Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with Machine Learning Algorithms

ABSTRACT

In this study, three new regression models are created for magnitude-type conversion with different machine learning algorithms (linear regression, regression trees, support vector machines, Gaussian process regression models, ensembles of trees) by using the earthquakes (M ≥ 4.0) that occurred in Turkey (1900–2020). Additionally, eight new equations are formed with linear and orthogonal regression methods. Developed equations and models are compared to equations selected from the literature by test data. As a result of the study, it is observed that machine learning algorithms create better models and provide results closer to the real values than created and selected equations.

KEYWORDS:

• Machine learning algorithms
• regression trees
• support vector machines
• magnitude
• Regression

Acknowledgements

The authors special thank the editors and anonymous reviewers to improve the manuscript with their constructive comments. The study includes a part of the PhD thesis of Kaan Hakan COBAN at the Graduate Institute of Natural and Applied Sciences in Karadeniz Technical University. Earthquake data were provided by the Bogazici University Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC), Republic of Turkey Prime Ministry Disaster & Emergency Management Authority Presidential of Earthquake Department (AFAD), and Harvard Global Centroid-Moment-Tensor project. Faults were digitized by Geoscience Map Viewer of the General Directorate of Mineral Research and Exploration (Emre, Duman, and Özalp et al. 2018; Emre et al. 2013).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Code availability statement

The authors confirm that the codes supporting the findings of this study are available within the article and/or its supplementary materials.