Identifying the source types of the seismic events using discriminant functions and tree-based machine learning algorithms at Soma Region, Turkey

Yavuz E., Iban M. C., ARPAZ E.

Environmental Earth Sciences, cilt.82, sa.11, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 82 Sayı: 11
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s12665-023-10946-8
  • Dergi Adı: Environmental Earth Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Amplitude ratio, Complexity, Low-magnitude quakes, Machine learning algorithms, Quarry blast, Statistical approaches
  • Kocaeli Üniversitesi Adresli: Evet

Özet

In Soma Region, located in the western part of Turkey, both tectonic and anthropogenic source type of events are identified in earthquake catalogues by various seismology centers. Especially because of the low-magnitude quarry blasts, mistakes can be made in seismic catalogs in terms of source type, which can be further complicated by similar magnitude earthquakes. With this regard, the vertical component seismograms of 445 seismic events with a magnitude of Ml ≤ 2.5 recorded at the station SOMA, operated by Boğaziçi University Kandilli Observatory and Earthquake Research Institute Regional Earthquake–Tsunami Monitoring Center (KOERI–RETMC), were analyzed. First, two statistical analyses (Linear and Quadratic Discriminant Functions–LDF/QDF) were applied for amplitude ratio and complexity methods for 345 waveforms that have the same source types in both KOERI–RETMC catalogs and the first manual determination. The accuracies of the statistical approaches are varied between 87.25% and 97.39% and the better statistical classifier is the QDF for complexity method. Then, using the values obtained from two methods together, tree-based machine learning (ML) classifiers called as Random Forest (RF), Gradient Boosting (GB) and Support Vector Machine (SVM) were applied to the same data set. All classifiers provided as 100% success rate for quarry blasts’ recordings, while earthquakes are categorized by RF, GB and SVM with 97.1%, 95.8% and 92.8%, respectively. Each ML algorithms were applied to the other 100 data identified as quarry blast on KOERI–RETMC catalogs but determined to be suspicious source types on first manual determination. Regarding to the outperforming RF and GB algorithms, the quarry blast recordings have just been reached as 53 and 55, respectively. Considering the accuracies of the ML algorithms in the testing and training data set, the source types of the low magnitude seismic events that are registered in the catalogs should be re-evaluated and refined in Soma Region using the station SOMA.