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• W4317207968 abstract "Abstract. We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterisation of the cover–basement interface. It consists of a cooperative inversion workflow relying on standalone inversion codes. Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining structural constraints with petrophysical prior information. First, we perform the 1D probabilistic inversion of MT data for all sites and recover the respective probabilities of observing the cover–basement interface, which we interpolate to the rest of the study area. We then calculate the probabilities of observing the different rock units and partition the model into domains defined by combinations of rock units with non-zero probabilities. Third, we combine these domains with petrophysical information to apply spatially varying, disjoint interval bound constraints (DIBC) to least-squares magnetic data inversion using the alternating direction method of multipliers (or ADMM). We demonstrate the proof-of-concept using a realistic synthetic model reproducing features from the Mansfield area (Victoria, Australia) using a series of uncertainty indicators. We then apply the workflow to field data from the prospective mining region of Cloncurry (Queensland, Australia). Results indicate that our integration methodology efficiently leverages the complementarity between separate MT and magnetic data modelling approaches and can improve our capability to image the cover–basement interface. In the field application case, our findings also suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement." @default.
• W4317207968 created "2023-01-18" @default.
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• W4317207968 date "2023-01-18" @default.
• W4317207968 modified "2023-10-16" @default.
• W4317207968 title "Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application" @default.
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• W4317207968 doi "https://doi.org/10.5194/se-14-43-2023" @default.
• W4317207968 hasPublicationYear "2023" @default.
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