Integrated interpretation of pXRF data on ancient nephrite artifacts excavated from Tomb No.1 in Yuehe Town, Henan Province, China

Previous studies of ancient jade using portable X-ray fluorescence (pXRF) have mostly focused on mineral identification, alteration status and provenance determination. It is usually used as an auxiliary instrument for spectroscopic detection with finer resolution. However, there is no substitute for the efficiency and stability of pXRF in-situ non-destructive analysis, which is less affected by the test environment. The scale of the data from the pXRF analysis did not allow for a more in-depth interpretation of ancient jade in the past. In this study, pXRF has been carried out for a total of 112 pieces of nephrite artifacts unearthed from the Yuehe tomb No.1 in Nanyang City, Henan Province, Central China. Certain patterns become clearer as the size of the data increases. The coefficient of variation, cluster analysis and correlation analysis can be used to separate elements into different assemblages, revealing whether the elements are from the primary and impurity minerals of nephrite itself, from the burial microenvironment in the soil, or even from other specific sources. In addition, most of the secondary whitening occurring in the batch of nephrite are accompanied by an increase in Ca content, confirming the previously refuted theory of calcification. More importantly, the principal component analysis of the twin nephrite artifacts suggests visually indistinguishable elemental changes caused by secondary changes, which may lead to misjudgment of ancient nephrite provenance using elemental data.

Integrated Interpretation of pXRF Data on Ancient Nephrite Artifacts Excavated from Tomb No.1 in Yuehe Town, Henan Province, China

Previous studies of ancient jade using portable X-ray fluorescence (pXRF) have mostly focused on mineral identification, alteration status and provenance determination. It is usually used as an auxiliary instrument for spectroscopic detection with finer resolution. However, there is no substitute for the efficiency and stability of pXRF in-situ non-destructive analysis, which is less affected by the test environment. The scale of the data from the pXRF analysis did not allow for a more in-depth interpretation of ancient jade in the past. In this study, pXRF has been carried out for a total of 112 pieces of nephrite artifacts unearthed from the Yuehe tomb No.1 in Nanyang City, Henan Province, Central China. Certain patterns become clearer as the size of the data increases. The coefficient of variation, cluster analysis and correlation analysis can be used to separate elements into different assemblages, revealing whether the elements are from the primary and impurity minerals of nephrite itself, from the burial microenvironment in the soil, or even from other specific sources. In addition, most of the secondary whitening occurring in the batch of nephrite are accompanied by an increase in Ca content, confirming the previously refuted theory of calcification. More importantly, the principal component analysis of the twin nephrite artifacts suggests visually indistinguishable elemental changes caused by secondary changes, which may lead to misjudgment of ancient nephrite provenance using elemental data.

X-ray Fluorescence and Laser-Induced Breakdown Spectroscopy Analysis of Li-Rich Minerals in Veins from Argemela Tin Mine, Central Portugal

In this work, X-ray fluorescence (XRF) and Laser-induced breakdown spectroscopy (LIBS) analyses were applied to samples of quartz, montebrasite, and turquoise hydrothermal veins in the Argemela Tin Mine (Central Portugal). Montebrasite (LiAl(PO4)(OH,F)) is potentially the main ore mineral; with its alteration, lithium (Li) can disseminate into other minerals. A hand sample was cut and analyzed by XRF and LIBS for several elements of interest including Cu, P, Al, Si, and Li. Although XRF cannot measure Li, results from its analysis are effective for distinguishing turquoise from montebrasite. LIBS analysis complemented this study, making it possible to conclude that turquoise does not contain any significant Li in its structure. The difference in spot size between the techniques (5 mm vs. 300 µm for XRF and LIBS, respectively) resulted in a poorer performance by XRF in accurately identifying mixed minerals. A thin section was petrographically characterized and mapped using LIBS. The mapping results demonstrate the possibility of the successful identification of minerals and their alterations on a thin section. The results of XRF analysis and LIBS mapping in petrographic sections demonstrate the efficacy of these methods as tools for element and mineral identification, which can be important in exploration and mining phases, complementing more traditional techniques.

