Predicting the Unpredictable: Advancements in Earthquake Forecasting Using Artificial Intelligence and LSTM Networks

IF 0.7 4区 地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS Geomagnetism and Aeronomy Pub Date : 2024-10-27 DOI:10.1134/S0016793224600693
Sevim Bilici, Fatih Külahcı, Ahmet Bilici
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Abstract

Earthquakes, representing some of the most devastating natural phenomena, pose a persistent challenge in prediction due to their unpredictable nature. Despite significant insights offered by traditional methods, their lack of precision often leaves communities at risk. This research explores a cutting-edge approach in earthquake prediction using artificial intelligence (AI), with a particular emphasis on the attention encoder–decoder long short-term memory (LSTM) model. Focused on modeling seismic events of varied scales that occurred from 2007 to 2010 in the North Anatolian Fault Zone (NAFZ), Turkey, this study compares the efficacy of various AI models including multi-layer perceptron (MLP) and LSTM. The research reveals that the attention encoder–decoder LSTM model surpasses its counterparts in performance. It demonstrates a remarkable capability in forecasting earthquakes by effectively deciphering complex patterns within the data, underscoring its viability as a powerful tool in seismic prediction. The attention encoder–decoder LSTM model, utilizing AI’s latest advancements, offers a nuanced approach by selectively concentrating on relevant data segments, a method particularly beneficial in analyzing complex seismic patterns. This study endeavors to advance the field of earthquake prediction, proposing a model that combines sophisticated AI techniques with in-depth seismic data analysis for more accurate forecasting.

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预测不可预测:利用人工智能和 LSTM 网络进行地震预测的进展
地震是最具破坏性的自然现象之一,由于其不可预知的特性,给预测工作带来了长期的挑战。尽管传统方法提供了重要的洞察力,但其缺乏精确性往往使社区处于危险之中。这项研究探索了一种利用人工智能(AI)进行地震预测的前沿方法,尤其侧重于注意力编码器-解码器长短期记忆(LSTM)模型。本研究以 2007 年至 2010 年在土耳其北安纳托利亚断裂带(NAFZ)发生的不同规模的地震事件建模为重点,比较了包括多层感知器(MLP)和 LSTM 在内的各种人工智能模型的功效。研究结果表明,注意力编码器-解码器 LSTM 模型的性能超越了同类模型。该模型能有效破译数据中的复杂模式,在地震预报方面表现出卓越的能力,凸显了其作为地震预报强大工具的可行性。注意力编码器-解码器 LSTM 模型利用人工智能的最新进展,通过选择性地集中于相关数据片段,提供了一种细致入微的方法,这种方法尤其有利于分析复杂的地震模式。这项研究致力于推动地震预测领域的发展,提出了一种将复杂的人工智能技术与深入的地震数据分析相结合的模型,以实现更准确的预测。
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来源期刊
Geomagnetism and Aeronomy
Geomagnetism and Aeronomy Earth and Planetary Sciences-Space and Planetary Science
CiteScore
1.30
自引率
33.30%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Geomagnetism and Aeronomy is a bimonthly periodical that covers the fields of interplanetary space; geoeffective solar events; the magnetosphere; the ionosphere; the upper and middle atmosphere; the action of solar variability and activity on atmospheric parameters and climate; the main magnetic field and its secular variations, excursion, and inversion; and other related topics.
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