@inproceedings{scholars17388,
           pages = {2714--2718},
       publisher = {European Association of Geoscientists and Engineers, EAGE},
         journal = {83rd EAGE Conference and Exhibition 2022},
           title = {ATTRIBUTE-ASSISTED IDENTIFICATION OF CARBONATE SEISMIC FACIES IN THE DANGEROUS GROUNDS REGION, DEEPWATER SABAH, MALAYSIA},
            year = {2022},
          volume = {4},
            note = {cited By 0; Conference of 83rd EAGE Conference and Exhibition 2022 ; Conference Date: 6 June 2022 Through 9 June 2022; Conference Code:184055},
          author = {Babikir, I. and Elsaadany, M. and Hermana, M. and Latiff, A. H. A. and Al-Masgari, A. A.},
            isbn = {9781713859314},
        keywords = {Carbonation; Geology; Learning systems; Neural networks; Seismology; Textures, Deepwater; Geologic information; Machine learning techniques; Malaysia; Middle Miocene; Reflection patterns; Seismic attributes; Seismic facies; Seismic facies analysis; Well control, Reflection},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142728544&partnerID=40&md5=fe41536f99c8c4fbd49af95b59a4cf05},
        abstract = {In underexplored regions, where well control is minimal, seismic facies analysis plays a crucial role in extracting geologic information. Furthermore, seismic attributes and machine learning techniques facilitate recognizing seismic facies in a quantitative and time-saving manner. We identify, primarily based on reflection patterns, several seismic facies in the Middle Miocene carbonate of the frontier Dangerous Grounds region. We employ texture attributes and an unsupervised artificial neural network to recognize those facies' natural grouping in the seismic volume. The basinal carbonates platform shows high amplitude, parallel, and highly continuous reflectors, whereas the marginal platform exhibits low to moderate amplitude, mounded reflection. The reefal buildups show mounded, divergent, and reflection-free patterns of weak internal reflectivity. The multiattribute analysis through the artificial neural network helps to quantify the targeted patterns, which is essential in understanding the depositional and diagenetic history of the carbonate buildups in the area. Copyright{\^A}{\copyright} (2022) by the European Association of Geoscientists \& Engineers (EAGE). All rights reserved.}
}