1887

Abstract

Summary

One fundamental challenge of FWI is the local minimum issue caused by the cycle skipping between the predicted and acquired data. In order to overcome this challenge and achieve a successful inversion, a good initial model becomes a must-have ingredient of FWI. In another word, the background model has to be accurate enough to start FWI. Our proposed matching pursuit FWI (MPFWI) is designed to build the relation between travel time shift and model error in a different and novel way, so that FWI can be used to correct the erroneous background model, and therefore mitigate cycle skipping issues and improve the robustness of FWI with inaccurate initial models.

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/content/papers/10.3997/2214-4609.201412911
2015-06-01
2024-04-25
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References

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