Employing convolution-enhanced attention mechanisms for earthquake detection and phase picking models

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 Convolutio...

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Bibliographic Details
Published inFrontiers in earth science (Lausanne) Vol. 11
Main Authors Wang, Shuwang, Liu, Feng, Yin, Xin-xin, Chen, Kerui, Cai, Run
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 02.11.2023
Subjects
attention
conformer
deep learning
earthquake detection
RSBU-CS
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Summary: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 .
ISSN:2296-6463
2296-6463
DOI:10.3389/feart.2023.1283857