We carry out inversion of the marine controlled-source electromagnetic data using genetic algorithm to estimate the subsurface vertical resistivity. 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 a-posteriori 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 two synthetic data sets generated from two different horizontally stratified earth models. The first model had one thin resistive hydrocarbon layer between the low-resistive sediments, whereas the second model had multiple thin resistive layers. For both cases, our inversion estimated the resistivity to a reasonable accuracy. Additionally, we tested our method to invert the multi-frequency data which further improved the quality of the inverted results. The results obtained from this inversion can form a basis for higher dimensional modelling and inversions. Also, this method can be easily extended to implement the joint inversion using seismic data.
Presentation Date: Wednesday, September 27, 2017
Start Time: 2:40 PM
Location: Exhibit Hall C/D
Presentation Type: POSTER