Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm
Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm
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DOI：
10.1111/1365-2478.12082
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发表时间：
2014-03
影响因子：
2.6
通讯作者：
I. Akca;T. Günther;M. Müller‐Petke;A. T. Basokur;U. Yaramanci
中科院分区：
文献类型：
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作者：
I. Akca;T. Günther;M. Müller‐Petke;A. T. Basokur;U. Yaramanci
磁共振测深(MRS)已日益成为水文地球物理中的一种重要方法，因为它可以估计基本的水力特性，如孔隙度和水力传导性。磁共振测深建模和反演需要一个电阻率模型。因此，联合解释或反演有利于减少单独进行磁共振测深和垂直电测深(VES)反演时产生的模糊性。提出了一种磁共振测深与垂直电测深联合反演的新方法。地下离散采用一维变厚度块体模型。与传统的强烈依赖初始模型的基于导数的反演方案不同，全局多目标优化方案(在这种情况下是遗传算法[GA])更适合在预定义的搜索空间中检查一组可能的解。多目标联合优化避免了在不应用加权方案的情况下一个目标凌驾于另一个目标之上。其结果是一组被称为帕累托最优集的非支配最优解。使用合成数据进行的测试表明，多目标联合优化在实验误差水平内逼近联合模型参数，并说明了折衷解的范围，这有助于理解两个模型和目标之间的一致性和冲突。总体而言，在北海一个岛屿的一次调查中测量到的现场数据的莱文伯格-马夸特反演提供了类似的解决方案。然而，多目标遗传算法提供了一种通过产生一组非支配解来探索搜索空间的有效方法。利用钻孔数据对反演结果进行了验证，表明所提出的遗传算法方法是对基于导数的反演的补充。
Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.