[vc_empty_space][vc_empty_space]
A Comparative Study of Simulated Annealing and Genetic Algorithm Method in Bayesian Framework to the 2D-Gravity Data Inversion
Putra A.S.a, Sukonob, Srigutomo W.c, Hidayat Y.b, Lesmana E.d
a Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia
b Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia
c Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Indonesia
d
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 Published under licence by IOP Publishing Ltd.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Artificial intelligent,Bayesian,Bayesian frameworks,Comparative studies,Geophysical inversion,Gravity method,Optimization method,Posterior probability[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bayesian distribution,genetic algorithm,geophysical inversion problem,gravity method,simulated annealing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Sincere thanks to the Rector, Director of DRPMI, and Dean of FMIPA of Universitas Padjadjaran, which have granted us the Program of the Academic Leadership Grant (ALG) under the coordination of Prof. Dr. Sudradjat, and a grant program of Competence Research of Lecturer of Universitas Padjadjaran (Riset Kompetensi Dosen Unpad/RKDU) under the coordination of Dr. Sukono, for boosting and increasing research activities and publications to researchers at Universitas Padjadjaran.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1742-6596/1204/1/012079[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]