnitude events is and the greater the possibility of rockburst is. The slip-type rockburst was mainly affected by structural plane; it is due to the accumulation of elastic strain energy at the structural plane and the sudden release under the action of unloading or disturbance, driving the block on the structure surface fast slide, mainly manifested as the sliding of large blocks of surrounding rock, and forming a “V” shape pit blasting or cuneate blasting crater, as well as destruction with crackle. Compared with the strained rockburst, the damage is more serious. The research results can provide reference for rockburst prediction and prevention in similar deep rock engineering.

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“…STA/LTA is widely used in seismic data analysis, often as a preliminary step before applying more advanced techniques for event detection and characterization. The U-Net++ follows the advanced idea of deep learning-based end-to-end classification, this paper considers the first-arrival picking of effective microseismic signals as a two classification problem and improves the first-arrival of effective microseismic signals [39]. DPick is an end-to-end approach, and the input is the vertical accelerograms without any preprocessing while the output is the P-wave arrival time [36].…”

Section: Comparison Of Different Algorithms To Pick Up When the Time ...mentioning

confidence: 99%

The CGAS Deep Learning Algorithm for P-Wave Arrival Time Picking of Mining Microseismic Events

Luo,

Xu,

PAn

et al. 2023

IEEE Access

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In light of the inadequacies of traditional P-wave arrival picking algorithms using long and short windows, which exhibit poor anti-noise ability and do not meet the requirements for efficient and accurate P-wave arrival picking, this study proposes a CGAS deep learning algorithm for P-wave arrival picking in mine micro-seismic events. The algorithm constructs the depth characteristics of microseismic waveform through deep learning training and converts the problem of picking up the microseismic waveform into a classification problem by employing data set segmentation and classification. This allows the algorithm to pick up the characteristic information of microseismic waveform and improve the accuracy of P-wave picking. The algorithm was applied to pick up the three-component micro-seismic waveforms obtained from the microseismic monitoring system in a mining area in Liaoning from 2019 to 2020. Through ablation experiments, it was found that adding each structure to the pickup model improved the relevant indexes to some extent. When all network structures were used, the accuracy of the P-wave pickup model reached 98.61%, ensuring the accuracy of P-wave pickup upon arrival and demonstrating the effectiveness of each layer structure in the classification of P-wave arrival. Compared to other P-wave arrival picking algorithms such as STA/LTA, U-Net++, Dpick, PhaseNet and EQTransformer, the algorithm proposed in this paper exhibited high precision in P-wave arrival time picking, providing a new research idea and technical means for efficiently determining the P-wave arrival of events.

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First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

Cited by 9 publications

References 51 publications

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Research on Temporal and Spatial Distribution Characteristics of Microseismic Events of Slip-Type Rockburst

The CGAS Deep Learning Algorithm for P-Wave Arrival Time Picking of Mining Microseismic Events

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