Geochemical Exploration Research Articles

Assessment of landscape impact on snow chemical composition in terms of mineral geochemical exploration

In areas with cold climate seasonal snowpack is an attractive sampling material for geochemical exploration of deep mineral accumulations. However, previous studies on assessment of exploration efficiency of snow cover have paid little attention to snow composition variability conditioned by local landscape structure.The layered sampling of snow sections was performed in different landscape settings of the local area. The research area is characterized by the absence of ore mineralization zones and low anthropogenic load. The physical-chemical parameters and concentration of wide range of elements were identified in snowmelt. Multi-component data was integrated by means of factor principal component analysis. Besides, additive and differential geochemical indexes were used. The classification snow models were developed using hierarchical cluster analysis. The variance of chemical elements distribution and factor models of geochemical pattern were compared before and after reduction of background fluctuation. In terms of variation coefficient values of chemical elements the area has background parameters of chemical inhomogeneity. In total variance a lateral component prevails over a vertical one. The highest concentrations of chemical elements were found in snow section of forest landscape. It is necessary to take into account the fact of geochemical background fluctuation when interpreting the data of metal assay snow survey.After the reduction of lateral geochemical background fluctuation, the factor model has better classified the chemical elements in terms of their connection with soluble and insoluble occurrence forms. The values of differential index of liquid and solid phases have a similar satisfactory vertical variability in different landscapes. The analysis of chemical elements concentration reveals snow horizons that were or are affected by thaw and percolating water. The snow bottom layer, which is sampled at geochemical exploration, is not strong influenced by elution.

Machine learning on white mica short-wave infrared (SWIR) spectral data in the Tengjia Au deposit, Jiaodong peninsula (Eastern China): A prospecting indicator for lode gold deposits

Lode gold deposits are the primary source of global gold resources and possess significant mineralization potential at depth, necessitating new strategies to locate deep concealed orebodies. The Tengjia Au deposit, a newly-discovered concealed altered rock-type lode gold deposit (50 t @ 3.89 g/t), is located within the Zhaoping metallogenic belt of the illustrious gold-rich Jiaodong peninsula in Eastern China. It is distinguished by pervasive phyllic alteration associated with gold mineralization, making it an ideal target for mineral geochemical exploration in lode gold deposits. The mineralization and alteration at Tengjia unfold across three distinct stages, delineated by mineral assemblages and textural relationships: K-feldspar-quartz (I), quartz-sericite-native gold-sulfide (Ⅱ), quartz-calcite-galena-sphalerite (Ⅲ) stages.Systematic analysis of short wavelength infrared (SWIR) spectra, coupled with petrographic observation, has unveiled an abundance of white micas (montmorillonite, muscovite, illite, paragonite, and phengite) within Stage Ⅱ at Tengjia. The Al-OH absorption feature wavelengths (Pos2200), as well as illite crystallinity (IC) values, exhibit a discernible shift towards longer wavelengths (>2204 nm) and higher values (>1.4) in the vicinity of ore deposition, which likely resulted from intense water–rock interaction between ore-forming fluid and wall rocks. Discriminant analysis of the orthogonal partial least squares method (OPLS-DA) shows that the absorption wavelengths corresponding to Water, –OH, and Al-OH effectively differentiate between ore and wall-rock samples. Additionally, analysis using the random forest algorithm (RF) demonstrates that spectral data from Tengjia white micas can reliably classify orebodies, achieving an accuracy of 83.2 %. Hence, the findings suggest that the unique SWIR spectral features of white micas offer a valuable tool for detecting the concealed Tengjia gold mineralization. This study proposes a novel approach that integrates machine learning technology with SWIR analysis for the identification of concealed lode gold deposits.

