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Abstract

Summary

Full waveform inversion (FWI) has proven that it has potential to provide high-resolution velocity parameters. However, for most cases, FWI suffers from an objective function with a local minimum instead of a global minimum due to cycle skipping between real data and predicted data. To avoid this issue, researchers have proposed an FWI work flow that uses offset stripping and inner mutes to limit the input for FWI to near offsets for the initial inversion iteration, and then gradually incorporates farther offsets in subsequent iterations as the velocity model accuracy improves with depth. However, this work flow is computationally expensive and cannot effectively avoid the cycle skipping issue. We propose a data selection algorithm that assures all input data for FWI is within a half-cycle difference compared with the predicted data. This data selection process is implemented in each iteration of the inversion to generate a velocity model with higher accuracy and fewer artefacts.

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/content/papers/10.3997/2214-4609.20140712
2014-06-16
2024-03-28
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