Layered 2-D inversion of profile data, evaluated using stochastic models

Abstract

In a sedimentary environment, layered models are often capable of reproducing the actual geology more accurately than smooth minimum structure models. We present and evaluate a 2D inversion scheme with lateral constraints (2D-LCI) and sharp boundaries for continuous profile oriented data sets. Here, we focus on resistivity data. All data and models are inverted as one system, producing layered sections with laterally smooth transitions. The models are regularized through laterally equal constraints that tie interface depths and resistivities of adjacent layers. Prior information, originating from e.g. electrical logs, migrates through the lateral constraints to the adjacent models, making resolution of equivalences possible. Information from areas with well resolved parameters will, in a similar way, migrate through the constraints to help resolve the poorly constrained parameters. The estimated model is complemented by a full sensitivity analysis of the model parameters supporting quantitative evaluation of the inversion result. For evaluation we use broad-banded von Kármán covariance functions to create various geological realistic models. We compare results from the 2D-LCI routine with results from the widely used smooth minimum structure program Res2dinv. The comparison is point-to-point on resistivities in the model space. The statistics conclude that the 2D-LCI resolves the true models to the same level as Res2dinv. The clear distinction of separate units and unit boundaries is often critical in hydrogeophysical or geotechnical applications, and hence we suggest using a layered inversion scheme in these cases.