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The Deep-Water Architecture Knowledge Base: towards an objective comparison of deep-marine sedimentary systems

¹ Turbidite Research Group, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK(e-mail: j.baas@earth.leeds.ac.uk)
² Rock Deformation Research, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK

A quantitative method is proposed for the comparison of deep-marine clastic depositional systems and the analysis of their architectural properties. The method comprises a knowledge base of quantitative, literature-derived information from modern and ancient, surface and subsurface deep-water systems, implemented as a relational database management system and referred to as the Deep-Water Architecture Knowledge Base (DWAKB). The types of information contained within the knowledge base include: (1) internal and external parameters controlling system architecture, such as tectonic setting, grain size of available sediment, degree of basin confinement and number and distribution of sediment input points; (2) the dimensions of architectural elements, such as channels, levees, lobes, open slope and basin plain; (3) the spatial organization of the architectural elements; and (4) the bed thickness distribution and proportion of different lithologies within the architectural elements. The potential value of the DWAKB for comparative studies of deep-marine clastic systems is considerably higher than that of classification schemes and system analogue concepts presently available. Thus, in contrast to classification schemes, the knowledge base is not limited in the number of controlling parameters and it does not have a limited time span because of the flexibility to update existing records and add new datasets. Moreover, system analogues can be selected more objectively through the use of statistical methods, and the knowledge base allows unsurpassed integration of large-scale architectural data with bed- and facies-scale data. The expected value of the DWAKB is illustrated with several examples of quantitative data analysis. These include the determination of the frequency of occurrence of levees in systems of different grain size, the calculation of the dimensions of submarine channels as a function of grain size and proximity to the source area, and the construction of idealized models for sand-rich and mixed mud–sand systems based on probabilities of spatial transitions between architectural elements.

KEYWORDS: clastic sediments, submarine environment, turbidite, knowledge-based systems