Citation :

Sivakumar Paramasivam , 2025. "Machine Learning in Seismology for Earthquake Prediction" ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 1, Issue 1: 16-26.

Abstract :

Geophysicists have long found it challenging to accurately predict earthquakes because seismic processes are chaotic and don't follow a straight line. Even while traditional seismology methods have improved a lot, scientists still can't properly predict when, when, and how big earthquakes will be. Stress accumulation modeling, plate tectonics, and probabilistic forecasting are some of these methodologies. As seismic data and computer resources grow swiftly, machine learning (ML) has become a promising tool to make predictive systems and early-warning systems better. This study focuses at how machine learning can be applied in seismology, especially to predict earthquakes, find strange things, and look at antecedents. Machine learning methods including supervised learning, unsupervised learning, and deep learning are being utilized more and more to study seismic waveforms, ancient earthquake catalogs, ground deformation, and other geophysical data. Regular statistical models can't discover small and complex patterns in data, but these models can. This study uses real data from the Southern California Seismic Network (SCSN) to see how well Random Forests, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs) can predict earthquakes. We checked the models' accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC).The results reveal that CNN models are quite good at finding signals before an earthquake in raw waveform data. The AUC is 0.93 and the F1-score is 89.1%. LSTM models did a remarkable job of capturing temporal dependency, especially when it comes to medium-term estimates. Random Forests were less advanced, but they were easy to grasp and operated well with very little computational resources. But all of the models had certain issues, such producing false positives when things were calm and not being able to be used in places like Japan and Italy. The study also highlights how difficult it is to employ machine learning in the study of earthquakes. Some of these include that big earthquakes don't happen very often, which makes datasets unbalanced; that black-box models like deep neural networks are hard to grasp; and that it's challenging to connect data-driven methods with existing physical models of seismicity. There are also ethical concerns about false alarms and what machine-made predictions might do to society.This research says that machine learning isn't a completely reliable technique to predict earthquakes on its own, but it is a highly helpful tool to employ with traditional seismology. ML models are a huge step forward in lowering the danger of earthquakes because they can discover patterns that aren't obvious, make real-time monitoring better, and make predictions more accurate. In the future, we should expect to see new advancements that use both geoscience expertise and machine learning. In the near future, combining physical modeling with data science could make earthquake prediction systems that are better, easier to understand, and easier to use.

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Keywords :

Seismic Waveform Analysis; Earthquake Prediction; Machine Learning; Seismology; Convolutional Neural Networks (CNN); Long Short-Term Memory (LSTM); Earthquake Forecasting; Early Warning Systems; Deep Learning in Geophysics; Data-Driven Seismic Models.