Geochemical exploration for tantalum in coltan-rich pegmatites at Bewadze-Mankoadze area of the Kibi-Winneba Belt, southern Ghana: Constraints from exploratory data analysis

The Bewadze-Mankoadze pegmatites in the Kibi-Winneba Belt of Ghana host several columbite group minerals (WGM) and wodginite group minerals (WGM) as well as other rare and radioactive elements such as uraninite and cesium. In this study, petrographic studies of rock samples from pegmatite outcrops and statistical analysis of the major and minor elements were conducted to identify the pathfinder elements of a new tantalum deposit in the area. Ten samples were obtained from each town for whole-rock geochemistry and thin sections were prepared from some of the samples taken for petrographic analysis. The petrographic analyses showed the presence of quartz, K-feldspars, plagioclase, muscovite, spodumene, albite, tourmaline, columbite group minerals, and montebrasite, which indicate that the studied samples are granitic pegmatites. The geochemical data of the 10 samples obtained from each town showed high concentrations of Cr, Cs, Rb, Sm, and Ta. The Ta concentrations ranged from 10.5 to 773 ppm with an average value of 260 ppm. Q-Q plots showed outliers and variations from the dataset's normal distribution, which were fixed by centred log-ratio transformation and demonstrated to be normal by the Kolmogorov-Smirnov test and Shapiro-Wilk test for normality. Spearman correlation revealed that Sc, Ga, Nb, and Cs showed a moderate to strong correlation with Ta. Factor analysis indicated elemental association of Ta with Cs, Zn, Nb, MgO, Sc, Ga, and V. Three (3) multi-element relationships were discovered by hierarchical cluster analysis: (1) As, La, Hf, CaO, U, Co, Pb, Ce, Ba and Na2O, (2) V, Nb, Ta, Ga, Sc, Cr, Cu, Nb and MgO, and (3) Ni, SiO2, Cs, Zn, Sm, Rb, K2O, Y, Th, Al2O3 and Fe2O3. Hence, a comparison of the results of the multivariate statistics established Sc, Ga, Nb, and Cs as the pathfinders of tantalum in the Bewadze-Mankoadze area. Geochemical anomalies involving these elements can be observed in the south-western portion of the study area, according to single and multi-element halo mapping. It is therefore recommended that exploration activities for tantalum mineralization should focus on the south-western part of the study area, where the anomalies of the pathfinder elements are located.

Whole rock lithogeochemical analysis of the Mount Read Volcanics: a new tool for geochemical exploration

The Mount Read Volcanics (MRV) of western Tasmania is a belt of Middle to Late Cambrian submarine volcanics and volcaniclastics and one of the major base and precious metal provinces within Australia. Exploration success in the MRV has been in decline in recent years due to a variety of challenges including intensely altered rocks with complex stratigraphy and lithofacies variation, and a lack of accessible outcrop. As a result, local and regional mapping of the MRV is often restricted to Group-level stratigraphy, rendering geological mapping ineffective for identifying decametre-scale mine stratigraphy. Surface rock chip and drill hole samples were collected and analysed using commercially available geochemical assays to create a regional geochemical dataset of the MRV, covering the entire province and all major stratigraphic units. From these data, a lithogeochemical methodology was developed, utilizing Ti/Nb and V/Sc systematics that effectively characterizes the samples according to composition and magmatic processes. This method is shown to be robust to biases due to alteration and lithofacies, eliminating the need for highly selective sampling or extensive data filtering. To demonstrate the utility of this method in an exploration context, data from the K-lens of the Rosebery polymetallic volcanic-hosted massive sulfide (VHMS) deposit was used as a case study. Using Ti/Nb–V/Sc systematics, a rock composition classification was developed that allowed for discrimination of the footwall, hanging wall and host stratigraphy of the Rosebery mine sequence. A geochemical fingerprint for the footwall stratigraphy to mineralization was developed, and by correlating this signature to the regional unclassified samples, geochemical analogues for the Rosebery footwall were readily identified. This approach rapidly reduces the exploration search space and provides valuable information for future mineral exploration. The methodology is applicable in other terrains; however, appropriate orientation studies are required to investigate potential biases due to lithofacies, alteration and mineralogical variability.

Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements

Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes has long been a significant subject in exploration geochemistry. The processing of high-dimensional, non-linear geochemical survey data necessitates a systematic framework to address common issues, including missing values, the closure effect, the selection of appropriate multivariate analysis methods, and anomaly detection techniques in order to detect geochemical anomalies associated with mineral occurrences. The Curnamona Province in South Australia is considered an emerging REE province with significant REE mineralization potential. In this study, we use data from this region to evaluate the performance of a novel machine learning-based framework that incorporates data pre-processing, multivariate statistical analysis, and anomaly recognition to address challenges such as missing data, noise interference, data imbalance and high non-linearity. We utilize lithogeochemical data to map potential greenfield regions of REE mineralization. The primary advantages of our framework lie in its provision of an effective random forest-based data imputation method, utilization of isometric log-ratio transformation to eliminate the closure effect, and reduction of the impact of outliers on data interpretation through robust principal component analysis. Additionally, the framework utilizes a deviation network to learn anomaly scores from complex, non-linear data under imbalanced data conditions, identifying geochemical anomalies associated with REE occurrences by leveraging prior knowledge rather than those caused by data noise or anthropogenic factors. The anomalous areas identified by this framework delineate all known REE deposits and extend to the surrounding regions. Furthermore, a close spatial coupling relationship exists between these strongly anomalous areas and the felsic granite intrusions. The comprehensive workflow for processing geochemical data proposed in this study can effectively address common challenges in the geochemical exploration of critical minerals. The identified geochemical anomalies can provide important clues for subsequent exploration.

Rare-metal halos of lithium pegmatite at the Wolfsberg deposit, Austria, and their implications for exploration

The Austroalpine Unit Pegmatite Province (AUPP) in the eastern European Alps hosts abundant Permian-aged, variably rare-metal-enriched pegmatite bodies. However, only a small number containing spodumene [LiAlSi2O6] have been discovered. Here, we characterize three drill cores from the AUPP’s largest spodumene pegmatite resource, the Wolfsberg deposit (Austria), to determine the extent of rare-metal halo formation within different host rock types generated by pegmatite-derived fluids and their applicability to spodumene pegmatite exploration. The halos in both mica schist and amphibolite host rocks, as quantified via whole-rock mass-balance calculations, are characterized by enrichments in Li, Cs, Sn, Rb and Tl, amongst others, elements which were highly mobile during initial interactions between pegmatite-derived fluids and host rocks. The concentrations of these elements within the halos can be several times that present in unaltered host rock. They are most enriched in altered host rock interbedded or at the contacts with pegmatite, and decrease with distance from pegmatite to a distance of at least 4 m in mica schist and <3 m in amphibolite. Lithium, and in mica schist, Cs, have the most consistent and furthest extending halos, with respective concentrations of >590 ppm and >40 ppm in mica schist, and >390 ppm Li in amphibolite indicating proximity to spodumene pegmatite. An ID-TIMS U-Pb titanite age of ca. 103 Ma derived from a rare-metal-enriched amphibolite within the alteration halo reflects recrystallization of the Permian protolith during the Cretaceous Alpine orogeny and indicates preservation of the original pegmatite signature through eclogite-facies metamorphism. Recognition of rare-metal halos surrounding pegmatite at Wolfsberg serves as a potential geochemical exploration tool for spodumene pegmatite within the AUPP and may be applicable to other regions.

Integrated Geophysical and Geochemical Exploration for Gold Mineralization: A Case Study in Lokoja Sheet 247, Ajaokuta L.G.A, Kogi State, Nigeria

Integrated Geophysical and Geochemical Exploration for Gold Mineralization: A Case Study in Lokoja Sheet 247, Ajaokuta L.G.A, Kogi State, Nigeria

Natural metal contents and influence of salinization in deep Canadian Shield groundwater: Base level versus mineral deposit enrichment halos

