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Hidden Markov models revealing the stress field underlying the earthquake generation

机译：隐藏的马尔可夫模型揭示了地震发生背后的应力场

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The application of the hidden Markov models (HMMs) is attempted for revealing key features for the earthquake generation which are not accessible to direct observation. Considering that the states of the HMM correspond to levels of the stress field, our objective is to identify these states. The observations are considered after grouping earthquake magnitudes and the cases of different number of states are examined. The problems of HMMs theory are solved and the ensuing HMMs are compared on the basis of Akaike and Bayesian information criteria. A new insight on the evaluation of future seismic hazard is given by calculating the mean number of steps for the first visit to a particular state, along with the respective variance. We further calculate an estimator of the mean number of steps for the first visit to a particular state and we construct its confidence interval. Additionally, a second approach to the problem is followed by assuming a different determination of observations. The HMMs applied to both approaches, contribute significantly to seismic hazard assessment via revealing the number of the stress levels as well as the way in which these levels are associated with certain earthquake occurrence.
机译：尝试使用隐马尔可夫模型（HMM）来揭示地震发生的关键特征，这些特征无法直接观察。考虑到HMM的状态对应于应力场的水平，我们的目标是识别这些状态。在对地震震级进行分组之后考虑观察结果，并检查不同状态的情况。解决了HMMs理论的问题，并根据Akaike和贝叶斯信息准则对随后的HMMs进行了比较。通过计算首次访问特定状态的平均步骤数以及相应的方差，可以对未来的地震危险性进行评估，从而获得新的见解。我们进一步计算出首次访问特定状态的平均步数估算值，并构造其置信区间。此外，针对问题的第二种方法是采用不同的观测值确定方法。应用于这两种方法的HMM通过揭示应力等级的数量以及这些等级与某些地震发生的关联方式，对地震危险性评估做出了重要贡献。

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