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Magnetotelluric 1D inversion using grid-search and Monte Carlo methods
Maulinadya S.a, Grandis H.a
a Institut Teknologi Bandung, 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]© 2018 European Association of Geoscientists and Engineers EAGE. All rights reserved.The magnetotelluric (MT) 1D inversion modeling is relatively simple and can be considered as a solved problem. Nevertheless, this subject still attracts researchers to study further for its nonlinearity, equivalence and usefulness for particular situation. For example, in the preliminary stage of a geothermal exploration program, 1D MT modeling is still employed to obtain an overall view of the subsurface resistivity distribution in the prospect area. Another example is the regional study of basement structures where the filling sediments are mostly stratified. From the computational point of view, the nonlinear, gradient-free approaches to the inverse problems are gaining interest due to the availability of powerful computational resources. The paper describes the 1D MT inverse problem resolution using grid-search and Monte Carlo approach. The “a priori” information is set to define the model space or search area. The grid-search attempts to search for the solution by evaluation of all possible models contained in the model space. The Monte Carlo approach is used to reduce the execution time using random samples from the model space. Tests using synthetic and field data associated with simple synthetic models showed satisfactory results, in terms of model recovery and misfit.[/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]Basement structures,Computational resources,Geothermal exploration,Monte Carlo approach,Problem resolution,Random sample,Resistivity distributions,Synthetic models[/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/10.3997/2214-4609.201800391[/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]