Geochemical Anomalies Research Articles

Thallium enrichment mechanism and geological significance of the Xiangquan thallium deposit in Anhui Province, China

The discovery of the independent thallium (Tl) deposit in Xiangquan, Anhui Province, provides an example for the study of the enrichment and mineralization of the highly dispersed rare metal element Tl. In this paper, a range of methods are used to characterize the enrichment, depletion, migration and occurrence state of Tl in different types of rocks and ores: lithogeochemistry; electron microprobe and principal component analysis; and exploring the significance of geochemical anomalies on the geological indication of Tl deposit formation. The results show that Tl is mainly distributed in silicified rocks of the Ordovician Lunshan Formation, and its average content is 307.08 μ g g –1 . The contents of As, Sb, S, Fe 2 O 3 and Tl in wall rocks, altered rocks and ores are significantly correlated, indicating a close relationship with Tl accumulation. The geochemical characteristics confirm that pyrite in the study area originates from submarine hot-water deposition. The main carrier of Tl is pyrite of hydrothermal sedimentary origin, in which Tl occurs as isomorphs in fine grains of pyrite. Tl mineralization may be accompanied by silicification, and the metallogenic environment is in a relative oxidation state. The mineralization process results in the geochemical differentiation of elements. Under the influence of tectonic and low-temperature hydrothermal activities, ore-forming elements migrate to structurally favourable conditions for mineralization to occur. This study provides a basis for further understanding the mechanisms of co-enrichment and separation of Tl and guidance for Tl mineral exploration.

Analysis of spatial structure and singularity of microbial biogeochemical anomalies in the Taiwan Strait Basin

The abundance of specific hydrocarbon-oxidizing bacteria (MV value) in surface soil is closely linked to the concentration of thermogenic light hydrocarbons found in the underlying layers. The identification of these bacteria using specific detection techniques offers unique insights into microbiological anomalies associated with oil and gas, underscoring the significance of delineating microbiological geochemical anomalies in the exploration process. Accurate determination of the anomaly range is crucial for forecasting potential oil and gas reservoirs. In this study, a variety of fractal and geological statistical methods were employed to analyze microbiological geochemical data from the Taiwan Strait Basin. The multifractal approach allows for the simultaneous assessment of spatial autocorrelation and singularity in geochemical fields, with the fractal distribution reflecting the localized enrichment and depletion patterns of microbiological geochemical elements in rocks and other substrates. Geological statistical methods, such as the variation function, enable the assessment of spatial autocorrelation in geochemical and geophysical fields. The results indicate that microbiological anomalies in the Jiulongjiang Depression are mainly concentrated in the southwestern part of the study area, with some anomalies also observed in the central region. Conversely, microbiological anomalies in the Jinjiang Depression are predominantly located in the southern portion of the study area. Microbiological anomalies in the study area (Jinjiang Depression and Jiulongjiang Depression) exhibit a northeast-southwest strip distribution, aligning with the trend of the depressions and faults, suggesting that faults could influence the distribution of oil and gas resources in the region. Based on microbiological statistical analyses, the Jiulongjiang Depression shows better potential for oil and gas prospects compared to the Jinjiang Depression, guiding future research efforts in the Jiulongjiang Depression.

Combining categorical boosting and Shapley additive explanations for building an interpretable ensemble classifier for identifying mineralization-related geochemical anomalies

The vast majority of shallow and deep learning techniques used to identify mineralization-related geochemical anomalies are black-box algorithms that lack the ability to elucidate the individual contributions of each element towards the model predictions. In addition, most of the anomaly identification models established by both shallow and deep learning algorithms lack robustness. Establishing interpretable and robust machine learning models is a challenge in applying machine learning techniques to geochemical anomaly identification. To this end, the categorical boosting (CatBoost) algorithm was employed to build a robust ensemble classifier to identify mineralization-related anomalies from the 1:50,000 geochemical reconnaissance data (stream sediment survey) in the Yeniugou area of Xinjiang (China). The receiver operating characteristic curve (ROC) and precision-recall (P-R) curve of the ensemble model were plotted, and the area under the ROC curve (AUC) as well as the area under the P-R curve (AUPRC) of the ensemble model were calculated to measure the performance of the ensemble model. The ROC curve of the ensemble model approximates that of the perfect classification model. The P-R curve of the ensemble model is close to the upper right corner of the P-R space. The AUC and AUPRC values of the ensemble model reaches 0.9981 and 0.7816, respectively. The identified polymetallic mineralization-related geochemical anomalies account for 3% of the whole exploration area, correctly identifying all known polymetallic deposits. To enhance the interpretability of the CatBoost model, the Shapley additive explanations (SHAP) tool was adopted to graphically interpret the predictions of the ensemble model. The graphic interpretation shows that the importance order of the 14 elements is Ni-Au-Ag-Sn-As-Cr-Zn-Cu-Pb-Sb-W-Bi-Mo-Co. Cu and Ni are most likely metallogenic elements of the study area. Cu interacts with Ni, Ag, As, Sn, Cr, Zn, Pb, Sb, W, Bi, and Co; and Ni interacts with Au, Sn, As, Zn, Cu, W, Bi, and Co. Two polymetallic prospective areas were delineated in the study area. One is Cu-Ni-polymetallic mineralization prospective area, and the other is Ni-polymetallic mineralization prospective area. It can be concluded that the combination of CatBoost and SHAP is an effective way to construct an interpretable ensemble model with high-performance and robustness in identifying mineralization-related geochemical anomalies.

