1887

Abstract

Summary

Land data processing is challenging for very noisy data and complex near-surface. Although many sophisticated processing techniques exist, however, on noisy data these often fail and are even prone to generate artifacts. The case study presents a time imaging workflow that is designed to handle very noisy data acquired with vibroseis, over a complex near-surface. For near-surface compensation, a model-based pick refinement strategy was used to discern the shingled first breaks properly and update the model through non-linear travel-time tomography. A basic yet effective signal conditioning strategy was used comprising of cascaded single-channel based processes to enhance the signal to noise ratio, restricting processing artifacts. A horizon-based migration velocity analysis on the constant velocity stacks was used to focus reflectors in areas where velocity trends on vertical semblance looked subjective. Finally, the remnant noise was tackled in the post-migration processing stage in three steps. These involve super gathers stacking to bolster reflection strength, structure-oriented filtering to enhance continuity without compromising discontinuities, and dipole filtering to improve resolution. This parsimonious approach has resulted in better quality time imaging over traditional land data imaging workflows.

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/content/papers/10.3997/2214-4609.202210235
2022-06-06
2024-03-28
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References

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