Vector Pair Reverse Time Migration method (VPRTM)
The process of scattering the acoustical wave on an obstacle at any moment of time may be considered as an interaction of two interconnected vectors: the particle velocity vector of the incident wave and generated by scattering the vector of the reflected or scattered wave. Mathematical method the inversion of the wave front in time based on the differential equations of the first order allows investigating such interaction more carefully. Proposed Vector Pair Reverse Time Migration method demands large storage memory and high computer performance but provides the new opportunities for studying the environment.
The Reverse Time Migration(RTM) image processing (Baysal et.al.,1983; Whitmore, 1983; McMechan, 1983) is wide using for constrain the Common Image Gather (CIG) in Angle Domain (AD). The method RTM has the own internal artefacts because the method is based on the wave-equation. For overcoming such interference the image regularization is performed by filtration in an extended spaces ( including the ADCIG) of angle parameters (Yoon and Marfurd, 2006; Sava and Fomel, 2006; Zhang and McMechan, 2011). Despite the growing the performance of computer, there exist opinions, that using the RTM in full extended spaces is still very expansive (Vyas et.al., 2011, Guan et.al., 2013).
Nevertheless Erokhin et. al., 2017, suggested the new Vector Pair Reverse Time Migration (VPRTM) method, which really demands relatively large memory and high computer performance, but it seems that method provide the new opportunities for studying the environment. We propose the new conception the extraction and regularization, by the VPRTM method, of information for CIG, which allows us to create new subsurface images based on the new Imaging Conditions.
The distinctive feature of the proposed VPRTM method is incorporation the velocity vector in procedure of extraction the information for designing extended CIG. Proposed Vector Domain CIG contain essentially more information and more possibility regularization of image processing. The method introduces higher requirements to the hardware, but it makes it possible to calculate many interesting attributes simultaneously. Especially this is concern with the phase attributes. The new suite of attributes, calculated simultaneously, gives us the good perspective to interpret obtained information together, using a powerful statistical tool.
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