UACNet: A universal automatic classification network for microseismic signals regardless of waveform size and sampling rate.

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. [ABSTRACT FROM AUTHOR]