Numerical Algorithms for Cooperative Imaging of Complex Geological Regions

收集了许多不同类型的数据，以便对底土进行成像。通常独立地使用相应的建模技术来表示不同的地质特征并计算模型响应。我们的目标是开发一种方法，使之成为一个合作企业。首席研究员已经开发了地震反演技术，对材料界面进行了相当复杂的三维建模。他也没有在其他方法上实现类似的能力，包括大地电磁反演。多目标优化是我们的方法所基于的数学技术。当多个模型具有耦合未知参数时，它为多个模型到相应数据集的合作拟合提供了一个自然的设置。通常，当独立运行时，这些不同的技术会产生相互冲突的结果。我们的多目标算法使用二次规划来计算权重，这些权重将不匹配泛函的向量标量化，同时尝试针对一组建议的参数向量来模拟用户的偏好。该算法试图生成帕累托最优参数向量，以供用户考虑。

Many different types of data are collected in order to image the subsoil. Corresponding modeling techniques are used, generally independently, to represent different geological features and calculate model responses. Our aim is to develop a methodology to make of this a cooperative enterprise. The principal investigator has already developed seismic inversion techniques, with fairly sophisticated three dimensional modeling of material interfaces. He is nor achieving similar capabilities for other methods, including magnetotelluric inversion. Multiobjective optimization is the mathematical technique on which we are basing our approach. It provides a natural setting for the cooperative fitting of multiple models to corresponding data sets, when the models have coupling unknown parameters. Usually, when run independently, these different techniques will produce conflicting results. Our multiobjective algorithm employs quadratic programming to calculate weights that scalarize the vector of misfit functionals, while trying to model the user's preferences with respect to a set of proposed parameter vectors. The algorithm attempts to generate Pareto optimal parameter vectors for the user's consideration.