The dynamic properties of mining induced seismic activity with respect to production rate, depth and size are studied in seven orebodies in the same underground iron ore mine. The objective is to understand the relationship between the measured seismic activity and the: seismic decay time, planned production rate, production size and mining depth. This relationship is the first step to individually customise the production rate for each orebody in the mine, make short-term predictions of future seismicity given planned productions, and to find out in what way the available predictors affect the seismicity. The seismic response with respect to the dependent variables is parametrised and the estimated decay times for each orebody, which are of particular interest here, are compared. An autoregressive model is proposed to capture the dynamic relationship between the induced seismic activity, the current production rate and the past seismic activity. Bayesian estimation of the parameters is considered and parameter constraints are incorporated in the prior distributions. The models for all orebodies are tied together and modelled hierarchically to capture the underlying joint structure of the problem, where the mine-wide parameters are learnt together with the individual orebody parameters from the observed data. Comparisons between the parameters from the hierarchical model and independent models are given. Group-level regressions reveal dependencies on size and mining depth. Model validation with posterior predictive checking using several discrepancy measures could not detect any model deficiencies or flaws. Posterior predictive intervals are evaluated and inference of model parameters are presented.

中文翻译：

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模拟采矿诱发地震活动与生产率、深度和规模之间的动态关系：矿山范围的分层模型

在同一地下铁矿的七个矿体中研究了采矿诱发地震活动在生产率、深度和尺寸方面的动态特性。目的是了解测量的地震活动与以下各项之间的关系：地震衰减时间、计划生产速度、生产规模和开采深度。这种关系是单独定制矿山中每个矿体的生产率、根据计划生产对未来地震活动进行短期预测以及找出可用预测器以何种方式影响地震活动的第一步。对因变量的地震响应进行参数化，并对每个矿体的估计衰减时间进行比较，这些在这里特别重要。提出了一个自回归模型来捕捉诱发地震活动、当前生产速率和过去地震活动之间的动态关系。考虑了参数的贝叶斯估计，并且在先验分布中加入了参数约束。所有矿体的模型都捆绑在一起并分层建模，以捕捉问题的潜在联合结构，其中矿区参数与观察数据中的单个矿体参数一起学习。给出了分层模型和独立模型的参数之间的比较。组级回归揭示了对规模和挖掘深度的依赖性。使用几种差异措施进行后验预测检查的模型验证无法检测到任何模型缺陷或缺陷。