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Sedimentary Environments of the Modern Godavari Delta: Characterisation and Statistical Discrimination Towards Computer Assisted Environment Recognition Scheme


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1 Delta Studies Institute, Andhra University, Visakhapatnam - 530 003, India
     

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Documentation of the textural and geochemical criteria of individual sub-environments of the modern Godavari delta shows that in view of prevalent shared nature of transportation, deposition and preservation processes, the textural and geochemical characteristics are not distinct enough to have these sub-environments separated by visual methods on the basis of measurements of constituent variables. Multivariate discriminant function analyses of these data show that the sub-environments of the modern deltaic system could be separated practically to the tune of 80.26% accuracy. The percentage of distinctness depends on the sensitivity of each parameter to different sub-environments and also the ability to separate between sample variations. These results have enabled the construction of a scheme for recognition of environments of unknown samples.

Keywords

Granulometry, Geochemistry, Sedimentary Environments, Statistical Discrimination.
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  • Sedimentary Environments of the Modern Godavari Delta: Characterisation and Statistical Discrimination Towards Computer Assisted Environment Recognition Scheme

Abstract Views: 183  |  PDF Views: 2

Authors

M. Ramkumar
Delta Studies Institute, Andhra University, Visakhapatnam - 530 003, India

Abstract


Documentation of the textural and geochemical criteria of individual sub-environments of the modern Godavari delta shows that in view of prevalent shared nature of transportation, deposition and preservation processes, the textural and geochemical characteristics are not distinct enough to have these sub-environments separated by visual methods on the basis of measurements of constituent variables. Multivariate discriminant function analyses of these data show that the sub-environments of the modern deltaic system could be separated practically to the tune of 80.26% accuracy. The percentage of distinctness depends on the sensitivity of each parameter to different sub-environments and also the ability to separate between sample variations. These results have enabled the construction of a scheme for recognition of environments of unknown samples.

Keywords


Granulometry, Geochemistry, Sedimentary Environments, Statistical Discrimination.