Employing convolution-enhanced attention mechanisms for earthquake detection and phase picking models - Wang, Shuwang; Liu, Feng; Yin, Xin-xin; Chen, Kerui; Cai, Run (Frontiers in Earth Science, 2023)
In response to the challenge of improving the performance of deep learning models for earthquake detection in low signal-to-noise ratio environments, this article introduces a new earthquake detection model called ECPickNet. Drawing inspiration from the EQTransformer, this model leverages Convolution-Enhanced Transformer technology, Conformer architecture, and incorporates the Residual Stacking Block Unit with Channel-Skipping (RSBU-CS) module. The manuscript provides a detailed overview of the model’s network architecture, parameter settings used during the training process, and compares it with several similar methods through a series of experiments. The experimental results highlight ECPickNet’s well performance on both the STEAD and Gansu datasets, particularly performing exceptionally well in the processing of low signal-to-noise ratio data. Interested readers can access and download the proposed method from the following website address: https://github.com/20041170036/EcPick.