Post-deposition Weathering of Pb-rich Particles From a Pb-Zn Smelting and Refining Factory Under Semiarid Conditions

In this study, urban dust samples were collected at 1 km radius surrounding one of the largest Ag-Cd-Pb-Zn smelting and refining complex in the world (Met-Mex Peñoles), which is in operation in Torreón (North México) since 1901. Metal-rich particles in urban dust were analyzed for elemental composition, and Pb-rich particles were identified, characterized, and analyzed for mineral identification by using conventional techniques such as scanning electron microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), and X-ray powder diffraction (XRD). Pb-rich particles showed a variety of sizes and morphologies and different contents of Pb and other elements. Pb-rich particles were related to the fugitive and non-controlled emissions from Met-Mex Peñoles. Galena occurs in individual and metal-rich agglomerate particles. The presence of secondary Pb minerals (e.g., Pb carbonates, Pb sulfate, and Pb oxides) evidenced the weathering in Pb-rich particles and metal-rich agglomerates. Secondary Pb minerals are incorporated in finer particles than original sulfide minerals, and they are also more concentrated in Pb and chemically more available than galena for the environment and humans. Physical-chemical transformations on the weathered Pb-rich particles are increasing the availability and toxicity of lead in the urban dust and the potential impacts on the environment and human health.

Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

Mineral Identification Based on Deep Learning That Combines Image and Mohs Hardness

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experience of the appraisers or the various measuring instruments, and the methods are either easily influenced by appraisers’ experience or require too much work. To solve the above problems, there are studies using image recognition and intelligent algorithms to identify minerals. However, current studies cannot identify many minerals, and the accuracy is low. To increase the number of identified minerals and accuracy, we propose a method that uses both mineral photo images and the Mohs hardness in deep neural networks to identify the minerals. The experimental results showed that the method can reach 90.6% top-1 accuracy and 99.6% top-5 accuracy for 36 common minerals. An app based on the model was implemented on smartphones with no need for accessing the internet and communication signals. Tested on 73 real mineral samples, the app achieved top-1 accuracy of 89% when the mineral image and hardness are both used and 71.2% when only the mineral image is used.

Minerals of Bolshoi Semiachik geothermal fields (Central Kamchatka, Russia)

<p>The volcanic complex Bolshoi Semiachik is characterized by intensive hydrothermal activity which is expressed by presence of thermal fields with gas-steam jets (T up to ~ 140 ºC), boiling pots (T up to ~ 100 ºC), warm lakes (T up to ~ 90 ºC) and ground (T up to ~ 97 ºC) . The circulating hydrothermal solution is rich in ammonium, sulfate and locally in carbonate. To date, little is known about surface mineralogy that occurs at the geothermal fields of the volcanic complex Bolshoi Semiachik. The major geological expeditions were carried out there in the 1960`s, and there was also some additional research carried out in the 1980`s. The study of minerals occurring at the surface of geothermal fields is relevant for planetary science since similar minerals are suggested for Mars and Europa (Jupiter moon) and geochemistry since such environments of mineral formation are very specific.</p><p>In the summer 2020 the expedition of the Institute of volcanology and seismology has been organized in order to monitor thermal fields and to conduct mineral and water samples for study. Here we report the first data on mineral identification of processed samples (at about 50). At that moment, minerals have been identified by powder X-ray diffraction and electron-microprobe analyses.</p><p>The surface of Bolshoi Semiachik geothermal fields is covered by clay minerals with montmorillonite that is rich in disseminated pyrite being the most abundant. Among salt minerals the common phases are sulfates: halotrichite-, copiapite and voltaite-group minerals, alunogen, gypsum and native sulphur. The SiO<sub>2</sub> polymorphs: tridymite, cristobalite are also found at the geothermal field surface. In the zone called Central Crater chalcantite has been found in association with rhomboclase and tridymite. Some samples with zeolite-group mineral - laumontite were also found, which at the moment is identified less reliably. The central (high temperature) part of deposits around steam-gas jet is composed of dickite in association with sulphur and quartz covered by alunogen and halotrichite efflorescent. The rim (at about 1 meter from the center) is composed of smectites, marcasite and natroalunite. This zonation is likely caused by pH which is lower at the central part where the steam unloads and increases at the peripheral area around the steam-gas jet.</p><p>Acknowledgment. The study has been supported by RFBR project # 20-35-70008. We are grateful to Volcanoes of Kamchatka for letting us to conduct the field works at Bolshoi Semiachik thermal fields. Experimental works on mineral identification have been carried out using Analytical Centre of IViS and Research Park of SPbU.</p>