Abstract
The Markov Chain Stochastic Process has been used both to analyze the vertical lithofacies of the Bida Sandstone (Campanian – Maastrichtian) in Bida area and interprete the corresponding depositional systems. Six lithofacies, comprising of F1: conglomerates (breccias); F2 : medium – very coarse, pebbly sandstone with dominant trough beddings ; F3: coarse-grained pebbly sandstone with small scale trough or planar cross-beds; F4: mediumgrained planar cross- bedded or massive sandstone lithofacies with abundant scattered pebbles ; F5: very fine to fine -grained, planar cross-bedded or parallel laminated sandstones; and F6: siltstone and claystone, variegated and mottled in places - ripple/parallel laminations and rootlets were recognised. A high frequency fining-upward (FU) transition pattern (or cycles) with a “memory function” was distilled from the sequence using the Markov Chain Stochastic Process. The fining- upward (FU) motif is interpreted as a product of braided river depositional system with well- developed flood plains and lakes behind non-cohesive river banks. The cycles indicate channel migration within a broad topographically variable alluvial plain, fed by basin-margin fans.
Highlights
Sedimentary environments generate characteristic lithofacies defined by variables such as texture, sedimentary structures, sand body geometry, and fossil contents
Olugbemiro and Nwajide (1990) interpreted these sediments as products of high–sinuosity channels based on unimodal paleocurrents and predominance of lower flow regime structures such as the planar cross-beds
The fining-upward transition pattern shown by the Facies Relationship Diagram (FRD), the dominantly very coarse-grained and pebbly nature of the deposits with its abundant vein quartz, weathered feldspar and clay chips suggest deposition of CONCLUSION Finite Markov Chain stochastic process has been used to objectively distill the actual lithofacies transition trend which was masked by several erosion truncation surfaces in the Bida Sandstone
Summary
Sedimentary environments generate characteristic lithofacies defined by variables such as texture, sedimentary structures, sand body geometry, and fossil contents. New tools in stratigraphy including semi-quantitative and quantitative techniques for data analysis were introduced to compliment descriptive stratigraphic analysis (Bokman, 1953; Schwarzacher, 1953; Krumbein and Dacey, 1969; Dacey and Krumbein, 1970; Selley, 1969; Hattori, 1973; Miall, 1973; Bernajee, 1979; Hoque and Nwajide, 1985; Gradstein, 2005; Perlmutter and Deazambuja Filho, 2005) These techniques, such as the finite markov chain analysis, time series analysis, power spectral density analysis and Kolmogorov functions introduce some objectivity and precision in analyzing “trends” and interpreting stratigraphic models and environments. Since the 1980s, research in quantitative stratigraphy and the Markov Chain Stochastic Process has waned
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