A sensors-based profile heterogeneity index for soil characterization

Souza, Arnaldo Barros e; Demattê, José A.M.; Bellinaso, Henrique; Mello, Danilo César de; Lisboa, Caroline Jardim da Silva; Mello, Fellipe Alcântara de Oliveira; Marques, Karina Patrícia Prazeres; Resende, M.; Reis, Josimar Vieira dos; Mancini, Marcelo; Silva, Sérgio Henrique Godinho; Curi, Nilton

doi:10.1016/j.catena.2021.105670

Cited by 5 publications

(6 citation statements)

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“…Also, 40 K is not incorporated in secondary minerals, so it is leached, whereas Th composes highly resistant minerals, such as ilmenite and zircon. Consequently, Th content increases with weathering (Dickson and Scott, 1997;Wilford et al, 1997b;Mello et al, 2021;Mello et al, 2022;Mello et al, 2023). In our case, gamma radiation and radionuclide contents are much more associated with the mineralogy of the substrate than land surface processes, due to the low intensity with which chemical weathering and pedogenesis operate in MA.…”

Section: Radionuclides and κ Contents On Lithological Compartments An...mentioning

confidence: 51%

“…The radiometric data (gamma-ray spectrometry) correspond to the acquisition of radionuclide contents eU, eTh and 40 K, quantified in ppm (eU and eTh) and % for 40 K. Magnetic susceptibility is given in 10-3 SI units and the sensor is able to detect mean κ values to a depth of 2 cm below the rock outcrops surface (Sales, 2021). Detailed calibration methods, method of collection and interpretation of results can be found in (Mello et al, 2022;Mello et al, 2021Mello et al, , 2022.…”

Section: Geophysical Survey Radiometric and κ Characterizationmentioning

confidence: 99%

“…Recently, proximal geophysical sensors have been used for lithological-pedological characterization, in other parts of the world, where the provided information is used to understand the lithosphere-pedosphere interaction and dynamics in a geoenvironmental context (Bastos et al, 2023;Vingiani et al, 2022). Geophysical surveys utilizing these sensors enabled the gathering of field information swiftly and efficiently, eliminating the necessity of sample collection for laboratory analysis Mello et al, 2020;Mello et al, 2021;Mello et al, 2023;Mello et al, 2023). Among the geophysical techniques used for lithological surveys, radiometric (gamma-ray spectrometry) and magnetic (magnetic susceptibility) stand out.…”

Section: Introductionmentioning

confidence: 99%

“…The quantities of these radionuclides are influenced by various factors such as lithological substrate and surface processes (mainly weathering, pedogenesis, pedogeomorphological, and periglacial) (Navas et al, 2018). Past studies undertaken by Dickson and Scott, (1997); Wilford and Minty, (2006) and Mello et al, (2021) have shown that the radionuclide contents are dependent on these factors. Proximal gamma-ray spectrometry, as highlighted by (Ford et al, 2008), offers a precise method for determining concentrations of specific radioactive elements and mapping their sources accurately in soil, bedrock, and surface geological exploration.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies used gamma-ray spectrometry mapping to delineate lithological maps (Arivazhagan et al, 2022;Loiseau et al, 2020;Shebl et al, 2021) and magnetic susceptibility (Bressan et al, 2020;Costa et al, 2019;Harris and Grunsky, 2015). In addition, recently Mello et al, (2022), Mello et al, (2020), Mello et al, (2021), Mello et al, (2022), Mello et al, (2022) have successfully used machine learning algorithms combined with data from multiple field geophysical equipment to map geophysical variables and understand tropical soils, lithology and landscapes, obtaining satisfactory results in mapping and understanding these landscapes using modeling via machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning to proximal gamma-ray and magnetic susceptibility surveys in the Maritime Antarctic: assessing the influence of periglacial processes and landforms

Mello,

Baldi,

Moquedace

et al. 2024

Preprint

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Abstract. Maritime Antarctica (M.A.) contains the most extensive and diverse lithological exposure compared to the entire continent. This lithological substrate reveals a rich history encompassing lithological, pedogeomorphological, and glaciological aspects of M.A., all influenced by periglacial processes. Although geophysical surveys can detect and provide valuable information to understand Antarctic lithologies and their history, such surveys are scarce on this continent and, in practice, almost non-existent. In this sense, we conducted a pioneering and comprehensive gamma-spectrometric (natural radioactivity) and magnetic susceptibility (κ) survey on various igneous rocks. The main objective was to create ternary gamma-ray and κ maps using machine learning algorithms, terrain attributes, and a nested-leave-one-out cross-validation method. Additionally, we investigated the relationship between the distribution of natural radioactivity and κ to gain insights into pedogeomorphological and periglacial processes and dynamics. For that, we used proximal gamma-spectrometric and κ data in different lithological substrates associated to terrain attributes. The geophysical variables were collected in the field from various lithological substrates, by use field portable equipment. The geophysical variables were collected in the field from various lithological substrates using portable equipment. These variables, combined with relief data and lithology, served as input data for modeling to predict and spatially map the content of radionuclides and κ by random forest algorithm (RF). In addition, we use nested-LOOCV as a form of external validation in a geophysical data with a small number of samples, and the error maps as evaluation of results. The RF algorithm successfully generated detailed maps of gamma-spectrometric and κ variables. The distribution of radionuclides and ferrimagnetic minerals was influenced by morphometric variables. Nested-LOOCV method evaluated algorithm performance accurately with limited samples, generating robust mean maps. The highest thorium levels were observed in elevated, flat, and west beach areas, where detrital materials from periglacial erosion came through fluvioglacial channels. Lithology and pedogeomorphological processes-controlled thorium contents. Steeper areas formed a ring with the highest uranium contents, influenced by lithology and geomorphological-periglacial processes (rock cryoclasty, periglacial erosion, and heterogeneous deposition). Felsic rocks and areas less affected by periglacial erosion had the highest potassium levels, while regions with sulfurization-affected pyritized-andesites near fluvioglacial channels showed the lowest potassium contents. Lithology and pedogeochemical processes governed potassium levels. The κ values showed no distinct distribution pattern. Pyritized-andesite areas had the highest levels due to sulfurization and associated pyrrhotite, promoting iron release. Conversely, Cryosol areas, experiencing freezing and thawing activity, had the lowest κ values due to limited ferrimagnetic mineral formation. Lithology and pedological-periglacial processes in Cryosols played a significant role in controlling κ values. In regions characterized by diverse terrain attributes and abundant active and intense periglacial processes, the spatial distribution of geophysical variables does not reliably reflect the actual lithological composition of the substrate. The complex interplay of various periglacial processes in the area, along with the morphometric features of the landscape, leads to the redistribution, mixing, and homogenization of surface materials, contributing to the inaccuracies in the predicted-spatialized geophysical variables.

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