A Comparative Study of Simulated Annealing and Genetic Algorithm Method in Bayesian Framework to the 2D-Gravity Data Inversion
A Comparative Study of Simulated Annealing and Genetic Algorithm Method in Bayesian Framework to the 2D-Gravity Data Inversion
复制标题
贝叶斯框架下的模拟退火和遗传算法方法与二维重力数据反演的比较研究
DOI：
10.1088/1742-6596/1204/1/012079
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
E. Lesmana
中科院分区：
文献类型：
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作者：
A. S. Putra;Sukono;W. Srigutomo;Y. Hidayat;E. Lesmana
现代优化方法在地球物理反演中的应用，有效地给出了求解复杂非线性问题的鲁棒全局解。在这里，我们测试了两种基于人工智能的方法，模拟退火和遗传算法对重力数据的处理。利用预测的异常几何形状，解决了从grav2d开源中提取的综合重力数据的反演问题。在单参数反演和同时多参数反演中，观察了这些方法之间的差异，以评估计算速度和内存空间的使用。结果表明，遗传算法在求解简单的反演问题（小数据集，需要反演的参数较少）时比模拟退火算法慢，但在大数据集时效率高。同时，对于大数据，模拟退火在失配函数的全局最小值定位方面存在一定的问题。在后一种情况下，我们模拟了贝叶斯框架下的方法，以查看参数的后验概率分布。
The use of modern optimization method in geophysical inversion has effectively given a robust global solution in its application to solve a complex non-linearity problem. Here, we tested two artificial intelligent-based methods, the simulated annealing and the genetic algorithm to the gravity data. Using predicted anomalies geometries, these methods are addressed to invert a synthetical gravity data extracted from grav2d open source. Differences between these methods are observed in both single parameter inversion and simultaneous multi parameter inversion to evaluate the speed of computing and the use of Space in memory. The result give us an idea that the genetic algorithm are slower than the simulated annealing in solving a simple inversion problem (small data set and less parameter to be inverse) but efficient in a large data set. meanwhile, the simulated annealing faced some problem in locating a global minima of the misfit function for the large data. in the latter case, we simulated the methods in the Bayesian framework to see the distribution of posterior probability of the parameters.