Subsurface Sensing Under Sensor Positional Uncertainty

Abstract

We consider the problem of classifying buried objects using electromagnetic induction data collected in a setting where there are errors in sensor positioning. Using a series of decay constants (or equivalently, Laplace plane poles) as features for classification, our algorithm seeks to estimate these poles and, subsequently, to determine the type of object in the sensor field of view. In many practical scenarios, a set of data is often accompanied by domain knowledge that the location of the transmitters and/or receivers is only known to within some degree of accuracy (e.g., 10 cm in the along-track direction and 5-cm cross-track). Here, we develop an approach to the extraction of information from such data sets in which the quantitative positional bound information is used in the context of a min-max optimization strategy. Specifically, we look for the parameters of interest that minimize the maximum data residual, where the maximum error is computed over ellipsoids or polyhedra of possible sensor locations defined by the bound information. Our formulation admits data collection with independent or dependent positional uncertainty values at successive nominal collection locations. Our algorithms for solving this optimization problem are validated using simulated and measured data