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Target-Oriented Fusion of Attributes in Data Level for Salt Dome Geobody Delineation in Seismic Data

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Abstract

Precise delineation of a salt dome’s geobody in seismic data requires intelligent integration, image fusion or combination of seismic attributes using advance methods. There are various attribute integration methods available and many other are still under development. In this study, we introduce a new strategy for feature extraction from seismic images followed by their combination at the data level and subsequent information integration on seismic image. The aim of the presented study was to introduce an efficient method for image segmentation using the ordered weighted averaging (OWA) and the logistic function methods. In other competitive methods, the combination of information is used by employing various weighting functions whereas in the OWA method the specific weights are defined according to the importance of the characteristics of the target under investigation, which can be enhanced in the extracted seismic attributes. Then, the images of seismic attributes are combined by the logistic function method to distinguish the target geobody from the rest of the image. In the logistic function method, the attributes are combined with modified equations with the fuzzy gamma and geometric mean operators. This strategy can define the boundaries of the target and distinguish the geobody in the seismic image. The methodology was applied to synthetic and real field datasets, which contain a salt dome. For comprehensive comparison of the performance of the proposed method with the OWA and the logistic function methods, their various modifications of competitive methods were also applied to the same datasets. The OWA with pessimistic and optimistic weighting algorithms were both applied to fuzzy and binary models. The modified fuzzy logistic function was also applied to the fuzzy and binary models whereas the modified geometric averaging was applied to the datasets. The results were compared qualitatively and quantitatively. For the synthetic data, a synthetic model was used as the base model for pixel-by-pixel comparison with the true model and the binary models. The accuracy is the ratio of pixels correctly selected as the target in the final binary model to the correct pixels of the target in the true model. For the field data example, however, because there was no true model available, an expert interpreted model was used as the base model. The qualitative comparisons of results for the synthetic and field datasets show that the OWA method can better identify the target under investigation in the seismic image. In the quantitative comparison, the OWA pessimistic method presented 99.00% and 94.6% accuracy in the synthetic and real field datasets, respectively.

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Authors and Affiliations

  1. Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

    Keyvan Khayer, Amin Roshandel Kahoo, Mehrdad Soleimani Monfared & Behzad Tokhmechi

  2. Geophysical Institute (GPI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Mehrdad Soleimani Monfared

  3. Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran

    Kaveh Kavousi

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  1. Keyvan Khayer
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  2. Amin Roshandel Kahoo
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  3. Mehrdad Soleimani Monfared
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  4. Behzad Tokhmechi
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  5. Kaveh Kavousi
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Correspondence to Amin Roshandel Kahoo.

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Khayer, K., Roshandel Kahoo, A., Soleimani Monfared, M. et al. Target-Oriented Fusion of Attributes in Data Level for Salt Dome Geobody Delineation in Seismic Data. Nat Resour Res 31, 2461–2481 (2022). https://doi.org/10.1007/s11053-022-10086-z

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  • DOI: https://doi.org/10.1007/s11053-022-10086-z

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