Groundwater interacts with mineral deposits, making this secondary environment a medium of choice for geochemical exploration. With increasing depth, groundwater in Precambrian shields typically matures and increases in salinity through prolonged water–rock interactions and mixing with geologically old saline fluids. As the focus of mineral exploration is gradually shifting toward deeper targets, the influence of salinity on pathfinder background levels and anomalous thresholds must be considered. We compiled a deep background database (Deep Abitibi Groundwater database; DAGW) containing groundwater samples between depths of 0 m and 690 m from barren exploration boreholes in the Abitibi subprovince, Quebec, to evaluate the natural elemental levels at depth. The DAGW provides valuable knowledge of the base levels of a wide spectrum of elements and of the changes in these base levels along with the increasing salinity in bedrock aquifers of the deep Canadian Shield. This can serve to estimate background values during hydrogeochemical exploration campaigns. The regional Abitibi base level for Zn (median of 7.5 μg/L), a key pathfinder for VMS mineralization, is unaffected by the increasing salinity levels down to depths of 690 m. Hence, down to this depth, the Zn concentrations measured may be used directly for mineral exploration, and no further correction for the effects of salinization is necessary. We conducted a case study near an undisturbed VMS-type deposit (Daniel 25) and a Zn enrichment halo was observed. Within the halo, Zn concentrations increased with depth, indicating that Zn mobility was positively affected by the geochemical conditions of the deeper aquifer. Anomalous Zn thresholds representing the proximal (50–600 m from the mineralization) and direct contact (within 50 m of the mineralization) footprints of the Daniel 25 deposit were estimated at 50 μg/L and 380 μg/L, respectively. An anomalous threshold was also estimated for Co, a secondary pathfinder, at 0.5 μg/L. Unlike Zn, several other elements, which could be used to trace mineral deposits, are affected by salinization. As a final step, we applied a correction to the Daniel 25 data, based on elemental baseline trends observed in the DAGW. The aim was to remove the effects of salinity and enhance the statistical correlations caused by the presence of the mineralized body. This correction led to the emergence of new spatial correlations, notably with Na and Sr, that are coherent with the increasing hydrothermal alteration in proximity to the ore.

A deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection

Machine learning (ML) and deep learning (DL) techniques have recently shown encouraging performance in recognizing metal-vectoring geochemical anomalies within complex Earth systems. However, the generalization of these techniques to detect subtle anomalies may be precluded due to overlooking non-stationary spatial structures and intra-pattern local dependencies contained in geochemical exploration data. Motivated by this, we conceptualize in this paper an innovative algorithm connecting a DL architecture to a spatial ML processor to account for local neighborhood information and spatial non-stationarities in support of spatially aware anomaly detection. A deep autoencoder network (DAN) is trained to abstract deep feature codings (DFCs) of multi-element input data. The encoded DFCs represent the typical performance of a nonlinear Earth system, i.e., multi-element signatures of geochemical background populations developed by different geo-processes. A local version of the random forest algorithm, geographical random forest (GRF), is then connected to the input and code layers of the DAN processor to establish nonlinear and spatially aware regressions between original geochemical signals (dependent variables) and DFCs (independent variables). After contributions of the latter on the former are determined, residuals of GRF regressions are quantified and interpreted as spatially aware anomaly scores related to mineralization. The proposed algorithm (i.e., DAN‒GRF) is implemented in the R language environment and examined in a case study with stream sediment geochemical data pertaining to the Takht-e-Soleyman district, Iran. The high-scored anomalies mapped by DAN‒GRF, compared to those by the stand-alone DAN technique, indicated a stronger spatial correlation with locations of known metal occurrences, which was statistically confirmed by success-rate curves, Student's t‒statistic method, and prediction-area plots. The findings suggested that the proposed algorithm has an enhanced capability to recognize subtle multi-element geochemical anomalies and extract reliable insights into metal exploration targeting.

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia.

