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Curvelet-Based 2-D Inversion for MT DataNormal access

Authors: Y. Su, C. Yin, Y. Liu, B. Zhang, X. Ren, X. Huang, C. Wang and J. Cai
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Electromagnetic Methods A
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201900702
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.04Mb )
Price: € 20

Summary:
s function of the curvelet transform is the “wedge base” that satisfies the anisotropic scale relationship (width∝length2) and has the characteristic of arbitrary directivity. Thus, it has the capability to “optimally” represent the edge of the target objects. To achieve a sparse constraint, we use L1-norm of the curvelet coefficients for the inversion. This can help extract the features of target objects more sparsely and get high-resolution inversion results. We compare the results of our curvelet-based inversion with those based on the traditional L1-norm and L2-norm inversions. The experiments with theoretical data demonstrate that the sparse constraint inversion based on the curvelet transform can better reveal the boundaries of target objects.


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