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Allocca, Vincenzo
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- PublicationOpen AccessHierarchical clustering and compositional data analysis for interpreting groundwater hydrogeochemistry: The application to Campi Flegrei volcanic aquifer (south Italy)(2022)
; ; ; ; ; ; ; ; ; ; ; ; ; ;; ; ; ; ; ; ; Comprehensive hydrogeochemical studies have been conducted in the Campi Flegrei volcanic aquifer since late 20th century due to the volcanic unrest. In the last decade, groundwater samples were grouped based on the dominant anion species (i.e. bicarbonate, sulfate and chloride) to explain the general hydrogeochemical processes. In this article, 44 groundwater samples are collected from Campi Flegrei aquifer to geochemically and spatially capture the main characteristics of the groundwater body. The hierarchical clustering algorithm is then performed on proportion of bicarbonate, sulfate and chloride, and the optimum number of clusters are determined regarding the results of deep hydrogeochemical investigations published in the past. The collected samples are categorized in the following groups: (1) bicarbonate-rich groundwater; (2) chlorine-rich groundwater; (3) sulfate-rich groundwater; and (4) mixed groundwater. The first group (As = 158.2 ± 169 μg/l, electric conductivity = 1,732.1 ± 1,086 μS/cm and temperature = 25.6 ± 8 ◦C) is mainly derived from poor arsenic meteoric water, but there is significant thermal/seawater contribution in the second one (As = 1,457.8 ± 2,210 μg/l, electric conductivity = 20,118.3 ± 11,139 μS/cm and temperature = 37.1 ± 20 ◦C). Interaction of the bicarbonate-rich groundwater and hydrothermal vapors gives rise to the sulfate-rich groundwater (As = 847.2 ± 679 μg/l, electric conductivity = 3,940.0 ± 540 μS/cm and temperature = 82.8 ± 3 ◦C) around Solfatara volcano. The mixed groundwater (As = 451.4 ± 388 μg/l, electric conductivity = 4,482.9 ± 4,027 μS/cm and temperature = 37.1 ± 16 ◦C) is observed where the three main groundwater groups undergo a mixing process, depending on the hydrogeology of the volcanic aquifer. Contrary to the bicarbonate- and sulfate-rich groundwater, the chlorine-rich and mixed groundwater generally occurs at low piezometric levels (approximately <1 m above sea level) near the coastline. The hierarchical cluster analysis provides more information about the volcanic aquifer, particularly when compositional data analysis is applied to study hydrogeochemistry of the homogeneous groundwater groups and to uncover the relationships between variables. Addressing compositional nature of data is recommended in the future studies for developing new tools that help deeper understanding of groundwater evolution in volcanic aquifers and identifying promising precursors of volcanic eruption.595 271 - PublicationOpen AccessExploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging(2023-04-24)
; ; ; ; ; ; ; ; ; ; ;Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for numerous volcanological, geothermal and engineering applications. Their rapid origin, in fact, leads to complex textures, where pores are dispersed in fine, heterogeneous and lithified matrices. We propose a framework to optimize their investigation and face innovative 3D/4D imaging challenges. A 3D multiscale study of a tuff was performed through X-ray microtomography and image-based simulations, finding that accurate characterizations of microstructure and petrophysical properties require high-resolution scans (≤ 4 μm/px). However, high-resolution imaging of large samples may need long times and hard X-rays, covering small rock volumes. To deal with these limitations, we implemented 2D/3D convolutional neural network and generative adversarial network-based super-resolution approaches. They can improve the quality of low-resolution scans, learning mapping functions from low-resolution to high-resolution images. This is one of the first efforts to apply deep learning-based super-resolution to unconventional non-sedimentary digital rocks and real scans. Our findings suggest that these approaches, and mainly 2D U-Net and pix2pix networks trained on paired data, can strongly facilitate high-resolution imaging of large microporous (volcanic) rocks.534 12