The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu-Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu-Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

Exploration indication of hidden gold deposits using the fine-grained soil prospecting method and its nano-micron metal migration evidence in the alluvial soil covered area, Jiaodong

Jiaodong stands out among three global provinces boasting gold reserves surpassing 5000 tons. As geological exploration in the region continues to rise, the significance of deep and covered areas in Jiaodong for ore prospecting becomes increasingly evident. In this study, a Fine-grained soil prospecting method was employed in the Qujia hidden gold deposit and its surrounding region, situated in Yantai City, Shandong Province, and concealed by alluvial soil. The gold (Au) content exhibited a high enrichment level, and the range of Au and silver (Ag) geochemical anomalies demonstrated a robust correlation with known hidden orebodies. Furthermore, transmission electron microscopy and nano-CT were utilized to identify nano-micron metal particles and micro-fractures in the wall rock. A hereditary relationship between the nano-micron metal particles of ore-forming elements present in surface media and the deep orebody was observed. A migration model detailing the movement of deep ore-forming materials in the form of nano-micron metal particles, governed by a multi-agent relay dominated by ascending geogas flow, was established in the Qujia gold deposit in Jiaodong. These findings contribute theoretical support for the fine-grained soil prospecting method in geochemical exploration within alluvial soil covered areas.

The Trans-Crustal Evolution of Miocene Magmatism Parental to Epithermal Mineralization in the Sierra Madre del Sur, Southern Mexico

Abstract Arc-related volcanism results from the interplay of magmatic processes occurring in trans-crustal systems that consolidate over time in a given location. Trans-crustal systems comprise extensive networks of magma reservoirs and conduits at different depths, wherein melts cyclically differentiate and segregate before reaching the surface. The study of these systems provides valuable insights into crustal-scale phenomena, such as the evolution of the continental crust and the formation of metallogenic regions. In this study, we address the trans-crustal magmatic evolution of lower Miocene magmatism parental to several intermediate-sulfidation epithermal deposits in the eastern Sierra Madre del Sur igneous province, southern Mexico. Using a multi-methodological approach, we document changes over ca. 1.1 Myr in the magmatic system that fed andesitic-to-felsic volcanism in this region. We employ whole-rock REE ratios and λ parameters—which are used to quantify the shape of a REE pattern—to track the involvement of pressure-sensitive minerals in the deep-crustal magmatic evolution. The andesitic rocks consist of lava flows, porphyries, and dikes that collectively show REE patterns suggestive of control by fractionated or residual (i.e. in crustal melting) amphibole and/or clinopyroxene. In contrast, the felsic rocks consist of rhyolitic–dacitic ignimbrites, domes, and dikes that show contrasting REE patterns suggestive of control by plagioclase, clinopyroxene, amphibole, and/or garnet. The distinct pressure-sensitive mineral assemblages in the andesitic and felsic rocks indicate that the locus of deep-crustal magma evolution varied within the middle–lower crust. These magmas were episodically injected into ephemeral shallow crustal reservoirs (shortly?) before being erupted, inducing a progressive thermomechanical maturation of the middle–upper crust. Meanwhile, low degrees of crustal assimilation occurred as recorded by Mesozoic inherited zircon ages and Sr–Nd–Pb radiogenic isotopes. An extensive middle–lower crustal magma evolution has been linked to the formation of porphyry Cu deposits (i.e. ‘fertile’ magmatism). Given that intermediate-sulfidation epithermal deposits may be genetically linked with porphyry Cu deposits, the documented processes could have contributed to the formation of epithermal deposits in the region. However, magmatic fertility proxies resemble those from infertile magmas worldwide. Since these proxies have been exclusively applied to porphyry-type deposits, our results highlight the importance of developing new geochemical exploration tools applicable to a wider range of ore deposits.

Advances in Thermal Infrared Remote Sensing Technology for Geothermal Resource Detection

This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data, and geochemical analyses. Also discussed are surface temperature inversion algorithms used to delineate anomalous ground-surface temperatures. Unlike traditional geophysical and geochemical exploration methods, remote sensing technology exhibits considerable advantages in terms of convenience and coverage extent. The paper addresses the major challenges and issues associated with using TIR remote sensing technology in geothermal prospecting.

