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 Chill\'an Volcanic Complex, Laguna del Maule, Villarrica and Puyehue-Cord\'on 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.

翻译：在火山监测中，有效识别地震事件对于理解火山活动和及时发布预警至关重要。传统方法依赖人工分析，存在主观性强且耗时费力的问题。此外，当前的自动化方法通常将检测与分类分开处理，大多依赖单台站信息，且通常需要定制化的预处理和特征表示才能进行预测。这些限制往往阻碍了其在不同火山条件下的实时监测与应用。本研究提出一种创新方法，通过将多通道一维信号直接转换为二维图像表示，利用语义分割模型实现地震事件的自动识别。该框架采用数据驱动的端到端设计，以最小预处理整合多台站地震数据，同时对五类地震事件进行检测与分类。我们在智利四座不同火山（内瓦多斯德尔奇连火山群、毛莱湖、比亚里卡火山、普耶韦-科登考列火山群）记录的约25,000个地震事件上，评估了四种先进的分割模型（UNet、UNet++、DeepLabV3+ 和 SwinUNet）。其中UNet架构被证明是最有效的模型，平均F1分数和交并比（IoU）分别达到0.91和0.88，并在未见过的火山数据集上展现出卓越的噪声鲁棒性和模型适应性。