Target-oriented image-domain elastic least-squares reverse time migration

• Published in: Journal of applied geophysics Vol. 229; p. 105496
• Main Authors: Wang, Mingqian, He, Bingshou
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-10-2024
• Subjects: Elastic wave; Image-domain least-squares migration; Multi-parameter Hessian matrix; Point spread functions; Reverse time migration; Target-oriented imaging; Multi-parameter Hessian matrix; Point spread functions; Elastic wave; Image-domain least-squares migration; Reverse time migration; Target-oriented imaging
• ISSN: 0926-9851
• DOI: 10.1016/j.jappgeo.2024.105496

Elastic least-squares reverse time migration (ELSRTM), as an imaging method, offers advantages over conventional elastic reverse time migration (ERTM), including higher resolution, better amplitude balancing, reduced crosstalk, and broader bandwidth. However, conventional ELSRTM involves iterative processes in the data domain, resulting in high computational costs. Moreover, since time is continuous during data domain extrapolation, it cannot solely focus on the target area within the subsurface medium. In contrast, image-domain ELSRTM (IDELSRTM) exhibits high computational efficiency and the ability to image target area. Currently, research on image-domain least-squares reverse time migration is predominantly focused on the acoustic wave assumption, despite elastic waves being closer to the actual subsurface medium and providing richer imaging information. In this study, within the framework of data domain ELSRTM, we derived the objective function for the IDELSRTM and introduced an L1 regularization term under the L2 norm to enhance inversion stability. We devised an inversion strategy employing the fast iterative shrinkage-thresholding algorithm (FISTA). Furthermore, drawing from the point spread functions theory in optics, we derived the mapping relationship between the elastic multi-parameter point spread functions (PSF) and the elastic multi-parameter Hessian matrix, and the relationship between the Hessian matrix and the ERTM images. We provided the computational method for the elastic multi-parameter Hessian matrix and utilized it as the linearized forward operator for IDELSRTM. Through numerical experiments, we further elucidated the relationship between the ERTM images and the Hessian matrix under the framework of IDELSRTM, along with the sources of crosstalk in ERTM. Applying our proposed target-oriented IDELSRTM method to layered models and the Marmousi2 model, we demonstrated its effectiveness in improving imaging resolution and quality with only a marginal increase in computational overhead compared to conventional ERTM.
•A target-oriented image-domain elastic least-squares reverse time migration method is proposed.•The relationship between elastic point spread functions and the elastic multi-parameter Hessian matrix is elucidated.•The sources of crosstalk in elastic migration images are revealed in image-domain.•This method helps to improve imaging resolution and broaden the bandwidth.