New understanding on the principle of earthquake

Author(s):  
Kunquan Lu ◽  
Zexian Cao

Earthquake is a natural disaster that causes enormous losses to human society and its prediction is a major scientific challenge widely concerned by the society. However, the mechanism of earthquake is far from clear, and the mainstream view in the international seismology community is that earthquakes are unpredictable. Based on some new concepts and new knowledge developed in physics, this study scrutinizes the incubation and occurrence of earthquake from a novel perspective, and introduces a new understanding of earthquake principle. It is found that the view of earthquake unpredictability originates from the incorrect understanding of both earthquake principle and the self-organized criticality (SOC). That is to say, earthquake is consistent with the laws of SOC, which means it would be impossible to make a medium- or long-term prediction, yet the short-term prediction should still be possible. The preconditions for successful prediction include understanding correctly of earthquake principle, obtaining sufficient characteristic precursory information, and gathering relevant geological data. Traditional seismology is based on the solid continuum mechanics which holds the view that earthquakes are caused by brittle fracture of crustal rocks through the so-called “elastic rebound” mechanism. This point of view is seriously inconsistent with many field observations, cannot account for many seismic phenomena. It therefore cannot obtain and understand the earthquake precursory information correctly, and naturally reach the false conclusion that earthquake is unpredictable. Based on the simple fact that the crust is composed of rock blocks with fault gouges filling in between them, we treat the crust as a discrete system and understand the earthquake incubation process by means of granular physics. The new understanding gained is that the tectonic force propagates through force chains formed by the rock blocks, and the rock blocks move in the manner of stick-slip. Furthermore, by carefully analyzing how the strength of crustal rocks and the distributions of tectonic force vary with depth, we propose that the physical mechanism of earthquake is plastic sliding of rock and a jamming — unjamming transition of rock motion. Our novel theory on the earthquake principle and the earthquake processes can explain many seismological phenomena that could not be understood in terms of traditional seismology, such as the heat-flow paradox and the cause of deep-focus earthquake, etc. Based on this new understanding of earthquake principle, we put forward suggestions on how to obtain the earthquake precursory information correctly, so as to realize the goal of short-term prediction of earthquake.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.


2017 ◽  
Vol 59 (2) ◽  
pp. 524-531 ◽  
Author(s):  
Yu Lei ◽  
Min Guo ◽  
Dan-dan Hu ◽  
Hong-bing Cai ◽  
Dan-ning Zhao ◽  
...  

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