Pemodelan Inversi Non-Linier dalam Geofisika Menggunakan Algoritma Rantai Markov

Abstract

In the frame of Bayesian inference, the inverse problem solution is described by posterior probability density function (PDF) in the model space. Estimation of PDF in a multidimensional space is based on a Markov chain having invariant probability identical to the posterior PDF of the model. Extensive exploration of the model space using a stochastic technique is expected to circumvent local minima. The Markov chain algorithm has been applied to non-linear inversion of magnetotelluric (MT) data using 1-D and 2-D models with satisfactory results, i.e. synthetic models have been recovered.