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Implementation of hybrid optimization for time domain electromagnetic 1D inversion
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).Time domain electromagnetic (TDEM) is a non-invasive geophysical method. This method is an active method that makes use of the electromagnetic wave properties so that the conductivity of the lithology in the subsurface can be derived. Inversion of TDEM data was usually calculated using derivatives of least square method. These methods have drawbacks, such as the results depend on good starting models, and the final results are sometimes stuck in a local minimum, instead of global minimum. These drawbacks can be overcome by using global optimization approach, such as Monte Carlo, simulated annealing, and genetic algorithm. However, these global optimization methods need long calculation time. Because of that, this research tries to combine these two approaches as a hybrid optimization method. The hybrid optimization method is combination of conjugate gradient (CG) method and very fast-simulated reannealing (VFSA) method. The algorithm was applied to invert several synthetic models of TDEM data. Noise was added to the synthetic data to test the capabilities of hybrid optimization algorithm. This combination method was the most efficient method when compared to the conjugate gradient or simulated annealing method separately. Levenberg-Marquardt and genetic algorithm inversion results of Volvi Basins TDEM data were compared with the hybrid optimization algorithm results. The hybrid optimization results were better than Levenberg-Marquardt results, and when compared to the genetic algorithm processes, the calculation times were faster.[/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/10.1063/1.4990922[/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]