Sedimentary facies of barataria bay, Louisiana determined by multivariate statistical techniques

Dilute-acid trace-metal fraction in northeastern Gulf of Mexico sediments

“…In these analyses, the x-and y-coordinates are utilized as the spacial variables, and the values of the parameters as the z-coordinate [12][13][14]. Table 2 provides the coefficients for the trend surface equations and goodness of fit measures.…”

“…The R-mode dendrogram resulting from the cluster analysis [10,12] of the fish and sediment data representing the nine mutually occupied stations is graphed in Figure 3. Barium, Cu and Ni in the skeletal flesh of the flounder cluster at about the 60% similarity level with carbonate content and with Ba and Cr leached from the MAFLA sediments.…”

Universal decomposition model: An efficient technique for palaeoenvironmental reconstruction from grain‐size distributions

“…Since the early studies of sediment grain size (Folk & Ward, 1957; Visher, 1969; Ashley, 1978), the realization of the multimodal nature of sediments and the role of various depositional processes in causing this nature has raised a crucial issue regarding how to carry out grain‐size analysis and interpret the geological significance of the indicators obtained. Despite numerous attempts (Passega, 1957; Sahu, 1964; Feldhausen & Ali, 1975; McCave et al ., 1995) to summarize the relationship between grain size and depositional environment, and to develop traditional methods for the study of grain size in modern environments, no systematic solution has been found due to the complexity of sedimentary processes. This can be attributable to the lack of established, highly accurate methodologies for measuring grain size and reliable tools for data analysis.…”

“…(Klovan, 1966; Davis, 1970). Therefore, some studies have used multivariate analysis to explore the GSDs more thoroughly (Klovan, 1966; Davis, 1970; Allen et al ., 1972; Feldhausen & Ali, 1975; Dal Cin, 1976; Miesch, 1976; East, 1987; Forrest & Clark, 1989; Frihy et al ., 1999). These studies generally treat the particle content of various grain‐size classes in the original GSDs as individual variables and use PCA or factor analysis to reduce the data dimensions and extract several principal components (potential factors) as new variables.…”

“…These studies generally treat the particle content of various grain‐size classes in the original GSDs as individual variables and use PCA or factor analysis to reduce the data dimensions and extract several principal components (potential factors) as new variables. In this process, some closely related grain‐size classes are combined into new parameters, which are then used to analyse and discriminate between different depositional environments (Klovan, 1966; Davis, 1970; Dal Cin, 1976; Frihy et al ., 1999) or perform clustering analysis using certain algorithms (Feldhausen & Ali, 1975; East, 1987).…”