Abstract
In volcano monitoring, effective recognition of seismic events is essential for understanding volcanic activity and raising timely warning alerts. Traditional methods rely on manual analysis, which can be subjective and labor-intensive. Furthermore, current automatic approaches often tackle detection and classification separately, mostly rely on single station information and generally require tailored preprocessing and representations to perform predictions. These limitations often hinder their application to real-time monitoring and utilization across different volcano conditions. This study introduces a novel approach that utilizes Semantic Segmentation models to automate seismic event recognition by applying a straight forward transformation of multi-channel 1D signals into 2D representations, enabling their use as images. Our framework employs a data-driven, end-to-end design that integrates multi-station seismic data with minimal preprocessing, performing both detection and classification simultaneously for five seismic event classes. We evaluated four state-of-the-art segmentation models (UNet, UNet++, DeepLabV3+ and SwinUNet) on approximately 25.000 seismic events recorded at four different Chilean volcanoes: Nevados del Chillán Volcanic Complex, Laguna del Maule, Villarrica and Puyehue-Cordón Caulle. Among these models, the UNet architecture was identified as the most effective model, achieving mean F1 and Intersection over Union (IoU) scores of up to 0.91 and 0.88, respectively, and demonstrating superior noise robustness and model flexibility to unseen volcano datasets.
Abstract (translated)
在火山监测中，有效地识别地震事件对于理解火山活动和及时发布警告警报至关重要。传统方法依赖于手动分析，这种方法可能主观且劳动密集型。此外，当前的自动方法通常分别处理检测和分类，大多依靠单一站点的信息，并且通常需要专门的预处理和表示来进行预测。这些限制常常阻碍它们在实时监测和不同火山条件下应用。本研究介绍了一种新颖的方法，利用语义分割模型通过将多通道1D信号直接转换为2D表示来自动化地震事件识别，使其可以用作图像。我们的框架采用数据驱动、端到端的设计，集成多站点地震数据并进行最少的预处理，同时执行检测和分类任务，涵盖五类地震事件。我们在四个不同的智利火山（Nevados del Chillán火山群、Laguna del Maule、Villarrica 和 Puyehue-Cordón Caulle）记录的大约25,000个地震事件上评估了四种最先进的分割模型（UNet、UNet++、DeepLabV3+和SwinUNet）。在这几种模型中，UNet架构被识别为最有效的模型，在F1分数和交并比（IoU）方面分别达到了最高0.91和0.88的均值，并且展示了对未见火山数据集的强大噪声鲁棒性和模型灵活性。
URL
https://arxiv.org/abs/2410.20595
