一个基于变压器的实时地震检测框架在异质环境中
Aming Wu1, Irshad Khan1, Young-Woo Kwon2
1School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea.
Scientific reports
|March 12, 2025
概括
本研究介绍了基于变压器的模型TFEQ,用于实时地震检测. TFEQ分析了P和S波,改善了在各种物联网环境中的检测.
科学领域:
- 地震学 地震学
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 与传统方法相比,深度学习方法在地震检测方面表现出更高的效率.
- 在异质环境中部署深度学习,由于数据的变化和各种评估指标,会带来挑战.
- 当前的模型往往优先考虑S波检测,忽视了P波的关键早期识别.
研究的目的:
- 推出TFEQ,一种基于变压器的新型模型,用于实时地震检测.
- 为了能够同时分析P波和S波,以改进地震事件的识别.
- 验证模型在各种物联网 (IoT) 环境中的有效性和适用性.
主要方法:
- 开发了基于变压器的深度学习架构TFEQ.
- 同时分析P波和S波地震数据.
- 使用来自CrowdQuake计划的MEMS传感器数据的案例研究.
主要成果:
- TFEQ展示了有效的实时地震检测能力.
- 该模型同时分析不同数据域的P和S波.
- 案例研究证实了TFEQ在各种数据集中的可靠性和概括性.
结论:
- 在异质物联网环境中,TFEQ为实时地震检测提供了强大的解决方案.
- 同时的P和S波分析提高了地震事件识别的准确性.
- 该模型显示了改善地震研究和防灾准备的巨大潜力.
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