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http://dx.doi.org/10.7582/GGE.2017.20.2.078

Trace Interpolation using Model-constrained Minimum Weighted Norm Interpolation  

Choi, Jihyun (Department of Earth Resources and Environmental Engineering, Hanyang University)
Song, Youngseok (Department of Earth Resources and Environmental Engineering, Hanyang University)
Choi, Jihun (Department of Earth Resources and Environmental Engineering, Hanyang University)
Byun, Joongmoo (Department of Earth Resources and Environmental Engineering, Hanyang University)
Seol, Soon Jee (Department of Earth Resources and Environmental Engineering, Hanyang University)
Kim, Kiyoung (Department of Geophysics, Kangwon National University)
Lee, Jeongmo (Department of Geology, Kyungpook National University)
Publication Information
Geophysics and Geophysical Exploration / v.20, no.2, 2017 , pp. 78-87 More about this Journal
Abstract
For efficient data processing, trace interpolation and regularization techniques should be antecedently applied to the seismic data which were irregularly sampled with missing traces. Among many interpolation techniques, MWNI (Minimum Weighted Norm Interpolation) technique is one of the most versatile techniques and widely used to regularize seismic data because of easy extension to the high-order module and low computational cost. However, since it is difficult to interpolate spatially aliased data using this technique, model-constrained MWNI was suggested to compensate for this problem. In this paper, conventional MWNI and model-constrained MWNI modules have been developed in order to analyze their performance using synthetic data and validate the applicability to the field data. The result by using model-constrained MWNI was better in spatially aliased data. In order to verify the applicability to the field data, interpolation and regularization were performed for two field data sets, respectively. Firstly, the seismic data acquired in Ulleung Basin gas hydrate field was interpolated. Even though the data has very chaotic feature and complex structure due to the chimney, the developed module showed fairly good interpolation result. Secondly, very irregularly sampled and widely missing seismic data was regularized and the connectivity of events was quite improved. According to these experiments, we can confirm that the developed module can successfully interpolate and regularize the irregularly sampled field data.
Keywords
MWNI; model-constrained MWNI; interpolation; regularization; aliasing;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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