Abstract
An online computer program called APMdisc (Active and Passive Margin discrimination) for the discrimination of siliciclastic sediments from active and passive margin settings was written in Java along with the ZK framework. APMdisc calculates four complex discriminant functions (DFilr(A-P)M and DFmlr(A-P)M; DFilr(A-P)MT and DFmlr(A-P)MT), which represent linear combinations of log-ratios of all ten major elements (M) and ten major and six trace elements (MT), respectively. In these equations, ilr and mlr stand for isometric log-ratio and modified log-ratio transformations of the chemical composition of active and passive margin sediments, respectively. The ilr transformation provided the same results as the mlr as documented for five case studies of Quaternary sediments from California, Antarctica, Nigeria, India, and Japan. We also present 9 other case studies (for Neogene to Quaternary sediments from known tectonic settings) and 11 application studies (Tertiary to Neoproterozoic sediments and sedimentary rocks) to show the functioning of the multidimensional discrimination proposed in this paper. In most cases, the results from the APMdisc were consistent with the literature conclusions inferred from different geological and geochemical techniques. Nevertheless, APMdisc provides probability estimates for both tectonic settings, which allows the decision to be made based on the probability concept. We also added a new Robustness module to APMdisc, which allows the user to test the robustness of a sample against field changes, such as weathering and diagenesis, and laboratory analytical errors or uncertainties. This program can therefore be recommended for deciphering the margin type of older terrains. The APMdisc program can be used online by researchers for tectonic discrimination based on sediment composition. The users can process the data file at our web portal http://tlaloc.ier.unam.mx.
Highlights
The tectonic discrimination of sediments from active and passive margins has been of much interest (e.g., Dickinson and Suczek, 1979; Kirkwood et al, 2016)
In order to infer the tectonic setting of an unknown basin, researchers are traditionally using the old tectonic discrimination diagrams proposed by Bhatia (1983), Bhatia and Crook (1986), and Roser and Korsch (1986), even though ArmstrongAltrin and Verma (2005), Verma and Armstrong-Altrin (2013, 2016), Basu et al (2016), and Verma (2020), among others, have cautioned about the low efficiency of these old diagrams to discriminate tectonic environments
The results showed that the mathematical properties of the log-ratio transformations are no longer important if the multidimensional technique involves linear discriminant analysis (LDA) (Figures AII-3a-e and AII-4a-e), which is consistent with Verma (2015, 2020) who showed that all four transformations provide exactly the same results when LDA is applied to any given database
Summary
The tectonic discrimination of sediments from active and passive margins has been of much interest (e.g., Dickinson and Suczek, 1979; Kirkwood et al, 2016). New multidimensional tectonic discriminant function diagrams have been proposed, for clastic sediments (Verma and Armstrong-Altrin, 2013, 2016) but. RIVERA-GÓMEZ et al / Turkish J Earth Sci computer program for the discriminant functions of Verma and Armstrong-Altrin (2016) to discriminate active and passive margin settings has not been proposed yet, which could facilitate the use of complex discriminant functions and evaluate the robustness of the multidimensional approach. In order to fill this gap, using both isometric log-ratio (ilr; Egozcue et al, 2003) and modified log-ratio (mlr, called hybrid log-ratio hlr; Verma, 2020; more details are provided in Appendix I) transformations, we created a new program, APMdisc, to discriminate clastic sediments from active and passive margin settings. For all test case studies, the diagrams indicated the expected tectonic setting, whereas for application studies, the APMdisc program indicated an active or passive margin with generally high percent success values
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