When is McMC feasible for AVO inversion?

TY - ABST

T1 - When is McMC feasible for AVO inversion?

AU - Kuppens, Santosh

AU - Madsen, Rasmus Bødker

AU - Hansen, Thomas Mejer

PY - 2018/3

Y1 - 2018/3

N2 - I show that one is able to use a McMC method for solving the AVO problem. A linear forward model is used for inversion of synthetic datasets, each with different signal-to-noise ratios. The (mean of the) obtained posterior distribution is compared to the closed-form expression (MAP-solution), to conclude that this approach samples the right distribution. An autocorrelation analysis is applied to obtain the autocorrelation time τ for each posterior distribution. One finding is that for each S/N ratio, τ stagnates. Furthermore, an exponential relation is found between S/N and τ.

AB - I show that one is able to use a McMC method for solving the AVO problem. A linear forward model is used for inversion of synthetic datasets, each with different signal-to-noise ratios. The (mean of the) obtained posterior distribution is compared to the closed-form expression (MAP-solution), to conclude that this approach samples the right distribution. An autocorrelation analysis is applied to obtain the autocorrelation time τ for each posterior distribution. One finding is that for each S/N ratio, τ stagnates. Furthermore, an exponential relation is found between S/N and τ.

M3 - Conference abstract for conference

T2 - 80th EAGE Conference and Exhibition

Y2 - 11 June 2018 through 15 June 2018

ER -