The Research on Method of Prediction Mine Earthquake Based on the Information Entropy Principle

Jia, Binghao; Zhou, Linli; Pan, Yishan; Yang, Chengfang; Li, Zhiyong

doi:10.1007/978-3-319-99972-2_24

Cited by 1 publication

(1 citation statement)

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“…In 2018, DeVries et al [7] introduced a deep learning scheme to discern criteria for predicting the location of aftershocks based on static stress. Jia et al [8] adopted Information Entropy Principle (IEP) to predict mine earthquake. However, due to the insufficient mediumlarge seismic data, these methods have poor performance, for example, the neural network and deep learning approaches require more training sets, otherwise, the prediction results will be unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Earthquake Prediction Model Based on Danger Theory in Artificial Immunity

Zhou¹,

Liang²,

Ming³

et al. 2020

NNW

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Earthquake prediction is an extraordinarily stochastic process. Determining the occurrence time, location of epicenter and magnitude of a coming earthquake in the following month is an extremely difficult task. Nowadays, some geophysical, statistical and machine learning methods are adopted to predict earthquakes, however, for the insufficient medium-large seismic data, their results are not satisfactory. Due to there is no obvious empirical relationship between seismicity features, magnitude and location of a coming earthquake in a particular time window, an earthquake prediction approach based on danger theory is proposed in this paper. It extracts eight indicators calculated from earthquake data for recent years in Sichuan and surroundings by Gutenberg-Richter(GR) inverse power-law, and predicts quakes with magnitude lager than 4.5 during the following month by numerical differential based Dendritic Cell Algorithm (ndDCA). We compare this approach with six state-of-art earthquake prediction algorithms. Overall our algorithm yields the encouraging results in all the qualified parameters assessed, and it provides technical support for the application of earthquake prediction.

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Section: Introductionmentioning

confidence: 99%