Hydrothermal alteration and geochemical proximity indicators to ore at the Metsämonttu Zn–Pb–Cu–Au–Ag deposit, Uusimaa belt, southern Finland

The Paleoproterozoic Metsämonttu Zn–Pb–Cu–Ag–Au deposit (1.5 Mt at 3.5 wt% Zn, 0.8 wt% Pb, 0.3 wt% Cu, 13.2 wt% S, 25 g/t Ag, and 1.4 g/t Au, production 1952–1974) is the largest past-producing mine in the Aijala–Orijärvi area (Orijärvi formation, Aijala member) within the Uusimaa belt, southern Finland. The Aijala member is characterized by 1.9–1.88 Ga felsic-dominated volcanic-sedimentary supracrustal rocks with intercalated sedimentary carbonates and iron formations. The area is underexplored, and little deposit-scale research has been carried out, despite the lateral continuum to the world-class ore district of Bergslagen in south-central Sweden. To better understand the Metsämonttu VMS-related alteration system, we reassessed the previously described metamorphic mineral assemblages and their protolith rock compositions by using mobile and immobile element geochemistry. This resulted in the definition of four metamorphosed alteration mineral assemblages and eight chemostratigraphic rock units. The chemostratigraphic results suggest that the lithological and/or structural setting of the Metsämonttu succession is more complex than previously considered. The stratigraphic footwall is mainly characterized by an extensive cordierite + anthophyllite ± biotite ± phlogopite + pyrite ± pyrrhotite (Mg–Fe–S) assemblage dominated by mafic rocks, designated Mafic B1 and Mafic B2, and Andesite A1. The main sulfide mineralization is hosted by tremolite + diopside ± biotite ± phlogopite ± chlorite skarn (Ca–Mg–K). A sericite-bearing muscovite + quartz ± biotite ± phlogopite + pyrite (K–Si–S) assemblage is composed of felsic to mafic protoliths (Mafic B1–B2, Andesite A1, Dacite A1, Dacite B1, Dacite C1) and extends several tens of meters into the stratigraphic hanging-wall. A quartz + pyrite ± muscovite (Si–S) assemblage represents the immediate ore-proximal alteration and is derived from rocks with a rhyolitic composition (Rhyolite A1).Limited drill core samples near Metsämonttu mineralization, along with restricted surface alteration, pose challenges in studying geochemical variations from distal to ore proximal areas and limits the ability to model the shape and size of the alteration zone. Large mass changes suggest that the alteration was hydrothermal, and due to several protolith compositions, the alteration is interpreted to be predominantly discordant to stratigraphy. A 60-m-wide alteration halo surrounds the Metsämonttu deposit. Major and trace elements, namely MgO, Na2O, K2O, SiO2, Fe2O3, Cu, Zn, Pb, S, Ag, Tl, Hg, Se, Te, Sn, Sb, Rb, and Sr, and the indices modified alteration index (MAI), Ishikawa alteration index (AI), chlorite‑carbonate-pyrite index (CCPI), S/Na2O can be used for chemical vectoring, which can assist regional or near-mine exploration. Elements MnO, Ba, Cd, Bi, As, Ni, Co, W, Ga, Mo, and indices Hashigushi index and advance argillic alteration index (AAAI) were less useful as geochemical exploration indicators.

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

AbstractRivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Zircon and Whole-Rock Trace Element Indicators of Magmatic Hydration State and Oxidation State Discriminate Copper Ore-Forming from Barren Arc Magmas

