2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events
Turarbek A. Adetbekov Y. Bektemesov M.
2023Science and Information Organization
International Journal of Advanced Computer Science and Applications
2023#14Issue 1788 - 796 pp.
Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields. When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support. In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline. In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system. The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods. All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022. The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively. Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting real-world earthquakes in seismic zones.
classification , deep learning , Earthquake , machine learning , prediction
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Information Systems Department, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
Information Systems Department
Abai Kazakh National Pedagogical University
10 лет помогаем публиковать статьи Международный издатель
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