Detection of precursors of strong earthquakes is a challenging research area. Recently, it has been shown that strong earthquakes affect electron distribution in the regional ionosphere with indirectly observable changes in the ionospheric delays of GPS signals. Especially, the total electron content (TEC) estimated from GPS data can be used in the seismic precursor detection for strong earthquakes. Although physical mechanisms are not well understood yet, GPS‐based seismic precursors can be observed days prior to the occurrence of the earthquake. In this study, a novel machine learning‐based technique, EQ‐PD, is proposed for detection of earthquake precursors in near real time based on GPS‐TEC data along with daily geomagnetic indices. The proposed EQ‐PD technique utilizes support vector machine (SVM) classifier to decide whether an observed spatiotemporal anomaly is related to an earthquake precursor or not. The data fed to the classifier are composed of spatiotemporal variability map of a region. Performance of the EQ‐PD technique is demonstrated in a case study over a region covering Italy in between the dates of 1 January 2014 and 30 September 2016. The data are partitioned into three nonoverlapping time periods, that are used for training, validation, and test of detecting precursors of earthquakes with magnitudes above 4 in Richter scale. The EQ‐PD technique is able to detect precursors in 17 out of 21 earthquakes while generating 7 false alarms during the validation period of 266 days and 22 out of 24 earthquakes while generating 13 false alarms during the test period of 282 days.

中文翻译：

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基于电离层数据的基于机器学习的地震前兆检测

强震前兆的探测是一个充满挑战的研究领域。近来，已经表明强地震影响了区域电离层中的电子分布，而GPS信号的电离层延迟却可以间接观察到。特别是，根据GPS数据估算的总电子含量（TEC）可用于强震的地震前兆检测。尽管尚未很好地了解物理机制，但是可以在地震发生前几天观测基于GPS的地震前兆。在这项研究中，基于GPS-TEC数据以及每日地磁指数，提出了一种基于机器学习的新型技术EQ-PD，用于近实时地检测地震前兆。提出的EQ-PD技术利用支持向量机（SVM）分类器来确定观察到的时空异常是否与地震前兆有关。馈送到分类器的数据由区域的时空变异图组成。在2014年1月1日至2016年9月30日期间意大利一个地区的案例研究中证明了EQ-PD技术的性能。数据分为三个不重叠的时间段，分别用于训练，验证和验证。里氏震级大于4的地震前兆的测试。