Predict the magnitudes of seismic events using Bayesian methods

Sánchez, Lissette Jiménez

doi:10.1016/j.soildyn.2019.105914

Cited by 2 publications

(1 citation statement)

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“…Many studies, such as [10][11][12], have been proposed to deal with EQP. Of these, artificial neural networks (ANNs) are the widely used approaches due to their ability to engage in self-learning and handle complex problems.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Dendritic Cell-Negative Selection Method for Earthquake Prediction

Wen

Lan

et al. 2022

Electronics

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Earthquake prediction (EQP) is an extremely difficult task, which has been overcome by adopting various technologies, with no further transformation so far. The negative selection algorithm (NSA) is an artificial intelligence method based on the biological immune system. It is widely used in anomaly detection due to its advantages of requiring little normal data to detect anomalies, including historical seismic-events-based EQP. However, NSA can suffer from the undesirable effect of data drift, resulting in outdated normal patterns learned from data. To tackle this problem, the data changes must be detected and processed, stimulating fast algorithmic adaptation strategies. This study proposes a dendritic cell algorithm (DCA)-based adaptive learning method for drift detection and negative selection algorithm (DC-NSA) that dynamically adapts to new input data. First, this study adopts the Gutenberg–Richter (GR) law and other earthquake distribution laws to preprocess input data. Then, the NSA is employed for EQP, and then, the dendritic cell algorithm (DCA) is employed to detect changes to trigger gradient descent strategies and update the self-set in NSA. Finally, the proposed approach is implemented to predict the earthquakes of MW > 5 in Sichuan and the surroundings during the next month. The experimental results demonstrate that our proposed DC-NSA is superior to the existing state-of-the-art EQP approaches.

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Section: Related Workmentioning

confidence: 99%