← 研踪 PaperTrailarXiv:2509.22574 · v2
Machine learning approaches to seismic event classification in the Ostrava region
首次提交 2025-09-27 00:51
版本更新 2026-11-09 03:50
雷达观测 2026-11-10 13:19
摘要
The north-eastern part of the Czech Republic, including the Czech part of the Upper Silesian Coal Basin, is among the most seismically active areas of the country. The seismic stations in the region record mining-induced events, which continue after the cessation of mining, natural earthquakes, which are rare but may originate in the same area, and quarry blasts. A rapid and reliable distinction between the three types is required for the routine evaluation of the continuously recorded data. We study their automatic discrimination with neural networks from short three-component windows starting one second before the P-wave onset. The main data set is the archive of the Seismic Polygon Frenštát (1992--2002), a triggered network whose records were recovered from the original format, cleaned with deterministic and learned quality checks and completed with automatically picked P-wave arrivals; it contains about 40,000 records of all three event types. Among five compared architectures, convolutional networks operating on short-time spectrograms outperform recurrent networks operating on magnitude spectra or on the raw waveform. The best model, EfficientNet-B0 applied to 24-s windows, reaches a macro-averaged F1 score of 0.96, and blasts and natural earthquakes are practically never confused with each other; class weighting and undersampling, the usual remedies for the strong class imbalance, lower the score. Finally, we examine the transfer to the station OKC, which monitors the region today with different instruments. A model trained on the archive alone fails on the modern records, whereas a single model trained on both data sets reaches a macro-F1 score of 0.96 on the archive and 0.92 on the modern station, where the accuracy is limited by the small number of catalogued natural earthquakes rather than by the model.
作者备注：v2: 29 pages, 3 figures, 13 tables; improved data cleaning and extended analysis to three-class classification
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v2 · 2026-11-09 03:50 · 最新观测版本
Machine learning approaches to seismic event classification in the Ostrava region
The north-eastern part of the Czech Republic, including the Czech part of the Upper Silesian Coal Basin, is among the most seismically active areas of the country. The seismic stations in the region record mining-induced events, which continue after the cessation of mining, natural earthquakes, which are rare but may originate in the same area, and quarry blasts. A rapid and reliable distinction between the three types is required for the routine evaluation of the continuously recorded data. We study their automatic discrimination with neural networks from short three-component windows starting one second before the P-wave onset. The main data set is the archive of the Seismic Polygon Frenštát (1992--2002), a triggered network whose records were recovered from the original format, cleaned with deterministic and learned quality checks and completed with automatically picked P-wave arrivals; it contains about 40,000 records of all three event types. Among five compared architectures, convolutional networks operating on short-time spectrograms outperform recurrent networks operating on magnitude spectra or on the raw waveform. The best model, EfficientNet-B0 applied to 24-s windows, reaches a macro-averaged F1 score of 0.96, and blasts and natural earthquakes are practically never confused with each other; class weighting and undersampling, the usual remedies for the strong class imbalance, lower the score. Finally, we examine the transfer to the station OKC, which monitors the region today with different instruments. A model trained on the archive alone fails on the modern records, whereas a single model trained on both data sets reaches a macro-F1 score of 0.96 on the archive and 0.92 on the modern station, where the accuracy is limited by the small number of catalogued natural earthquakes rather than by the model.
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