Magnetotelluric Bayesian trans-dimensional inversion improved by parallel tempering algorithm
Yin Bin1,2, Hu Xiangyun1,2
1. Institute of Geophysics and Geomatics, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China;
2. Hubei Key Laboratory of Subsurface Multi-scale Imaging, Wuhan, Hubei 430074, China
Abstract：Conventional linearized gradient inversions have been widely used in magnetotelluric (MT) data processing.However,these inversions have several problems.For example,inversion results are closely dependent on the initial model,and solution uncertainty cannot be analyzed.To overcome these problems,we propose MT Bayesian trans-dimensional inversion optimized by parallel tempering algorithm.First we apply the trans-dimensional inversion to inverse MT data and analyze the uncertainty based on the stochastic inversion theory.Then,we bring in the parallel tempering algorithm in order to accelerate the inversion convergence.Finally,we simultaneously run several Markov Chains and make them swap in different temperatures.Tests on synthetic model with noise prove that the proposed inversion is obviously improved by the parallel tempering algorithm,and it is better than conventional ones.
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