Details of the Abstract
| Title of paper |
The analysis of topography and distortion effects in real data: A Case Study on 3D inversion of MT data from the Northeast Carpathian Volcanic Arc |
| List of authors | Neukirch, M., Varılsüha, D., Minakov, A., Smirnov, M. |
| Affiliation(s) | Marine Science Institute ICM-CSIC, Istanbul Technical University, University of Oslo, Luleå University of Technology |
| Summary |
The interpretation of magnetotelluric data (MT) acquired in the areas with rugged topography and galvanic distortion can be challenging. While 3D MT inversion algorithms capable of tackling these issues exist, their widespread adoption is hindered by ongoing development and complexities in implementation such as an inverse problem solution on multi-resolution adaptive meshes. Particularly, one way to mitigate galvanic dis- tortion during the inversion process can be to introduce additional constraints on the data and weights in the objective function. However, this approach requires determining appropriate values for weights given varying data confidence levels, site distribution, and data coverage in terms of available periods at each site. This communication presents our methodology for evaluating the impact of topography and distortion on the interpretation of MT observations, on the example of a 3D MT survey collected as a part of the geothermal resource assessment project at the Northeast Carpathian Volcanic Arc in Northern Romania. The study area is located in the Baia Mare mining district, characterized by scattered near-surface conductive anomalies and diverse terrain with significant elevation changes exceeding 800m between sites and 1300m across the model. We use two different software packages to test various approaches for the quantitative interpretation of real data: a finite-difference ModEM code widely used for 3D MT inversion Kelbert et al (2014) and a more re- cently developed DEVA3DMT Varılsüha (2020) which is a hybrid finite element method and it is capable of accommodating both topographical features and estimating distortion parameters together with the inversion for resistivity of the subsurface. We assess the performance with both uncorrected and distortion-corrected data and highlight issues arising from incorporating coarse topography grids, such as near-surface conductivity anomalies and biased results in forward modeling, respectively. We evaluate the impact of each of the two fac- tors by incorporating topography and distortion correction in the inversion workflow. Through this analysis, we underscore the importance of considering both topography and galvanic distortion in MT data interpretation, offering insights into the effectiveness of different inversion approaches. |
| Session Keyword | 3.0 EM methods for exploration (geothermal, mineral resources, etc.) |
| File upload |
3.0_the_analysis_of_topograph_varilsuha.pdf
3P43.pdf |