Basinal variation of seismic attribute response in deepwater architectural element recognition

The advancement of seismic attributes and visualization techniques has allowed the study of seismic geomorphology from 3D reflection data. The study of deepwater deposits denes and characterizes architectural elements depending on their genesis, morphology, and position along the slope and basin floor. However, every individual basin’s geological configuration determines the dimensions, morphology, and lithological composition of its architectural elements. To understand how seismic attributes help characterize geological settings, we employ multiple datasets with variable qualities since few studies elaborate on compiling and discussing the differences between basins. We explore and compare the use of seismic attributes to highlight deepwater architectural elements in three different basins around the world: The Ceará Basin in Equatorial Brazil, The Taranaki Basin in New Zealand, and The North Carnarvon Basin in Australia, focusing on the deepwater sedimentary section in each case. Although the first two datasets are examples of siliciclastic environments and the North Carnarvon, a mixed carbonate-siliciclastic exponent, the architectural elements identified in all the datasets are similar, as well as their attribute response. The results show that the most robust attributes to characterize deepwater elements such as incised channels, channel-levee systems, and lobes are a combination of geometric, amplitude derived, frequency, and textural attributes. These seismic attributes indicate morphological, lithological, bed stacking, and help to define the stratigraphic architecture. Moreover, we found that the co-rendering of RMS (lithology-proxy), coherence (morphology indicator), and curvature attributes help to define the internal configuration for most of the deepwater architectural elements. While each basin is unique, our results and comparisons serve as a guide for seismic interpreters to use in deepwater seismic geomorphology characterization.