Advanced Deep Learning for Distinguishing the Quarry Blasts from Induced Seismicity
Description:
The signal characteristics of non-seismic events caused by 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 paper, we propose an advanced 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-sec three-channel waveforms for time-frequency conversion. The proposed discrimination framework comprises a dilated convolutional transformer (DCT) and a capsule neural network. DCT combines the local perception capability of traditional convolutional neural networks, effectively extracting spatial features from multi-channel scalograms. Additionally, the multi-head 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 capsule neural network for hierarchical feature representation. The dynamic routing mechanism in the capsule neural network allows for flexible and adaptive feature propagation and integration between capsules, enabling precise distinction between earthquakes and quarry blasts. We use an artificial intelligence (AI) earthquake dataset recorded by the Texas Seismological Network (TexNet) to demonstrate the classification performance of the proposed network. Compared to state-of-the-art classification networks, the proposed method has higher reliability and more satisfying performance.
Session: From Physics to Forecasts: Advancements and Future Directions of Induced Seismicity Research - I
Type: Oral
Date: 4/15/2025
Presentation Time: 09:00 AM (local time)
Presenting Author: Yangkang
Student Presenter: No
Invited Presentation:
Poster Number:
Authors
Liuqing Yang yangliuqingqin@163.com Uppsala University |
Yangkang Chen Presenting Author Corresponding Author yangkang.chen@beg.utexas.edu University of Texas at Austin |
Daniel Siervo daniel.siervo@beg.utexas.edu University of Texas at Austin |
Katerine Vallejo kate.vallejo@beg.utexas.edu University of Texas at Austin |
Alexandros Savvaidis alexandros.savvaidis@beg.utexas.edu University of Texas at Austin |
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Advanced Deep Learning for Distinguishing the Quarry Blasts from Induced Seismicity
Category
From Physics to Forecasts: Advancements and Future Directions of Induced Seismicity Research