Generalized linear sampling method for elastic-wave sensing of heterogeneous fractures. (3rd March 2017)

• Record Type: Journal Article
• Authors: Pourahmadian, Fatemeh; Guzina, Bojan B; Haddar, Houssem
• Is Part Of: Inverse problems. Volume 33:Number 5(2017:May)
• Publication Date: 2017-03-03
• Subjects: linear sampling method -- inverse scattering -- seismic imaging -- elastic waves -- fractures -- specific stiffness -- hydraulic fractures; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/33/5/055007
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: A theoretical foundation is developed for the active seismic reconstruction of fractures endowed with spatially varying interfacial conditions (e.g. partially closed fractures, hydraulic fractures). The proposed indicator functional carries a superior localization property with no significant sensitivity to the fracture's contact condition, measurement errors, or illumination frequency. This is accomplished through the paradigm of the F ♯ -factorization technique and the recently developed generalized linear sampling method (GLSM) applied to elastodynamics. The direct scattering problem is formulated in the frequency domain where the fracture surface is illuminated by a set of incident plane waves, while monitoring the induced scattered field in the form of (elastic) far-field patterns. The analysis of the well-posedness of the forward problem leads to an admissibility condition on the fracture's (linearized) contact parameters. This in turn contributes to the establishment of the applicability of the F ♯ -factorization method, and consequently aids the formulation of a convex GLSM cost functional whose minimizer can be computed without iterations. Such a minimizer is then used to construct a robust fracture indicator function, whose performance is illustrated through a set of numerical experiments. For completeness, the results of the GLSM reconstruction are compared to those obtained by the classical linear sampling method (LSM).