Details of the Abstract
| Title of paper | Hierarchical transdimensional Bayesian inversion of 2D magnetotelluric data |
| List of authors | Bajpai, A., Varma, S., Singh, A. |
| Affiliation(s) |
Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines) Dhanbad., Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines) Dhanbad., Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines) Dhanbad. |
| Summary |
In this study, we present an algorithm for 2D inversion of magnetotelluric (MT) data. The algorithm is based on the trans-dimensional Markov chain Monte Carlo scheme for estimating the probabilistic conductivity model for the MT data. The concept of the Voronoi cell is used to parameterize the earth. The number of Vornoi cells used to represent the model is treated as unknown. Additionally, the noise level is also treated as unknown and is estimated thus making the approach completely data-driven. As millions of forward calls are required to create an ensemble that is a good approximation of the posterior probability density, we employ a multi-resolution finite difference scheme to speed up the computations. In this scheme, the mesh used for computing forward responses is represented by vertically stacked subgrids. The horizontal grid resolution of subgrids decreases with depth. The algorithm is tested over synthetic data. The results demonstrate the estimation of noise levels and uncertainty quantification in the model parameters. |
| Session Keyword | 2.0 EM theory, modelling and Inversion |
| File upload |
2.0_hierarchical_transdimensi_bajpai_01.pdf
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