A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks

Zhao, Yong; Xu, Hao; Yang, Tianhong; Wang, Shuhong; Sun, Dongdong

doi:10.1080/19475705.2021.1968043

Cited by 16 publications

(4 citation statements)

References 40 publications

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“…Hybrid methods [43] Combines multiple methods, e.g., integrating machine learning with deep learning, or combining time-frequency analysis with statistical feature analysis.…”

Section: Can Identify Unknown Categoriesmentioning

confidence: 99%

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

Shu,

Dawod

2023

Applied Sciences

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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 identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.

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“…Hybrid methods [43] Combines multiple methods, e.g., integrating machine learning with deep learning, or combining time-frequency analysis with statistical feature analysis.…”

Section: Can Identify Unknown Categoriesmentioning

confidence: 99%

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

Shu,

Dawod

2023

Applied Sciences

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“…Meanwhile, Tang et al [19] presented an innovative network architecture called ResSCA, which combined a new deep spatial and channel attention (DSCA) module with improved residual connections and CNN. Zhao et al [20] established a hybrid model fusing singular spectrum analysis (SSA), CNN, and long short-term memory (LSTM). Saad et al [21] developed a fully automatic real-time amplitude estimation system based on the Vision Transformer network.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

Peng,

Lei,

Wang

2023

Sustainability

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As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.

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“…It has found application in many different domains. Zhao et al (2021) developed a hybrid model by combining a long short-term memory network (LSTM), a convolutional neural network (CNN), and a singular spectrum analysis (SSA). Among these, the LSTM network effectively extracted the time features, and the hybrid model could accurately extract data features of monitoring signals and further improve the recognition performance of mass spectral signals.…”

Section: Introductionmentioning

confidence: 99%

Digital construction of geophysical well logging curves using the LSTM deep-learning network

Gao

2023

Front. Earth Sci.

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A complete well logging suite is needed frequently, but it is either unavailable or has missing parts. The mudstone section is prone to wellbore collapse, which often causes distortion in well logs. In many cases, well logging curves are never measured, yet are needed for petrophysical or other analyses. Re-logging is expensive and difficult to achieve, while manual construction of the missing well logging curves is costly and low in accuracy. The rapid technical evolution of deep-learning algorithms makes it possible to realize the digital construction of missing well logging curves with high precision in an automated fashion. In this article, a workflow is proposed for the digital construction of well logging curves based on the long short-term memory (LSTM) network. The LSTM network is chosen because it has the advantage of avoiding the vanishing gradient problem that exists in traditional recurrent neural networks (RNNs). Additionally, it can process sequential data. When it is used in the construction of missing well logging curves, it not only considers the relationship between each logging curve but also the influence of the data from a previous depth on data at the following depth. This influence is validated by exercises constructing acoustic, neutron porosity, and resistivity logging curves using the LSTM network, which effectively achieves high-precision construction of these missing curves. These exercises show that the LSTM network is highly superior to the RNN in the digital construction of well logging curves, in terms of accuracy, efficiency, and reliability.

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A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks

Cited by 16 publications

References 40 publications

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

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

Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

Digital construction of geophysical well logging curves using the LSTM deep-learning network

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