Constraining gravity inversion with lower-dimensional seismic information: Imaging the eastern Yilgarn Craton
| Autor: | Rashidifard, Mahtab, Giraud, Jeremie, Lindsay, Mark, Jessell, Mark, Ogarko, Vitaliy |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| DOI: | 10.5281/zenodo.7687174 |
| Popis: | Integrating complementary geophysical datasets has proven to be a powerful tool for constraining the subsurface properties and also generating a model compatible with available datasets. As different geophysical surveys are designed with specific targets, their respective coverage usually do not overlap in all regions of interest. This prompts the development of new techniques that enable the integration of geophysical datasets with different spatial coverage in a single workflow. In this study, we aim at introducing a workflow that allows quantitative integration of geophysical datasets with different surface coverage, resolution, and levels of sparsity. We focus on constraining gravity inversion with seismic data sparsely distributed within the model space using a generalized level-set approach. An illustration of the applicability of the technique on 2D and 3D models with lower-dimensional constraints from seismic data is presented using two examples. We show that the uncertainty in target positioning can be quantitatively appended to the regularization terms allowing level-set to correct the boundary positioning. Furthermore, the flexibility of the approach in terms of including spatially distributed constraints from seismic interpretation in the level-set inversion is demonstrated. Finally, the primary results of the constrained level-set inversion on the Yamarna region are presented. The inverted density contrast model of the subsurface follows the detectible features of main greenstones in the seismic section. The resulting model encourages introducing new geological units and new structural constraints to be applied during modelling process of the region in future studies. Open-Access Online Publication: March 01, 2023 |
| Databáze: | OpenAIRE |
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