用机器学习区分采矿与天然地震，准确率达95%。
Machine learning approaches to seismic event classification in the Ostrava region
- 用LSTM和XGBoost模型分析地震波形数据
- 二分类F1分数达0.94–0.95，效果优异
- 适合地震监测、矿业安全与地质研究者
捷克东北部是全国最活跃的地震区之一，主要地震多由历史采矿活动引发，但也有天然构造地震。此外，矿区爆破也会被地震台站记录。尽管采矿已停止，矿震仍持续发生。因此快速区分构造性与人为地震仍具重要意义。当前，奥斯特拉瓦-克拉什内波莱的OKC台站自2007年起以100 Hz采样率提供连续波形数据。1992至2002年间，该区域由包含五个台站的弗伦斯蒂特地震台阵（SPF）协同监测，采用触发式STA/LTA系统。本研究基于带有标签的SPF数据集，应用并比较多种机器学习方法。在二分类任务中，长短期记忆网络（LSTM）与XGBoost模型均达到0.94–0.95的F1分数，证明现代机器学习技术在快速地震事件判别中的潜力。
原文摘要 · Abstract (English)
The northeastern region of the Czech Republic is among the most seismically active areas in the country. The most frequent seismic events are mining-induced since there used to be strong mining activity in the past. However, natural tectonic events may also occur. In addition, seismic stations often record explosions in quarries in the region. Despite the cessation of mining activities, mine-induced seismic events still occur. Therefore, a rapid differentiation between tectonic and anthropogenic events is still important. The region is currently monitored by the OKC seismic station in Ostrava-Krásné Pole built in 1983 which is a part of the Czech Regional Seismic Network. The station has been providing digital continuous waveform data at 100 Hz since 2007. In the years 1992--2002, the region was co-monitored by the Seismic Polygon Frenštát (SPF) which consisted of five seismic stations using a triggered STA/LTA system. In this study, we apply and compare machine learning methods to the SPF dataset, which contains labeled records of tectonic and mining-induced events. For binary classification, a Long Short-Term Memory recurrent neural network and XGBoost achieved an F1-score of 0.94 -- 0.95, demonstrating the potential of modern machine learning techniques for rapid event characterization.
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