Intelligent identification method and application of seismic faults based on a balanced classification network

Yang, Jing; Ding, Renwei; Wang, Huiyong; Lin, Niantian; Zhao, Lihong; Zhao, Shuo; Zhang, Yujie

doi:10.1007/s11770-022-0976-9

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…In situations where the data distribution is imbalanced, evaluating the model's recognition performance requires a confusion matrix to be established and the calculation of precision, recall, and F1 score [41][42][43][44][45]. Unlike traditional accuracy, these metrics offer a more comprehensive and objective assessment of the classifier's performance.…”

Section: Evaluation Methods For Unbalanced Distribution Of Signal Sam...mentioning

confidence: 99%

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning

Sun,

Lin,

Yao

et al. 2023

Sensors

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The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.

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Section: Evaluation Methods For Unbalanced Distribution Of Signal Sam...mentioning

confidence: 99%

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning

Sun,

Lin,

Yao

et al. 2023

Sensors

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“…The emergence of artificial intelligence has brought fault recognition to a climax. Using deep learning [10][11][12][13][14] for fault recognition overcomes the limitations of traditional recognition methods, efficiently finds the mapping relationship between the input data and target output, and dynamically learns features during the training process. Semantic segmentation has achieved superior application results in the fields of autonomous driving [15][16][17], medical image segmentation [18][19][20][21], and iris recognition [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure

et al. 2022

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Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults.

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MultiURNet for 3D seismic fault attributes fusion detection combined with PCA

Yang,

Lu,

Tao

et al. 2024

Journal of Applied Geophysics

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Intelligent identification method and application of seismic faults based on a balanced classification network

Cited by 4 publications

References 22 publications

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning

An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure

MultiURNet for 3D seismic fault attributes fusion detection combined with PCA

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