A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
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DOI：
10.1609/aaai.v34i01.5376
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发表时间：
2020-02
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通讯作者：
Kevin Fauvel;Daniel Balouek-Thomert;D. Melgar;Pedro Silva;Anthony Simonet;Gabriel Antoniu;Alexandru Costan;Véronique Masson;M. Parashar;I. Rodero;A. Termier
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作者：
Kevin Fauvel;Daniel Balouek-Thomert;D. Melgar;Pedro Silva;Anthony Simonet;Gabriel Antoniu;Alexandru Costan;Véronique Masson;M. Parashar;I. Rodero;A. Termier
我们的研究旨在通过机器学习来提高地震预警系统的准确性。EEW系统的设计目的是在中型和大型地震的破坏性影响达到某个位置之前对其进行检测和表征。传统的基于地震仪的EEW方法由于对地面运动速度的敏感性而不能准确地识别大地震。另一方面，最近引进的高精度GPS台站由于其产生噪声数据的倾向而对识别中等地震无效。此外，GPS台站和地震仪可能会在不同地点大量部署，从而产生大量的数据，影响EEW系统的响应时间和鲁棒性。在实践中，EEW可以被视为机器学习领域的一个典型分类问题：多传感器数据作为输入，地震严重度是分类结果。在本文中，我们介绍了分布式多传感器地震预警（DMSEEW）系统，这是一种新的基于机器学习的方法，它结合了两种类型的传感器（GPS站和地震仪）的数据来检测中、大地震。DMSEEW基于一种新的叠加集成方法，该方法已在地球科学家验证的真实世界数据集上进行了评估。该系统建立在地理上分布式的基础设施上，确保在响应时间和对部分基础设施故障的鲁棒性方面的有效计算。实验结果表明，DMSEEW比传统的地震仪方法和采用相对强度准则的组合传感器（GPS和地震仪）方法具有更高的精度。
Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data consequently, affecting the response time and the robustness of EEW systems.In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.