Turbidite facies and bed-thickness characteristics inferred from microresistivity (FMS) images of lower to upper Pliocene rift-basin deposits, Woodlark Basin, offshore Papua New Guinea

Awadallah, Sherif A.; Hiscott, R.N.; Bidgood, Michael D.; Crowther, T.E.

doi:10.2973/odp.proc.sr.180.166.2001

Cited by 5 publications

(11 citation statements)

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“…Scale‐invariant power‐law relationships can also describe the distribution of sedimentary bed thickness [ Rothman and Grotzinger , ; Awadallah et al ., ; Carlson and Grotzinger , ]. The equation for a power‐law relationship takes the form

N (t) = ct^(- d)

where t is bed thickness, N is the number of beds of thickness t , c is a constant, and d is a constant scaling exponent given by the slope of the plot in log( N ) versus log( t ) space.…”

Section: Introductionmentioning

confidence: 99%

“…[] suggest that the distribution of turbidite bed thickness represents a self‐organized system regulated by a complex nonlinear diffusion equation that exhibits power‐law scaling, while Beattie and Dade [] and Awadallah et al . [] favor turbidite deposition driven by the external forcing of earthquakes that follow Gutenberg‐Richter scaling — another power law.…”

Section: Introductionmentioning

confidence: 99%

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Bed thickness distributions on Mars: An orbital perspective

2013

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[1] Studies on Earth show that sedimentary bed thickness and bed thickness distributions record information about the processes controlling sediment deposition. High-resolution digital terrain models (DTMs) such as those derived from the High Resolution Imaging Science Experiment (HiRISE) now provide the opportunity to quantify bed thickness properties on Mars over several orders of magnitude, down to the submeter scale. This study uses HiRISE DTMs and visible images to measure bed thickness distributions at 10 deposits on Mars, with the aim of determining whether statistical techniques can provide useful criteria for distinguishing sedimentary depositional processes. Basic statistics, including mean thickness and range, are examined, as are histograms, cumulative frequency plots, and log-log plots. Statistical tests interrogate these deposits for thinning or thickening upward trends and the presence of normal, lognormal, and exponential distributions. Although there are challenges associated with these methods, the statistical analysis of bed thickness, coupled with morphological and mineralogical interpretations, has the potential to be a powerful tool for characterizing and classifying sedimentary rocks on Mars. In particular, bed thickness statistics are particularly well suited for examining changes in sediment supply and accommodation within Martian sedimentary sequences.

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N (t) = ct^(- d)

where t is bed thickness, N is the number of beds of thickness t , c is a constant, and d is a constant scaling exponent given by the slope of the plot in log( N ) versus log( t ) space.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bed thickness distributions on Mars: An orbital perspective

2013

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“…, 1994; Malinverno, 1997; Pirmez et al. , 1997; Chen & Hiscott, 1999; Winkler & Gawenda, 1999; Awadallah et al. , 2001; Carlson & Grotzinger, 2001; Chakraborty et al.…”

Section: Introductionmentioning

confidence: 99%

“…Power law bed thickness distributions have been linked to earthquakes with comparably distributed magnitudes (Beattie & Dade, 1996; Awadallah et al. , 2001) and to self‐organized criticality (Rothman et al.…”

Section: Introductionmentioning

confidence: 99%

Turbidite bed thickness distributions: methods and pitfalls of analysis and modelling

Sylvester

2007

Sedimentology

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Turbidite bed thickness distributions are often interpreted in terms of power laws, even when there are significant departures from a single straight line on a log–log exceedence probability plot. Alternatively, these distributions have been described by a lognormal mixture model. Statistical methods used to analyse and distinguish the two models (power law and lognormal mixture) are presented here. In addition, the shortcomings of some frequently applied techniques are discussed, using a new data set from the Tarcău Sandstone of the East Carpathians, Romania, and published data from the Marnoso‐Arenacea Formation of Italy. Log–log exceedence plots and least squares fitting by themselves are inappropriate tools for the analysis of bed thickness distributions; they must be accompanied by the assessment of other types of diagrams (cumulative probability, histogram of log‐transformed values, q–q plots) and the use of a measure of goodness‐of‐fit other than R2, such as the chi‐square or the Kolmogorov–Smirnov statistics. When interpreting data that do not follow a single straight line on a log–log exceedence plot, it is important to take into account that ‘segmented’ power laws are not simple mixtures of power law populations with arbitrary parameters. Although a simple model of flow confinement does result in segmented plots at the centre of a basin, the segmented shape of the exceedence curve breaks down as the sampling location moves away from the basin centre. The lognormal mixture model is a sedimentologically intuitive alternative to the power law distribution. The expectation–maximization algorithm can be used to estimate the parameters and thus to model lognormal bed thickness mixtures. Taking into account these observations, the bed thickness data from the Tarcău Sandstone are best described by a lognormal mixture model with two components. Compared with the Marnoso‐Arenacea Formation, in which bed thicknesses of thin beds have a larger variability than thicknesses of the thicker beds, the thinner‐bedded population of the Tarcău Sandstone has a lower variability than the thicker‐bedded population. Such differences might reflect contrasting depositional settings, such as the difference between channel levées and basin plains.

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“…Turbidite bed thickness distributions show significant departures from a straight line on a cumulative log-log plot (log-log exceedence plot) and these departures are described frequently in terms of ''segmented power laws'' (Rothman and Grotzinger, 1995;Beattie and Dade, 1996;Malinverno, 1997;Pirmez et al, 1997;Chen and Hiscott, 1999;Drummond, 1999;Awadallah et al, 2001;Sinclair and Cowie, 2003). Alternatively, these distributions have been described by a lognormal mixture model (Ricci Lucchi, 1967;Hiscott and Middleton, 1979;Drummond and Wilkinson, 1996;Murray et al, 1996;Drummond, 1999;Talling, 2001;Sylvester, 2007).…”

Section: Introductionmentioning

confidence: 95%