Evolution of statistical earthquake models to account for measurement errors and dependence.

已经提出并制定了许多策略来降低环境中的二氧化碳排放。碳捕获与封存（CCS）系统已被确定为最有效的策略之一。为了使这类地质构造能够安全发展，应首先对相关诱发地震活动的任何危险进行评估。这种评估需要扩展现有的评估构造地震和人为地震潜在危害的方法。具体而言，我们的研究旨在增强现有的统计模型，如随机点过程模型（ETAS模型）和极值方法，这些模型通常用于地震分析，纳入当前方法中被忽视的特征。例如，我们的研究挑战了关于地震记录和协变量信息的假设，调查了假定的独立性对余震震级的影响。我们项目的另一个新颖方面涉及计算点过程和极值方法中的测量误差，识别从传感器网络振动推断的地震震级，时间和位置的误差特性。在这个研究项目中，我们使用历史地震目录。我们的目标是探索这些强假设如何影响结果，并纳入地震活动建模中不确定性和依赖性的相关研究。工业合作伙伴：壳牌。

Numerous strategies have been proposed and developed to lower CO2 emissions within the environment. Carbon Capture and Storage (CCS) systems have been identified as one of the most effective strategies. To enable a safe growth of such geological formations, any dangers over the linked induced seismic activity should first be evaluated. This evaluation requires extending existing approaches for assessing the potential hazards of tectonic earthquakes and human-induced earthquakes. Specifically, our research aims to enhance existing statistical models, such as stochastic point process models (ETAS models) and extreme value methods, commonly used in earthquake analysis, incorporating features overlooked in current approaches. For instance, our research challenges assumptions about earthquake records and covariate information, investigating the effects of assumed independence in aftershock magnitudes.Another novel aspect of our project involves accounting for measurement errors in both point process and extreme value methods, recognising the error properties of earthquake magnitudes, time and locations inferred from sensor network vibrations. Within this research project, we use historical earthquake catalogues. We aim to explore how these strong assumptions affect results and incorporate related studies of uncertainties and dependence in seismicity modelling.Industrial partner: Shell.