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Wan, Le (Department of Geology and Geophysics, University of Utah) | Puahengsup, Pechet (Department of Geology and Geophysics, University of Utah) | Zhdanov, Michael S. (Department of Geology and Geophysics, University of Utah)
In this paper we develop a fast 3-D electromagnetic (EM) migration method for marine geophysical exploration. The developed migration algorithm is based on downward extrapolation of the observed EM field using a special form of finite-difference equation for the migration field. It allows us to migrate within the sea-bottom formations the EM signals observed by the sea-bottom receivers. The migration field is subsequently transformed in the resistivity image of the sea-bottom geoelectrical structures. This technique is in an order faster than the conventional inversion. It can be used for fast imaging of the marine magnetotelluric (MT) and controlled-source electromagnetic (CSEM) data in off-shore hydrocarbon (HC) exploration.
A thin sheet model consists of one or several horizontal,interpretation of 3-D electromagnetic data.
We carry out the inversion of marine controlled-source electromagnetic data using real coded genetic algorithm to estimate the isotropic resistivity. Unlike linearized inversion methods, genetic algorithms belonging to class of stochastic methods are not limited by the requirement of the good starting models. The objective function to be optimized contains data misfit and model roughness. The regularization weight is used as a temperature like annealing parameter. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the aposteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on three synthetic data sets generated from horizontally stratified earth models. For all cases, our inversion estimated the resistivity to a reasonable accuracy. The results obtained from this inversion can serve as starting models for linearized/higher dimensional inversion.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: Poster Station 13
Presentation Type: Poster
For efficiency, forward and inverse models are matrix of partial derivatives.