Earthquake

Earthquake prediction using artificial intelligence in the Ferghana depression (Uzbekistan)

Ikram Atabekov

Institute of Seismology, Zulfiyakhanum Street 3, 100128 Tashkent, Uzbekistan

DOI: https://doi.org/10.59429/ear.v2i1.1879

Keywords: geodynamical modeling; stress; earthquake; machine learning; deep learning

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Abstract

Earthquake prediction remains a formidable challenge due to its theoretical and practical complexities. The multifactorial nature of earthquakes leads to diverse anomalies, which are potential precursors. However, the intricate earthquake process and limited knowledge of the Earth's crust structure restrict the accuracy of these predictions. This study introduces an advancements using machine learning and deep learning methods, notably the Kora 3 and Kora 4 algorithms, to identify key earthquake features. We employed LSTM and RNN deep learning algorithms to predict earthquakes of magnitude M≤4.3 without temporal data. Our methodology was applied to the 2022 earthquake monitoring in the Fergana depression, demonstrating significant advancements in seismic event prediction.

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