Detection of gravity field source boundaries using deconvolution method

Complications arise in the interpretation of gravity fields because of interference from systematic degradations, such as boundary blurring and distortion. The major sources of these degradations are the various systematic errors that inevitably occur during gravity field data acquisition, discretization and geophysical forward modelling. To address this problem, we evaluate deconvolution method that aim to detect the clear horizontal boundaries of anomalous sources by the suppression of systematic errors. A convolution-based multilayer projection model, based on the classical 3-D gravity field forward model, is innovatively derived to model the systematic error degradation. Our deconvolution algorithm is specifically designed based on this multilayer projection model, in which three types of systematic error are defined. The degradations of the different systematic errors are considered in the deconvolution algorithm. As the primary source of degradation, the convolution-based systematic error is the main object of the multilayer projection model. Both the random systematic error and the projection systematic error are shown to form an integral part of the multilayer projection model, and the mixed norm regularization method and the primal-dual optimization method are therefore employed to control these errors and stabilize the deconvolution solution. We herein analyse the parameter identification and convergence of the proposed algorithms, and synthetic and field data sets are both used to illustrate their effectiveness. Additional synthetic examples are specifically designed to analyse the effects of the projection systematic error, which is caused by the uncertainty associated with the estimation of the impulse response function.