Spatially continuous probabilistic prediction of sparsely measured ground properties constrained by ill-posed tomographic imaging considering data uncertainty and resolution

Abstract

Probabilistic prediction of 2D or 3D distributions of sparsely measured borehole or direct-push logging data can contribute to solving hydrological, petroleum, or engineering exploration tasks. We use and improve a recently developed workflow constrained by ill-posed geophysical tomography to achieve 2D probabilistic predictions of geotechnical exploration target parameters that could only be measured by 1D borehole or direct-push logging. We use artificial neural networks (ANNs) to find the optimal prediction models between ensembles of equivalent geophysical tomograms and sparsely measured logging data. During the training phase of ANNs, we consider four different training strategies taking into account the logging data uncertainty and geophysical tomographic ambiguity to avoid data overfitting of the ANNs. Thus, we successfully transform the logging data uncertainty and geophysical tomographic reconstruction ambiguity as well as differences in spatial resolution of logging and tomographic models into the probabilistic 2D prediction of our target parameters in a data-driven manner, which allows application of our methodology to any combination of geophysical tomograms and hydrologic, petroleum, or engineering target parameters solely measured in boreholes. To illustrate our workflow, we use an available field data set collected at a field site south of Berlin, Germany, to characterize near-subsurface sedimentary deposits. In this example, we employ cross-borehole tomographic radar-wave velocity, P-wave velocity, and S-wave velocity models to constrain the prediction of tip resistance, sleeve friction, and dielectric permittivity as target parameters.