A hybrid framework for Detection of Multivariate porphyry Cu Signatures and Anomaly Enhancement: Incorporation of SFA, GMPI, and Grey Wolf Optimization

Geochemical data from stream sediment are commonly employed for regional scale mineral exploration, as they can be utilized to detect geochemical anomalies associated with mineralization processes. However, recognizing efficient multielement geochemical signatures related to target mineralization using stream sediment geochemical data can be challenging due to the intricate nature of mineralization events. To achieve this goal, this article proposes a combination of statistics, logistic functions and machine learning techniques. Initially, the staged factor analysis (SFA) approach was applied to the data to determine the geochemical footprints associated with porphyry copper mineralization in the Varzaghan area. Subsequently, the geochemical mineralization probability index (GMPI) was utilized to facilitate appropriate separation, more precise identification, and the transfer of data into a fuzzy space. By using a combination of SFA and GMPI, it was feasible to enhance the detection of geochemical halos and concealed anomalies. Ultimately, the output of these two approaches was subjected to an improved clustering technique known as GWO-K-means to detect geochemical anomalies. The findings indicated that there is a strong relationship between known mineral occurrences (KMOs) and the identified anomalous areas. The success rate curve demonstrated that the optimized model outperformed the K-means model, proposing that optimization can boost the accuracy in identifying geochemical targets. Overall, the outcomes of this study can be beneficial for proper planning of future exploration activities.

Deep embedded clustering: Delineating multivariate geochemical anomalies in the Feizabad region

The objective of this research was to identify geochemical anomalies associated with mineralization in the Feizabad region using deep embedded clustering (DEC). Two approaches were employed: using all geochemical elements (non-selective) and using factor scores (selective) to construct input data. Results showed that the non-selective approach was highly successful in identifying anomalies, not only confirming known mineral occurrences (KMO) but also introducing new exploration targets. Additionally, model evaluation using the success rate curve (SRC) confirmed its high predictive power. This method is particularly useful in green field. The DEC model, by combining deep neural networks (DNN) and clustering algorithms, reveals complex patterns in geochemical data. This algorithm increases clustering accuracy through automatic data clustering, gradual input of large data, and compatibility with imbalanced data. Furthermore, by simultaneously integrating feature learning and clustering, it provides higher optimization and accuracy. The anomalies identified by the DEC model are consistent with granodiorite units in the region and may indicate the potential for mineralization in the study area. Additionally, the identified targets are associated with granitoids, hydrothermal alteration zones, and intrusive rocks.

Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine

Machine learning, as an increasingly prominent method in recent years, has introduced new methodologies and perspectives for extracting geological alteration information. To enhance the accuracy of remote-sensing-alteration mineral information, this study focuses on the extraction of alteration information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. Spectral Angle Mapper (SAM) technology was used to extract sample data from hyperspectral imagery, which were then used to train the machine learning model for alteration information extraction. The experimental results show that, when compared to the Random Forest and the Support Vector Machine algorithms, the Kernel-Based Extreme Learning Machine algorithm achieved the highest accuracy and the best effect in the extraction results. It closely matches the known mineral points and geochemical anomalies in the area, confirming that the method has a clear advantage in the extraction of hyperspectral alteration information.

Masked Autoregressive Flow for Geochemical Anomaly Detection with Application to Li–Cs–Ta Pegmatites Exploration of the Superior Craton, Canada

Masked Autoregressive Flow for Geochemical Anomaly Detection with Application to Li–Cs–Ta Pegmatites Exploration of the Superior Craton, Canada

Recognition of multi-element geochemical anomalies related to Pb–Zn mineralization applying upgraded support vector machine in the Varcheh district of Iran

Recognition of multi-element geochemical anomalies related to Pb–Zn mineralization applying upgraded support vector machine in the Varcheh district of Iran

Unsupervised Machine Learning-Based Singularity Models: A Case Study of the Taiwan Strait Basin

