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Abstract

Summary

Supreme than reverse-time migration, linearized inversion (least-squares reverse-time migration) inverts the Born approximation operator to build an imaging result, reflecting the actual amplitude of subsurface structures. The condition number of the Born approximation operator, adjoint of which is reverse-time migration operator, is the main factor affecting the inversion’s convergence. Conventional cross-correlation imaging condition of reverse-time migration introduces massive unrealistic low-frequency back-scattering noise, which usually has strong amplitude compared to the actual structure image. Even though the back-scattering noise can be suppressed by iteration, it will cost many iterations, which involves many evaluations of the Born approximation operator and its adjoint. The inverse scattering imaging condition can better condition the reverse time migration operator by removing the low-frequency back-scattering noise. Thus, this paper first implemented the exact adjoint of the reverse time operator with the inverse scattering imaging condition to drive inversion. Together with other proper selected preconditions toward the reverse time operator and its adjoint, our approach provides excellent Linearized inversion results evaluated with the Sigsbee 2b data set.

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/content/papers/10.3997/2214-4609.202210466
2022-06-06
2024-04-29

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References

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Preconditioned Linearized Inversion with the Inverse Scattering Imaging Condition
Conference Proceedings 2022, 1 (2022); https://doi.org/10.3997/2214-4609.202210466
/content/papers/10.3997/2214-4609.202210466
/content/papers/10.3997/2214-4609.202210466
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