Low frequency full waveform seismic inversion within a tree based Bayesian framework. Issue 1 (9th October 2017)

• Record Type: Journal Article
• Authors: Ray, Anandaroop; Kaplan, Sam; Washbourne, John; Albertin, Uwe
• Is Part Of: Geophysical journal international. Volume 212:Issue 1(2018:Jan.)
• Page Start: 522
• Page End: 542
• Publication Date: 2017-10-09
• Subjects: Inverse theory -- Probability distributions -- Wave propagation; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggx428
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Limited illumination, insufficient offset, noisy data and poor starting models can pose challenges for seismic full waveform inversion. We present an application of a tree based Bayesian inversion scheme which attempts to mitigate these problems by accounting for data uncertainty while using a mildly informative prior about subsurface structure. We sample the resulting posterior model distribution of compressional velocity using a trans-dimensional (trans-D) or Reversible Jump Markov chain Monte Carlo method in the wavelet transform domain of velocity. This allows us to attain rapid convergence to a stationary distribution of posterior models while requiring a limited number of wavelet coefficients to define a sampled model. Two synthetic, low frequency, noisy data examples are provided. The first example is a simple reflection + transmission inverse problem, and the second uses a scaled version of the Marmousi velocity model, dominated by reflections. Both examples are initially started from a semi-infinite half-space with incorrect background velocity. We find that the trans-D tree based approach together with parallel tempering for navigating rugged likelihood (i.e. misfit) topography provides a promising, easily generalized method for solving large-scale geophysical inverse problems which are difficult to optimize, but where the true model contains a hierarchy of features at multiple scales.