Predicting Earthquakes in The Eastern Anatolian Region Using Machine Learning Algorithms

This study investigates whether the 7.8-magnitude earthquake that occurred on February 6, 2023, in Kahramanmaraş, Türkiye, could have been predicted using advanced machine learning techniques. Additionally, it aims to assess the likelihood of similar devastating earthquakes occurring in this region in the future. Addressing these questions serves as the primary motivation for this research, with the goal of improving our understanding of seismic hazards and enhancing predictive capabilities for better disaster preparedness. By combining existing research findings with innovative predictive features, the study developed a meticulously crafted feature matrix to evaluate the capability of machine learning algorithms in forecasting such high-magnitude seismic events. Accurate earthquake prediction is crucial for developing early warning systems, disaster planning, and seismic risk assessments. The analysis utilized instrumental records of 36933 earthquakes (Md≥1) that occurred within a circular area of a 100-km radius, centered at 37.288⁰ latitude and 37.043⁰ longitude, spanning the period from August 30, 1908, to September 30, 2024. The data were obtained from Boğaziçi University, Kandilli Observatory and Earthquake Research Institute, Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC). The compiled catalogue includes various magnitude scales (Ms: surface wave magnitude, Md: duration magnitude, MLM_LML​: local magnitude, Mb​: body wave magnitude, and Mw​: moment magnitude), along with origin time, epicenter, and depth information.

 

To create a homogeneous catalogue, a conversion equation between moment magnitude (Mw and other scales (Md, ML, Mb, Ms, M) was determined using the general orthogonal regression method. Depth parameters were analyzed to exclude artificial events, and the final magnitude range was between 1 and 7.8, with depths ranging from 1 to 40 km. The most reliable conversion equation was obtained for ML and Mw as 1,887 events had both ML and Mw magnitudes. The derived conversion equation is: is Mw*=1.00005*ML+(-0.06440), R² =0.97473.

Machine learning models-including Linear Regression, Support Vector Machines, Naïve Bayes, and Random Forest-were applied to both the uniform catalogue and inhomogeneous catalogue. The results revealed a significant difference in earthquake patterns for events with magnitudes less than 6 before and after modeling. These findings indicate that in regions with high seismic activity, modeling efforts can provide more reliable insights into the spatial distribution and magnitude of seismicity. Among the machine learning algorithms tested, the Random Forest model demonstrated the best performance, achieving the highest accuracy in predicting the maximum earthquake magnitude category within a 30-day timeframe. While predicting extreme-magnitude seismic events remains a significant challenge, the findings highlight the potential of data-driven approaches to enhance seismic risk management and preparedness. The methodology developed in this study offers valuable insights and practical applications for Turkey's Eastern Anatolian Fault and other seismically active regions.