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Binary Coded Genetic Algorithm (BCGA) with Multi-Point Cross-Over for Magnetotelluric (MT) 1D Data Inversion
Wijanarko E.a,b, Grandis H.b
a Research and Development Centre for Oil and Gas Technology LEMIGAS, Indonesia
b Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung (ITB), Bandung, 40132, Indonesia
[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 IOP Publishing Ltd. All rights reserved.The magnetotelluric (MT) 1D modelling has been continuously receiving interest due to its effectiveness in obtaining overall subsurface resistivity image of an investigated area. The advances in computational resources allow increasing implementations of non-linear inversion using global search approach, such as Genetic Algorithm (GA). The genetic algorithm adopts the process of natural selection (survival for the fittest) and genetic transformation, i.e. selection, reproduction, mutation and population change, to solve the optimization problem. This paper discusses GA application in MT 1D modelling using binary coding representation with multi-point cross-over, i.e. one for every model parameter. The model parameters are resistivity and thickness of homogenously horizontal layers. The algorithm parameters need to be set to work properly, i.e. population size, number of genes, number of bits, crossover probability, mutation probability and number of generations. Despite binary coded GA (BCGA) drawbacks discussed in the literatures, we found that binary representation allows relatively extensive exploration of the search space. Test using synthetic data from three-layered synthetic models lead to satisfactory results, in terms of synthetic model recovery and data misfit comparable with the noise level contained the synthetic data.[/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]Binary coded genetic algorithms,Binary representations,Computational resources,Cross-over probability,Extensive explorations,Genetic transformation,Global search approach,Optimization problems[/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][/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][/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/10.1088/1755-1315/318/1/012029[/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]