On facies belts and facies mosaics: Holocene isolated platforms, South China Sea

Abstract

Although the general criteria for recognition and environmental interpretation of different carbonate facies are well‐established, a predictive understanding of the areal extent and spatial patterning of facies bodies and why they might organize into facies belts or facies mosaics is poorly constrained. To explore patterns and process dynamics of facies on isolated carbonate platforms, quantitative analysis of thematic maps derived from remote sensing images of 27 Holocene atolls of the Paracel and Spratly chains in the South China Sea explores variability within and among platforms. On these systems, most annular shelf‐margin reefs are less than 500 m wide on both chains; inboard of the reefs, reef sand aprons range up to 500 m (Spratlys) and 1000 m (Paracels) wide. Around individual platforms, Spratly Chain sand apron widths are wider to the north‐west, whereas apron widths in the Paracel Chain are more symmetrical; collectively, data indicate log‐normal width‐exceedance probability distributions. Platform‐interior patch reefs include area‐exceedance probability distributions and gap size distributions (lacunarity) consistent within chains, but distinct between the chains. To understand the processes underlying distinct distributions, simulations explored distinct growth scenarios. Results suggest that differences may represent distinct process classes: proportional growth processes with multiplicative random effects (reef sand aprons – belts), versus non‐linear, size‐proportional growth of randomly aged and distributed elements (patch reefs – mosaics). The probabilistically distinct sizes and spatial patterns of geomorphic elements within these general process classes are interpreted to represent ‘variations on themes’ related to the different impacts of tropical storms, winter cold fronts and circulation in each chain. The results highlight fundamentally different growth patterns impacting the sizes and distribution of facies belts and mosaics on isolated carbonate platforms. Because these types of bodies ultimately construct stratigraphy, the themes could be applied to understand and predict variability in the architecture of subsurface reservoir analogues.

Holocene sediment distribution in the Al Wajh platform lagoon (northern Red Sea, Saudi Arabia), a modern analogue for large rift basin carbonate platforms

“…Riegl & Purkis, 2009) and platform carbonate (e.g. Strasser & Védrine, 2009; Rankey, 2016; Dyer et al ., 2018) successions.…”

Rock fabric controls on pore evolution and porosity–permeability trends in oolitic grainstone reservoirs and reservoir analogs

“…Second, the box size: as box size increases, lacunarity will also decrease as the standard deviation decreases relative to the mean. Third, the "gappiness" of the pore network: for a given porosity, samples with clumped or isolated pores will exhibit higher lacunarity (Plotnick et al, 1996;Rankey, 2016). Conversely, rocks with homogenously distributed pore networks exhibit lower lacunarity.…”

Computation of Lacunarity from Covariance of Spatial Binary Maps

“…Applications of lacunarity indices beyond those already mentioned for GBL include description of the structure of protein gels (Dàvila and Parés 2007) (appears to use L M1 -Section C of the supplementary material) , the vacuole of lungs suffering from cancer (Borys et al 2008), microglia (Karperien et al 2013), urban settlements (Owen 2011), biofilms (Anderson et al 2015), porous rocks (Anovitz and Cole 2015), coral reefs (Rankey 2016), the effect of high-pressure treatments on rabbit sausage (Xue et al 2017), orange juice cloudiness (Aghajanzadeh et al 2017), and handwriting of patients taking antipsychotic drugs (Aznarte et al 2014) . Applications of lacunarity indices usually focus on differences given by different test set sizes (Mandelbrot 1983;Plotnick et al 1993), the average across a range of test set sizes (Chappard et al 2001;Karperien 2005), or the exponent of a power-law approximation of the lacunarity index (Allain and Cloitre 1991;Cheng 1997).…”