Abstract To meet surging requirements of copper for the green energy revolution, minable resources subequal to all copper production in history must be found in the next two decades. We show that trace elements in zircon and whole-rock samples that are diagnostic of unusually high-pressure magmatic differentiation and high hydration state and oxidation state of their parent silicate melt are effective for discriminating copper sulfide-ore-productive arc magmas from infertile arc magmas. Tests on our database of 5,777 zircons from 80 igneous complexes, including 2,220 zircons from ore-generative intrusions in 37 major porphyry and high-sulfidation epithermal Cu(-Au-Mo) deposits worldwide, demonstrate that our magmatic copper fertility discriminants apparently perform equally well in intraoceanic arcs, continental margin arcs, and continental collision orogens of Ordovician to Quaternary age. That performance consistency means that the tectono-magmatic controls on development of magmatic-hydrothermal copper ore-forming fertility are essentially the same in all those plate-convergence settings. The ratio Ce/√(U × Ti) in zircon is a quantitative indicator of the relative oxygen fugacity of the silicate melt and its sulfur-carrying capacity. The ratio of the europium anomaly to ytterbium in granitoid melts and zircon is an uncalibrated but empirically useful indicator of the melt’s hydration state and ability to provide chloride-complexed metals to exsolving hydrothermal fluids. Plots of (EuN/Eu*)/YbN vs. Ce/√(U × Ti) in zircon are remarkably effective for discriminating igneous complexes, arc segments, and time intervals within them that can generate and are likely to host magmatic-hydrothermal Cu(-Au-Mo) ore deposits. Arrays of cognate zircons on such plots have slopes that vary with pressure-dependent chlorinity of exsolving fluid and its efficacy in scavenging CuCl from the melt. Our zircon indicators of Cu metallogenic fertility are applicable to detrital as well as in situ zircons and can assist with ore discovery in watersheds upstream from a sediment sampling site. We formulated a composite zircon copper fertility index (ZCFI) that can be applied to each microbeam spot analysis—ZCFI = 104 (EuN/EuN*)/YbN + 5 Ce/√(Ui × Ti)—and substantially decreases the number of zircon analyses needed for reliable prospectivity assessment in a detrital grain population, thereby making this watershed-scale exploration tool cost-competitive with other methods of geochemical exploration.

Practical applications of quality assurance and quality control in mineral exploration, resource estimation and mining programmes: a review of recommended international practices

Independent quality assurance and quality control (QA/QC) programmes are required by reporting codes for publicly listed companies and are necessary to optimize data quality at all stages of the sampling, preparation and analytical processes involved in mineral exploration, resource estimation and mining grade control. QA/QC programmes should be adjusted over time to meet changing requirements in data quality at different stages of mineral resource development and exploitation. Certified reference materials are used to monitor accuracy and bias at the project laboratory relative to consensus values for the material from round-robin certification analyses. They are also used to monitor drift over time within an individual laboratory and to identify significant failures in QC at the analytical batch level caused by abrupt changes in concentration related to re-calibration of instruments or procedural changes at the laboratory. Duplicate analyses of sample material are generated at key stages of sampling and preparation to estimate the precision of data generated at each stage. Invariably, the largest source of uncertainty occurs during the initial sampling. Coarse blanks are used to monitor cross-contamination between samples or from sample preparation equipment. Furthermore, each of these QC sample types can be used to discover possible sample mix-ups. Supplementary material: An Excel spreadsheet for the calculation of the average coefficient of variation (CV AVE ) (Appendix C) is available at https://doi.org/10.6084/m9.figshare.c.7070137 Thematic collection: This article is part of the Reviews in Exploration Geochemistry collection available at: https://www.lyellcollection.org/topic/collections/reviews-in-exploration-geochemistry

Prospecting Prediction for the Yulong Metallogenic Belt in Tibet Based on Remote Sensing Alteration Information and Structural Interpretation

The Yulong porphyry copper belt in eastern Tibet is located in the middle of Tethys–Himalayan metallogenic mega-province, which is one of the three major porphyry copper metallogenic mega-provinces. The Yulong copper belt belongs to the super porphyry copper belt and represents one of the most important copper mineralization prospecting areas in China. A significant quantity of research data shows that this study area belongs to the environment of intracontinental collision and compression, with a complex geological structure, magmatic rock development and excellent metallogenic geological background. However, because this area is located in an alpine and high-altitude area, it is difficult to carry out any traditional field geological surveys, and the existing studies of both prospecting and prediction are relatively weak. This study focused on information extraction for alteration minerals in the Yulong metallogenic belt and its surroundings based on multispectral data and hyperspectral data, establishing a spectral library of alteration minerals in this area. Based on Sentinel-1A radar data and Landsat-8 OLI color synthesis data, the linear structure of the study area was interpreted. On this basis, the information extraction results relating to alteration minerals obtained from multi-source remote sensing data, linear structure interpretation results and the geochemical exploration data of the study area were superimposed to comprehensively analyze the metallogenic geological conditions and mineralization characteristics in the area, establish remote sensing prospecting indicators there and optimize the potential areas for prospecting, providing technical support for the next step of prospecting and exploration in the area.

