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<p>Earthquake detection is the base of seismological research. Recent advancements have highlighted the superior efficacy of deep learning techniques compared to conventional methods. However, deploying these techniques in highly heterogeneous environments poses significant challenges, primarily due to variations in datasets and the diversity of evaluation methods. Notably, existing models often focus on detecting the more pronounced S-waves, neglecting the crucial early detection of P-waves. To address this, our study introduces TFEQ, a transformer-based model designed for real-time earthquake detection within diverse IoT environments. Uniquely, TFEQ concurrently analyzes both P and S waves across different domains. We further substantiate TFEQ’s effectiveness and its broad applicability through case studies involving MEMS sensor data collected by the CrowdQuake initiative, demonstrating its reliability and generalization capabilities.</p>

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A transformer-based real-time earthquake detection framework in heterogeneous environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月11日

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  • Aming Wu,
  • Irshad Khan,
  • Young-Woo Kwon

摘要

Earthquake detection is the base of seismological research. Recent advancements have highlighted the superior efficacy of deep learning techniques compared to conventional methods. However, deploying these techniques in highly heterogeneous environments poses significant challenges, primarily due to variations in datasets and the diversity of evaluation methods. Notably, existing models often focus on detecting the more pronounced S-waves, neglecting the crucial early detection of P-waves. To address this, our study introduces TFEQ, a transformer-based model designed for real-time earthquake detection within diverse IoT environments. Uniquely, TFEQ concurrently analyzes both P and S waves across different domains. We further substantiate TFEQ’s effectiveness and its broad applicability through case studies involving MEMS sensor data collected by the CrowdQuake initiative, demonstrating its reliability and generalization capabilities.