Suppressing migration image artifacts using a support vector machine method

Yuqing Chen, Yunsong Huang, Lianjie Huang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Reverse time migration (RTM) can produce high-quality images of complex subsurface structures when using seismic data acquired by a reasonably dense data acquisition geometry. However, RTM produces significant image artifacts when using data from a sparse data acquisition geometry because of incomplete cancellation of migration "smiles."These artifacts obscure migration images of actual geology, leading to possible misidentification of important geologic features of interest. A specularity filter based on the semblance equation is commonly used in the dip-angle angle-domain common image gather (ADCIG) to preserve signals while suppressing image artifacts. In dip-angle ADCIG, the signals are assumed to have higher semblance scores because they are horizontally more coherent than the artifacts. However, this assumption fails when the image artifacts are severe. We have developed a new approach to suppressing migration image artifacts using a support vector machine (SVM) method. We first develop multiple criteria to distinguish between the signals and artifacts in the dip-angle ADCIG, rather than using only the semblance criterion. We then calculate the weights using a supervised SVM method. The weights approach one for valid signal points, and approach zero for artifact points. Finally, we apply the weights to the dip-angle ADCIG to preserve the effective signals and suppress the image artifacts. We verify the effectiveness of our method, denoted as SVM filtering, using numerical tests on synthetic and field data to produce migration images with improved signal-to-noise ratios and reduced aliasing artifacts.
Original languageEnglish (US)
Pages (from-to)S255-S268
Number of pages1
Issue number5
StatePublished - Sep 1 2020

Bibliographical note

KAUST Repository Item: Exported on 2022-06-21
Acknowledgements: This work was supported by the U.S. Department of Energy (DOE) through the Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC, for the National Nuclear Security Administration (NNSA) of the U.S. DOE under contract no. 89233218CNA000001. Y. Chen would like to thank King Abdullah University of Science and Technology for funding his graduate studies. This research used resources provided by the LANL Institutional Computing Program, which is supported by the U.S. DOE NNSA under contract no. 89233218CNA000001.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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