Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization

Abstract

S U M M A R YWe apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian posterior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys information on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of resolution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source-receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm's use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.

Transdimensional Love-wave tomography of the British Isles and shear-velocity structure of the East Irish Sea Basin from ambient-noise interferometry

“…In fact, the concept of transdimensionality is not limited to traveltime tomography but can be adapted to a number of different inverse problems including regression (Gallagher et al 2011), inversion of controlled source electromagnetic data (Ray et al 2014), inversion of surface-wave dispersion data (Young et al 2013), and joint inversion of surface-wave dispersion and receiver function data (Bodin et al 2012b). …”

Trans‐dimensional Bayesian inversion of controlled‐source electromagnetic data in the German North Sea

“…The interface probability also increases between 180 mbsf and 520 mbsf indicating one or more interfaces with large depth uncertainty. More detailed display options have been used to image the PPD for 2-D profiles, for example, by Ray et al (2014). The CF for the first receiver is well determined and slightly over 1, which may be related to unknown parameters such as water depth and time shift as discussed in the sections above.…”

Bayesian inversion of marine controlled source electromagnetic data offshore Vancouver Island, Canada

“…8 where the uncertainty information is displayed in terms of the marginal probability density profile, as well as marginal and joint marginal probability densities. More detailed display options have been used to image the PPD for 2-D profiles, for example, by Ray et al (2014). Additional inversion parameters, shown in the bottom panels of Figs 10 and 11, are calibration factor a and time delay dt.…”

“…Parameter relationships show some nonlinear behaviour, and parameters are both positively and negatively correlated. Taking into account parameter relationships may improve the interpretation, as, for example, done by Ray et al (2014) who evaluate the resistivity-thickness (RT) product at gas-reservoir depth. While resistivities from inversion alone are generally underestimated compared to drill-hole resistivities, the RT product yields better agreement and can be converted to resistivities with seismic depth constraints.…”