Client-Server Application for Automated Estimation of Bottom Sediment Composition in the Fraction &gt;0.1 mm from Microphotography Using Modern Deep Learning Methods

Golikov, V.A.; Krinitskiy, M.A.; Borisov, D.G.; Riazanova, E.I.

Авторы: Golikov V.A., Krinitskiy M.A., Borisov D.G., Riazanova E.I.
Журнал: Moscow University Physics Bulletin
Том: 78
Номер: S1
Год издания: 2023
Издательство: Allerton Press
Местоположение издательства: New York, N.Y., United States
Первая страница: S104
Последняя страница: S114
DOI: 10.3103/s0027134923070093
Аннотация: Sediments covering the sea-floor are considered the largest natural archive of paleoclimatic and paleoceanographic information. The study of sediment composition in the coarse fraction (variety of mineral and biogenic grains over 0.063 mm in size) is widely used to pry climate clues out of the sediment record. At present, specialists use a binocular microscope to visually classify grains from a small portion of a sediment sample. This time-consuming technique requires the observer to possess geological expertise. In the previous work, we proposed an algorithm for automatic unsupervised detection of particles and their clustering. In the current work, we present qualitative improvements in the algorithm which now employs the state-of-the-art clustering method, SPICE. This method made it possible to eliminate overclustering and limit the number of clusters to three, making the results more suitable for interpretation. We trained the algorithm and interpreted the obtained results. The resulting model can be used as a classifier, enabling the calculation of particle distribution by clusters, analysis of grain-size distribution, and comparison of these results with those obtained through other lithological analyses. According to the mean deviation from the results of the X-ray diffraction analysis, our method outperforms visual description techniques. Lastly, we developed and deployed an application that automates server-side calculations and allows users to evaluate the of clustering and grain-size measurements.
Добавил в систему: Криницкий Михаил Алексеевич

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Client-Server Application for Automated Estimation of Bottom Sediment Composition in the Fraction &gt;0.1 mm from Microphotography Using Modern Deep Learning Methodsстатья

Client-Server Application for Automated Estimation of Bottom Sediment Composition in the Fraction >0.1 mm from Microphotography Using Modern Deep Learning Methodsстатья