Quantitative bed-type classification for a global comparison of deep-water sedimentary systems

Characterisation of deep-water successions is often undertaken at the scale of sedimentary beds. However, different studies often apply alternative bed-type classification schemes, rendering the quantitative comparison of bed properties of different deep-water systems difficult. In this study a quantitative approach to the development of a universal deep-water bed-type classification scheme is proposed based on the synthesis of a large sedimentological dataset, containing >32,000 deep-water facies and >10,000 beds accumulated in 27 turbidite-dominated systems. The classification scheme is applicable to discriminate and categorise lithological (sand, gravel) layers and is based on: (i) the proportion of, gravel, sand, sandy-mud and muddy-sand in the bed, (ii) the presence and nature of vertical sharp grain-size changes, and (iii) the presence and thickness ratio of laminated sedimentary facies. Comparing the bedding properties of channel-fills, terminal deposits (e.g. lobes or sheets) and levees showed that the three architectural-element types are characterised by differences in bed frequency and thickness, overlying mudstone proportions, vertical bed thickness trends, mud thickness and sand-gravel fraction values. Building on these recognised statistical differences an algorithm was developed that is capable of generating, in a stochastic manner, geologically realistic synthetic sedimentary logs depicting deep-water terminal-deposit, channel-fill and levee elements. The one-dimensional facies modelling is governed by a series of input parameters, including total number of beds, sand-gravel thickness, and sand-gravel fraction. The approach can be tailored to produce synthetic logs for specified types of depositional systems (e.g., categorised according to dominant grain size of deposits, age of deposition and global climate (icehouse vs. greenhouse conditions)). A large number of synthetic sedimentary logs can be generated, which can be utilised as training datasets in machine learning algorithms developed to aid subsurface interpretations of clastic sedimentary successions.