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Abstract

Summary

Joint migration inversion (JMI) is a recently proposed full wavefield inversion method, which tries to minimize the mismatch between observed reflection data and forward modeled data. Transmission effects and surface/internal multiples are included in the forward modeling process using a multi-dimensional version of the Bremmer series. However, since the current implementation of JMI uses an angle-independent reflectivity model, to avoid over-parametrization, it cannot easily handle large-offset data due to the angle-versus-offset (AVO) effects in the measured data. In this work, we propose to mitigate this AVO challenge in JMI using a local attribute -- local orthogonalization -- between the modeled and recorded data during inversion. The local orthogonalization weight indicates those areas of modeled data that do not correlate well with the recorded data. Afterwards, the high orthogonalization part containing strong AVO effects is adaptively subtracted from the residual based on the orthogonalization weights. We demonstrate the effectiveness of our proposed method with a complex synthetic example based on the Marmousi model and a marine field dataset from the Vøring basin in Norway.

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/content/papers/10.3997/2214-4609.202010996
2020-12-08
2024-04-27

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References

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202010996
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Robust AVO-Preserving Joint Migration Inversion of Seismic Reflections Using a Data Mismatch Based on Local Orthogonalization
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202010996
/content/papers/10.3997/2214-4609.202010996
/content/papers/10.3997/2214-4609.202010996
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