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Statistical and Non-linear Dynamics Methods of Earthquake Forecast: Application in the Caucasus
Frontiers in Earth Science ( IF 2.9 ) Pub Date : 2020-05-14 , DOI: 10.3389/feart.2020.00194
Tamaz Chelidze , Giorgi Melikadze , Tengiz Kiria , Tamar Jimsheladze , Gennady Kobzev

In 20th century, more than 10 strong earthquakes (EQs) of magnitudes 6,7 hit South Caucasus, causing thousands of casualties and gross economic losses. Thus, strong-EQ forecast is an actual problem for the region. In this direction, we developed a physical percolation model of fracture, which considers the final failure of solid as a termination of the prolonged process of destruction: generation and clustering of micro-cracks, till appearance—at some critical concentration—of the infinite cluster, marking the final failure. Percolation provides a model of preparation of an individual strong event (slip or EQ). The natural seismic process contains many such events: the appropriate model is a non-linear stick-slip model, which is a particular case of the general theory of the integrate-and-fire process. Non-linearity of the seismic process is in contradiction with a memoryless Poissonian approach to seismic hazard. The complexity theory offers a chance to improve strong EQs’ forecast using analysis of hidden (non-linear) patterns in seismic time series, such as attractors in the phase space plot. For a regional forecast, we applied the Bayesian approach to assess the conditional probability expected in the next 5 years of strong EQs of magnitudes five and more. Later on, in addition to Bayesian probability assessment, we applied to seismic time series the pattern recognition technique, based on the assessment of the empirical risk function [generalized portrait (GP) method]: nowadays, this approach is known as the support vector machine (SVM) technique. The preliminary analysis shows that application of the GP technique allows predicting retrospectively 80% of M5 events in Caucasus. Besides long- and middle-term forecast studies, intensive work is under way on the short-term (next-day) EQ prediction also. Here, we present the results of multiparametrical (hydrodynamic and magnetic) monitoring carried out on the territory of Georgia. In order to assess the reliability of the precursors, we used the machine learning approach, namely, the algorithm of deep learning ADAM, which optimizes target function by a combination of optimization algorithm designed for neural networks and a method of stochastic gradient descent with momentum. Finally, we used the method of receiver operating characteristics (ROC) to assess the forecast quality of this binary classifier system. We show that the true positive rate statistical measure is preferable for the EQ forecast.



中文翻译:

地震预报的统计和非线性动力学方法:在高加索地区的应用

在20世纪,南高加索地区发生了10多次6.7级强地震,造成数千人伤亡和经济损失。因此,强烈的情商预测是该地区的实际问题。在这个方向上,我们建立了物理渗流破裂模型,该模型将固体的最终破坏视为延长破坏过程的终止:微裂纹的产生和聚集,直到无限临界团簇的出现(在一定的临界浓度下)。 ,标志着最终的失败。渗流提供了准备单个强事件(滑移或均衡)的模型。自然地震过程包含许多此类事件:合适的模型是非线性粘滑模型,这是“集成并发射”过程的一般理论的特例。地震过程的非线性与地震记忆的无记忆泊松方法矛盾。复杂性理论为通过分析地震时间序列中的隐藏(非线性)模式(例如相空间图中的吸引子)提供了改进强均衡器预测的机会。对于区域性预测,我们使用贝叶斯方法评估了未来5年内等强度大于等于5的强大EQ的条件概率。后来,除了贝叶斯概率评估之外,我们还基于经验风险函数[通用肖像(GP)方法]的评估,将模式识别技术应用于地震时间序列:如今,这种方法被称为支持向量机(SVM)技术。初步分析表明,使用GP技术可以追溯预测高加索地区M5事件的80%。除了长期和中期预测研究以外,短期(第二天)情商预测也正在进行大量工作。在这里,我们介绍了在佐治亚州境内进行的多参数(水动力和磁)监测的结果。为了评估前体的可靠性,我们使用了机器学习方法,即深度学习ADAM算法,该算法通过为神经网络设计的优化算法和具有动量的随机梯度下降方法相结合来优化目标函数。最后,我们使用接收器工作特征(ROC)方法来评估此二进制分类器系统的预测质量。

更新日期:2020-06-29
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