Revista de Geografía y Desastres Naturales

Revista de Geografía y Desastres Naturales
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ISSN: 2167-0587

abstracto

Predicción de la hidrodinámica costera en cuarentena en sedimentos con utilidad de características texturales y susceptibilidad magnética de la costa de Jagatsinghpur, India

Vasudevan Sivaprakasam, Sathiyamoorthy Gunasekaran, Selvaganapathi Rajendran, Balamurugan Palani, Baranidharan Sathyanarayanan

This current research aims to perceive the depositional energy environment of Jagatsinghpur coastal area sediments with the outcome of textural aspects of particle size dissemination and Magnetic Susceptibility (MS). In the study, 36 sediment samples were recovered from 12 locations, with 3 points such as Low Tide (LT), High Tide (HT), and Berm (BM), at every 5 km interval. To observe outcomes from different parameters such as Mean (MQ), Standard Deviation (SD), Skewness (SK) and Kurtosis (KU), bivariate plots, and MS methods were adopted. The MQ and SD of LT, HT, and BM infer medium to fine sand with a moderately to well-sorted nature, while the SK and KU of LT, HT, and BM infer extremely coarse skewed to fine skewed with platykurtic to extreme leptokurtic nature. Textural parameters show the southern part of the study area experienced a low energy condition. The energy process plot indicates sediments fall in the inner shelf and river environment. The CM (C=One percentile in micron, M= Median in micron) plot expressed the sediment samples' reactions as graded suspension and rolling mechanisms. The MS of low frequency, the higher quantity of magnetic content of 3 zones: Low Tide (LT), High Tide (HT), and Berm (BM), has been 62.57 × 10-8 m3kg-3, 72.83 × 10-8 m3kg-3, and 454.63 × 10-8 m3kg-3. The Pearson’s Correlation signifies the +ve relationship between Berm low frequency and Berm Mean, low tide means (r2=0.57, r2=0.40) signifies the multi- source of sediment deposition. From the output, the northern part of the study infers a high-energy environment.

Descargo de responsabilidad: este resumen se tradujo utilizando herramientas de inteligencia artificial y aún no ha sido revisado ni verificado.
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