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Time domain electromagnetic 1D inversion using genetic algorithm and particle swarm optimization
Yogi I.B.S.a, Widodoa
a Bandung Institute of Technology, 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]© 2017 Author(s).Most of geophysical data needs to be inverted, so that the inversion results can be interpreted further. There are two mayor methods to approach inversion calculation, they are least square methods with its derivative, and global optimization methods. The global optimization methods have two advantages over least square methods and its derivative; the inversion result are not sensitive to the starting model and this method can get results from global minimum instead of local minimum. These advantages make the global optimization methods give better results when priori data is unavailable. In this research we tried to implement genetic algorithm (GA) and particle swarm optimization (PSO) to do inversion of time domain electromagnetic (TDEM) 1D data with central loop configuration. The inversions were applied for synthetic. The starting models were varied from the closest to the furthest from the real synthetic models. After that, the results and the processes from the two global optimization methods were compared. This comparison results showed that the results and the performance were not much different. Both inversion results give similar models with the synthetic models. At the end of the research, the global optimizations methods were applied to TDEM real data from Volvi Basin, Greece. The inversion results of real data showed that there are two mayor resistivity layers for three layers model inversion and four resistivity layers for four and five layers model inversion. Overall PSO and GA gave similar results. However, PSO gave easier adjustment for the inversion process than the GA.[/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][/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/10.1063/1.4990901[/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]