Data-driven Green's function retrieval and imaging with multidimensional deconvolution

Abstract

Standard imaging techniques rely on the single scattering assumption. This requires that the recorded data do not include internal multiples, i.e. waves bouncing multiple times between layers before reaching the receivers at the acquisition surface. When multiple reflections are present in the data, standard imaging algorithms incorrectly image them as ghost reflectors. These artifacts can mislead the interpreters in locating potential hydrocarbon reservoirs. Recently, we introduced a new approach for retrieving the Greens function recorded at the acquisition surface due to a virtual source located at depth. Additionally, our approach allows us to decompose the Green's function in its downgoing and upgoing components. These wave fields are then used to create a ghostfree image of the medium with either crosscorrelation or multidimensional deconvolution, presenting an advantage over standard prestack migration. We illustrate the new method with a numerical example based on a modification of the Amoco model.