1887
Volume 63 Number 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Seismic data acquired in hardrock environment pose a special challenge for processing. Frequent lack of clear coherent events hinders imaging and interpretation. Additional difficulty arises from the presence of significant amount of cultural noise associated with production and processing of ore, which often remains in the processed, stacked data. Motivated by those challenges, we developed an efficient workflow of denoising 3D post‐stack seismic data by using 2D discrete curvelet transform aimed at improving signal‐to‐noise ratio of the data. Our approach is based on the adjustment of the thresholds according to scales and angles in the curvelet domain, making parameterization flexible. We demonstrate effectiveness of our method using 3D post‐stack volumes from the three different mining camps in Canada, which were characterized by variable data quality. Remarkable signal enhancement, confirmed by the improvements in the mean signal‐to‐noise ratio of the dataset, is obtained not only due to random energy attenuation but also by removal of certain features corrupting the data (e.g., acquisition footprint). Comparison with the F‐X/F‐XY deconvolution results shows the superiority of our algorithm in respect to signal enhancement, signal preservation, and amount of the removed noise. Imaged structures, even if initially dominated by random energy, are easier to follow after curvelet denoising and enhanced for interpretation. Therefore, our approach can significantly reduce interpretation uncertainties when dealing with the seismic data acquired in the hardrock environment.

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/content/journals/10.1111/1365-2478.12234
2015-03-13
2020-08-05
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