Extracting geological and alteration information and predicting antimony ore based on multisource remote sensing data in Huangyangling, Xinjiang

Introduction: China has the world’s largest amount of antimony resources and plays a vital role in the global antimony industry. The Huangyangling area in Xinjiang belongs to the Lazhulong-Huangyangling antimony mineralization belt; this area is rich in antimony ore resources, with potential for discovery of additional ore. This high-elevation mountainous area is located in the northwestern region of the northern Tibetan Plateau, with a harsh natural environment and poor transportation access; thus, the use of traditional geological exploration methods is limited, and remote sensing technology provides unique advantages.Methods: Using Landsat 8 OLI and Gaofen-6 (GF-6) data, we extracted stratigraphic, lithologic and fault information from the study area through information enhancement processing of the images. Based on Landsat 8 OLI, Sentinel-2 and ASTER images and the use of remote sensing image preprocessing and interference information masking, the spectral characteristics of limonite, as well as hydroxyl, carbonate, and silicified alteration minerals were utilized; the characteristics included reflection or absorption in specific spectral bands. The spectral characteristics were then combined with principal component analysis (PCA) and the band ratio method to extract the alteration information for the study area.Results: The stratigraphy and boundaries between stratigraphic and lithologic units extracted in this study were defined, and numerous small faults were identified based on a comparison with 1:250,000 geologic maps. And the overall distribution trends of the indicators of alteration extracted in this study were located along fault zones; additionally, the hydroxyl and carbonate alteration anomalies matched well with known deposits (occurrences). Thus, the alteration information is a good indication of antimony ore bodies. Based on the geological interpretation, alteration information extraction and geochemical exploration data, four prospecting areas were identified.Discussion: The method of extracting geological and hydrothermal alteration information by remote sensing has practical significance because it provides the possibility for geological work and mineral exploration in difficult high-elevation environments.

Application of portable multi-component gas analyzer to mineral exploration in semi-arid steppes of northern China: A case study from the Qinjiaying Ag–Pb–Zn prospect

Soil gases, particularly sulfur gases, have not been widely used in mineral exploration due to their high mobility and reactivity. However, our team developed a portable multi-component gas analyzer (PMGA) capable of on-site and real-time measurement of H2S, SO2, CH4, and CO2 concentrations in soil gas. This study showcases the applicability and effectiveness of PMGA in the geochemical exploration of concealed ore deposits in the Qinjiaying Ag–Pb prospect in semi-arid steppes. Comparison tests between different analyzers and periods were conducted in the Qinjiaying Ag–Pb prospect. The results showed that PMGA has high stability, consistency, and reproducibility. Then, soil gas geochemical surveys were carried out by applying PMGA along eight prospecting lines, with three prospecting lines passing through the known mineralized zone and the remaining five prospecting lines focusing on the covered areas. Three composite anomalies of H2S, SO2, CH4, and CO2 were identified. The No. 1 soil gas anomaly corresponds well to the known No. I mineralized zone. The No. 3 gas anomaly is characterized by large-scale and strong multi-gas anomalies, indicating the presence of great prospecting potentials. Finally, drilling verification revealed a medium-sized Ag–Pb–Zn deposit in the No. 3 gas anomaly. The results demonstrate that PMGA is a powerful tool for mineral exploration in the study area. This successful practice provides a paradigm for applying PMGA in mineral exploration in semi-arid steppes.