QBTransNet: Deep Learning Earthquake Distinction from Quarry Blasts Using Dilated Convolutional Transformer

Abstract The accurate distinction between earthquakes and non-earthquake events such as quarry blasts is crucial for the subsequent data analysis. However, signal characteristics of quarry blasts are similar to earthquake events, leading to unreliable and potentially erroneous manual identification, especially in the absence of source location information. In this article, we propose a reliable deep-learning-based framework to distinguish between earthquakes and quarry blasts. In the data preprocessing stage, we apply the continuous wavelet transform algorithm to the 60 s three-channel waveforms for time–frequency conversion. The proposed discrimination framework comprises a dilated convolutional transformer (DCT) and a capsule neural network (CapsNet). DCT combines the local perception capability of traditional convolutional neural networks, effectively extracting spatial features from multichannel scalograms. In addition, the multihead self-attention module in the transformer dynamically adjusts feature weights across different positions to adaptively focus on significant features, which is crucial for handling complex background noise and irrelevant information in earthquake and quarry blast signals. Then, the features extracted by DCT are transferred to the CapsNet for hierarchical feature representation. The dynamic routing mechanism in the CapsNet allows for flexible and adaptive feature propagation and integration between capsules, enabling precise distinction between earthquakes and quarry blasts. We use an artificial intelligence earthquake dataset recorded by the Texas Seismological Network to demonstrate the classification performance of the proposed network. Compared to state-of-the-art classification networks, the proposed method has higher reliability and satisfactory results, with the testing accuracy, precision, recall, and F1-score reaching 99.22%, 99.34%, 99.01%, and 99.18%, respectively. We also demonstrate the robustness of the proposed network through a real-time monitoring test.