标题： HEIMDALL：一种基于图的微震检测和定位器 显示英文标题

标题： HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity

摘要： 在本工作中，我们提出了一种新的深度学习模型，用于微震监测，该模型利用地震台站记录之间的连续时空关系，形成用于地震目录创建的端到端流程。 它采用图论和最先进的图神经网络架构，在滚动窗口上同时执行相位拾取、关联和事件定位，使其适用于播放和近实时监测。 作为在全球范围内减少碳排放的总体战略的一部分，在向绿色能源转型的更大背景下，人们对开发增强型地热系统越来越感兴趣。 在冰岛亨吉尔地区的复杂地热区域进行了测试，使用了临时实验的公开数据，我们的模型使用手动修订和自动地震目录进行了训练和验证。 结果表明，与之前发布的自动系统和参考目录相比，事件检测显著增加，包括2018年12月的$4 M_w$地震序列和2019年2月的一天地震序列。 我们的方法减少了虚假事件，最小化了人工监督，并减少了对管道进行大量调整或对深度学习模型进行迁移学习的需求。 总体而言，它验证了一个稳健的地热地震区域监测工具，补充了现有系统，并增强了地热能开发过程中的运营风险缓解能力。 显示英文摘要

摘要： In this work, we present a new deep-learning model for microseismicity monitoring that utilizes continuous spatiotemporal relationships between seismic station recordings, forming an end-to-end pipeline for seismic catalog creation. It employs graph theory and state-of-the-art graph neural network architectures to perform phase picking, association, and event location simultaneously over rolling windows, making it suitable for both playback and near-real-time monitoring. As part of the global strategy to reduce carbon emissions within the broader context of a green-energy transition, there has been growing interest in exploiting enhanced geothermal systems. Tested in the complex geothermal area of Iceland's Hengill region using open-access data from a temporary experiment, our model was trained and validated using both manually revised and automatic seismic catalogs. Results showed a significant increase in event detection compared to previously published automatic systems and reference catalogs, including a $4 M_w$ seismic sequence in December 2018 and a single-day sequence in February 2019. Our method reduces false events, minimizes manual oversight, and decreases the need for extensive tuning of pipelines or transfer learning of deep-learning models. Overall, it validates a robust monitoring tool for geothermal seismic regions, complementing existing systems and enhancing operational risk mitigation during geothermal energy exploitation.