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This paper presents new theoretical representations for multidimensional down-down deconvolution methods used to remove surface-related multiples from ocean-bottom seismic data. Using reciprocity theory, the authors overcome previous limitations based on horizontally layered medium assumptions, developing both receiver-side and source-side down-down deconvolution approaches. Each method offers distinct advantages: receiver-side deconvolution better samples the shallow subsurface using downgoing wavefields, while source-side deconvolution benefits from denser source arrays but faces challenges with sparse receiver configurations.
Why it matters
Improved methods for removing unwanted reflections from seismic data enhance the quality of subsurface imaging, which is critical for oil and gas exploration, geological surveys, and understanding Earth's structure. These theoretical advances provide more flexible alternatives to existing techniques, potentially improving data processing in complex geological environments.
Understand the Science
arXiv:2509.13123v2 Announce Type: replace
Abstract: Multidimensional up-down deconvolution effectively eliminates surface-related multiples from ocean-bottom seismic data. Recently, several down-down deconvolution methods have been introduced as attractive alternatives. Whereas multidimensional up-down deconvolution fully accounts for lateral variations of the medium parameters, the underlying theory of some of the down-down deconvolution methods is essentially based on the assumption that the medium is horizontally layered. Using reciprocity theory, this assumption is circumvented. This leads to representations for either receiver-side or source-side multidimensional down-down deconvolution. Compared with multidimensional up-down deconvolution, receiver-side down-down deconvolution only utilizes the downgoing part of the wavefield that better samples the shallow subsurface, but it is not entirely data-driven. Source-side down-down deconvolution benefits from the better sampled source array, but in the presence of sparsely sampled receivers it requires solving an underdetermined system of linear equations.