Machine learning in microseismic monitoring

Anikiev, Denis; Birnie, Claire; Waheed, Umair bin; Alkhalifah, Tariq; Gu, Chen; Verschuur, D.J.; Eisner, Leo

doi:10.1016/j.earscirev.2023.104371

Cited by 21 publications

(8 citation statements)

References 168 publications

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“…Compared with the SVD method, the FSVD method retained the effective signals better in both the high-Figure 10. Analysis of noise removal effect of FSVD under different SNR: (a) Results of high SNR signal before and after denoising(channels 5#): 1 The original signal before denoising, 2 the signal after FSVD denoising, 3 is the amplitude distribution of the original signal in the time-frequency domain, the larger the amplitude, the darker the color. 4 is the amplitude distribution of the signal after denoising in the time-frequency domain.…”

Section: Comparison and Analysis Of The Effect Of Noise Reduction By ...mentioning

confidence: 99%

“…It is often used to monitor and evaluate the final fracturing effect in oil and gas, mining, and other fields [1,2]. However, the amount of microseismic monitoring data is huge, and there is complex background noise interference, which is inconvenient for later data processing and analysis [3,4]. Therefore, in microseismic monitoring technology, noise reduction and the filtering of microseismic mine signals are important prerequisites for rapid and accurate analysis.…”

Section: Introductionmentioning

confidence: 99%

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The Single-Channel Microseismic Mine Signal Denoising Method and Application Based on Frequency Domain Singular Value Decomposition (FSVD)

Zhu

Sui

Li³

et al. 2023

Sustainability

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The purpose of denoising microseismic mine signals (MMS) is to extract relevant signals from background interference, enabling their utilization in wave classification, identification, time analysis, location calculations, and detailed mining feature analysis, among other applications. To enhance the signal-to-noise ratio (SNR) of single-channel MMS, a frequency-domain denoising method based on the Fourier transform, inverse transform, and singular value decomposition was proposed, along with its processing workflow. The establishment of key parameters, such as time delay, τ, reconstruction order, k, Hankel matrix length, n, and dimension, m, were introduced. The reconstruction order for SVD was determined by introducing the energy difference spectrum, E, and the denoised two-dimensional microseismic time series was obtained based on the SVD recovery principle. Through the analysis and processing of three types of typical microseismic waveforms in mining (blast, rock burst, and background noise) and with the evaluation of four indicators, SNR, ESN, RMSE, and STI, the results show that the SNR is improved by more than 10 dB after FSVD processing, indicating a strong noise suppression capability. This method is of significant importance for the rapid analysis and processing of microseismic signals in mining, as well as subsequently and accurately picking the initial arrival times and the exploration and analysis of microseismic signal characteristics in mines.

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Section: Comparison and Analysis Of The Effect Of Noise Reduction By ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Single-Channel Microseismic Mine Signal Denoising Method and Application Based on Frequency Domain Singular Value Decomposition (FSVD)

Zhu

Sui

Li³

et al. 2023

Sustainability

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show abstract

“…The detection and characterization of microseismic events using machine learning have become a topic of increasing interest, as evidenced in the recent review of Anikiev et al. (2023). In Waheed et al.…”

Section: Introductionmentioning

confidence: 99%

“…This study considers one such problem, that of the detection of induced seismicity in the Groningen Gas Field, located in the province of the same name in the Netherlands. The detection and characterization of microseismic events using machine learning have become a topic of increasing interest, as evidenced in the recent review of Anikiev et al (2023). In Waheed et al (2020), a simple LR model -essentially, a minimally shallow neural network without a hidden layer -with five trainable parameters was used for low-magnitude earthquake detection in data from this area, on the grounds that the interpretability of such a model would highly advantageous.…”

Section: Introductionmentioning

confidence: 99%

A comparison of deep and shallow models for the detection of induced seismicity

Goel

Gorse

2023

Geophysical Prospecting

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Can an interpretable logistic regression model perform comparably to a deep learning model in the task of earthquake detection? In spite of the recent focus in academic seismological research on deep learning, we find there is hope that it can. Using data from the Groningen Gas Field in the Netherlands, relating to low‐magnitude induced seismicity, we build on a recently presented four‐input logistic regression model by adding to it four further statistically derived features. We evaluate the performance of our feature‐enhanced model relative to both the original logistic regression model (shallow machine learning model) and a deep learning model proposed by the same research group. We discover that at the signal‐to‐noise ratio of this earlier work, our enhanced logistic regression model in fact overall outperforms the deep learning model and displays no false negative errors. At the lower signal‐to‐noise ratios also considered here, while the number of false positive errors made by the logistic regression model increases, the number of undetected earthquakes remains zero. Though the number of false positives is for the highest imbalance ratios currently prohibitive, the benefit of our four additional features, which increases as the signal‐to‐noise ratio decreases, suggests that an interpretable model might be made to perform comparably to a more complex deep learning model at real‐world class imbalance ratios if further useful inputs could be identified.

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“…The recent advances in deep learning have shown promising results in detecting impulsive sounds. In particular, deep convolutional neural networks (CNNs) have shown remarkable performance in sound classification tasks [3][4][5]. The use of recurrent neural networks (RNNs) has also been shown to be effective in modeling sequential data such as audio signals [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Смайлов¹,

Dosbayev²,

Omarov³

et al. 2023

IJACSA

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In this research paper, we presented a novel approach to detect impulsive sounds in real-time using a combination of Deep CNN and RNN architectures. The proposed approach was evaluated using our collected dataset of impulsive sounds, and the results showed that it outperformed traditional audio signal processing methods in terms of accuracy and F1score. The proposed approach has several advantages over traditional methods, including the ability to handle complex audio patterns, detect impulsive sounds in real-time, and improve its performance with a large dataset of labeled impulsive sounds. However, there are some limitations to the proposed approach, including the requirement for a large amount of labeled data to train effectively, environmental factors that may impact the accuracy of the detection, and high computational requirements. Overall, the proposed approach demonstrates the effectiveness of using a combination of Deep CNN and RNN architectures for impulsive sound detection, with potential applications in various fields such as public safety, industrial settings, and home security systems. The proposed approach is a significant step towards developing automated systems for detecting dangerous events and improving public safety.

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Machine learning in microseismic monitoring

Cited by 21 publications

References 168 publications

The Single-Channel Microseismic Mine Signal Denoising Method and Application Based on Frequency Domain Singular Value Decomposition (FSVD)

The Single-Channel Microseismic Mine Signal Denoising Method and Application Based on Frequency Domain Singular Value Decomposition (FSVD)

A comparison of deep and shallow models for the detection of induced seismicity

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

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