Nonlinear Bayesian inversion for estimating water pipeline dimensional and material parameters using acoustic wave dispersion

Abstract

Monitoring pipeline thinning and material degeneration is becoming important for water-filled pipeline condition assessment. In this paper, an inverse method is proposed for estimating a pipeline's dimensional and material parameters using the dispersion characteristics of its modal wavenumbers. The inverse method is established by matching observed wavenumber dispersion characteristics of the water-filled pipeline with forward model predictions, where pipeline inner radius, thickness and density, and longitudinal and transverse wave speeds of the pipeline wall material are taken as unknown parameters. To account for the strong nonlinearity of the inverse problem and improve inversion efficiency, a Bayesian inversion scheme is formulated using a parallel-tempering Markov chain Monte Carlo approach. The characteristics and the performance of the proposed inverse method are investigated by systematic simulations which cover the impact of the number of modes utilized, dispersion frequency interval and observation errors. Laboratory experiments are utilized to validate the inversion method using wavenumber dispersion observations (below 50 kHz) from three metallic pipelines all with the same outer radius but different wall thicknesses and materials. The uncertainties of the estimated dimensional parameters are found to be lower than 0.2 mm and different materials are successfully distinguished and identified for the three pipelines.