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Pareto-Optimal Multi-objective Inversion of Geophysical Data

机译：Pareto-最佳地球物理数据的多目标反演

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摘要

In the process of modelling geophysical properties, jointly inverting different data sets can greatly improve model results, provided that the data sets are compatible, i.e., sensitive to similar features. Such a joint inversion requires a relationship between the different data sets, which can either be analytic or structural. Classically, the joint problem is expressed as a scalar objective function that combines the misfit functions of multiple data sets and a joint term which accounts for the assumed connection between the data sets. This approach suffers from two major disadvantages: first, it can be difficult to assess the compatibility of the data sets and second, the aggregation of misfit terms introduces a weighting of the data sets. We present a pareto-optimal multi-objective joint inversion approach based on an existing genetic algorithm. The algorithm treats each data set as a separate objective, avoiding forced weighting and generating curves of the trade-off between the different objectives. These curves are analysed by their shape and evolution to evaluate data set compatibility. Furthermore, the statistical analysis of the generated solution population provides valuable estimates of model uncertainty.
机译：在建模地球物理属性的过程中，提供不同的数据集可以大大改善模型结果，只要数据集兼容，即对类似的特征敏感。这种联合反演需要不同数据集之间的关系，其可以是分析或结构的。经典上，联合问题表示为标量目标函数，它组合了多个数据集的错入功能和一个联合术语，其用于数据集之间的假定连接。这种方法遭受了两个主要缺点：首先，可以难以评估数据集的兼容性和第二，但错误的聚合引入了数据集的加权。我们介绍了一种基于现有遗传算法的Pareto最优的多目标联合反演方法。该算法将每个数据设置为单独的目标，避免强制加权和生成不同目标之间的权衡的曲线。通过它们的形状和演进分析这些曲线以评估数据集兼容性。此外，产生的溶液群体的统计分析提供了模型不确定性的有价值估计。

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