点击次数：

影响因子：8.0
DOI码：10.1016/j.engappai.2023.107088
发表刊物：Engineering Applications of Artificial Intelligence
关键字：Deep learning; Microseismic signal; Waveform size; Sampling rate; Universal automatic classification
摘要：In microseismic monitoring, various types of vibration events are often collected. Realizing the automatic identification of microseismic events in many suspected events is the basis of monitoring timeliness. However, due to the different sampling methods of microseismic data provided by different products, the data often contains different waveform sizes and sampling frequencies. This makes it difficult for existing approaches to be widely used in different projects without data preprocessing. In this paper, we propose the Universal Automatic Classification Network (UACNet), a deep learning approach that automatically identifies microseismic data in engineering without preprocessing. The UACNet model includes multiple convolution layers, adaptive average pooling layers, fully connected layers, and UAC blocks. UAC block is a residual structure with multiple convolutional layers and reset and update gates. The adaptive average pooling layer unifies the input size, and the UAC block functions as a feature extraction network to mine sufficient features from data. We test the proposed UACNet on engineering data and compare it with existing common and advanced methods. As a result, UACNet passed the ablation study, and the classification accuracy of UACNet is 95.62%, which is higher than 89.14% of CNN, 91.24% of ResNet, 91.04% of CapsNet, and 86.16% of RTFN, respectively. Moreover, the influence of waveform size, sampling rate, signal-to-noise ratios, and amplitude on the accuracy of UACNet is analyzed. The results show that UACNet can overcome the influence of these factors and truly realize automatic real-time classification of microseismic signals without preprocessing.
论文类型：期刊论文
学科门类：工学
一级学科：矿业工程
文献类型：J
卷号：126
页面范围：107088
字数：6000
是否译文：否
发表时间：2023-09-04
收录刊物：SCI