The identification of geochemical anomalies in oil and gas indicators is a fundamental task in oil and gas exploration, as the process of oil and gas accumulation is a low probability event. Machine learning algorithms for anomaly detection are applicable to the identification of oil and gas geochemical anomalies related to oil and gas accumulation. However, when using oil and gas indicators for anomaly detection, the diversity of these indicators often leads to the influence of the indicator redundancy on the identification of such features. Therefore, it is particularly important to select appropriate oil and gas indicators for anomaly detection. In this study, a hybrid model combining unsupervised machine learning methods and singularity analysis methods was used to evaluate oil and gas indicator anomalies using geochemical data from the Taiwan Strait Basin. The models used in this study include the singularity index model (LSP), the principal component model combined with the singularity index model (PCA and LSP), and the cluster analysis combined with the principal component model and the singularity index model (CLA-PCA-LSP). PCA models can reduce the dimensions of the data and retain as much information as possible. CLA divides data samples into different groups, so that samples within the same group are more similar and samples between different groups are less similar. LSP is mainly used for measuring the setting and singular degree of local anomalies in multi-scale geochemistry, geophysics, and other types of local anomalies, and it has a unique advantage in extracting low and weak anomalies and nonlinear characteristics. The results of the study show that the results obtained using the CLA-PCA-LSP hybrid model are very similar to those obtained by performing PCA on the entire index and then calculating the singularity index. This also verifies that, for the study areas of the Jiulongjiang Depression and Jinjiang Depression, we can select oil and gas indicators that are favorable for exploration analysis, without including all indicators in the analysis scope, thereby improving the computational efficiency. The application of a singularity analysis method and generalized self-similarity principle in extracting the geochemical information of oil and gas indicators in the Taiwan Strait Basin highlights key technologies such as the identification of weak anomalies, decomposition of composite anomalies, and integration of spatial information. The combination anomalies delineated by the singularity analysis method and S-A method not only reflect the spatial relationship with known oil and gas reservoir distribution, but also show the multiple combination anomalies in unknown areas, providing favorable guidance for the next exploration direction in the Taiwan Strait Basin.

Hidden deposit exploration using the tectono-geochemistry method in the western Xicheng ore field, China

Geochemical anomalies involve complex geological and geochemical processes. Integrating metallogenic processes into the interpretation of geochemical data can promote mineral exploration. In this study, 3080 subsamples were collected from the western Xicheng ore field and combined into 1312 composite samples using the tectono-geochemistry method. Nineteen elements were analyzed for each composite sample. Factor analysis based on the CLR-transformed data yielded four factors, including the Ag–Sb–Hg–Pb–Au–(B–Ba) association of F1, Zn–Cd–Pb association of F2, Bi–Sn–(Au–As) association of F3, and W–Sn–(Cu) association of F4. Thresholds of each factor were obtained using the concentration–number (C–N) fractal model. Six targets were delineated based on the factor anomaly maps, and one Pb–Zn and two Au deposits were discovered in Targets I and II, respectively. These discoveries and the good spatial correspondence between known deposits and anomalies provide compelling evidence for the effectiveness of the tectono-geochemistry method in the study area. More importantly, a model for the genetic relationship between geochemical anomalies and metallogenesis was constructed. The late tectonic-magmatic-hydrothermal transformation dominated the geochemical pattern in the study area. The degree of interaction between the Au-rich magmatic-hydrothermal fluids and SEDEX-style Pb–Zn mineralizations yielded leakage halos with various elemental assemblages. In addition, W–Sn anomalies may serve as auxiliary exploration indicators for Au mineralizations.

Microplastic and heavy metal contamination in sediments of the high-altitude Nundkol Lake of northwestern Himalayas-Kashmir

This study investigates heavy metal and microplastic contamination in the sediment of Nundkol Lake, a high-altitude glacial lake in the northwestern Himalayas. Sediment analysis revealed heavy metal concentrations significantly exceeding background levels, with average concentrations of cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), zinc (Zn), and chromium (Cr) recorded at 40.991 ppm, 54.434 ppm, 87,843.59 ppm, 1544.566 ppm, 83.288 ppm, 16.312 ppm, 104.571 ppm, and 106.514 ppm, respectively. Notably, the highest levels of Cu, Fe, Mn, and Pb were observed in sample GL2, indicating potential local geochemical anomalies. The trace elements were quantified using a Graphite Furnace - Atomic Absorption Spectrophotometer (Perkin Elmer-PinAAcle 900AA) for high precision.Microplastic analysis revealed an average abundance of 76±12.08 items/kg dry weight, predominantly fibers. The microplastics were mostly red (55.26 %), blue (36.84 %), orange (5.26 %), and green (2.63 %). Raman spectroscopy identified Polyamides (PA) as the predominant type (86.5 %), with the remainder being Polyethylene (PE) and Polypropylene (PP). The results suggest tourism, fishing, and trekking activities as major contributors to microplastic presence. This study uniquely combines the assessment of heavy metal and microplastic contamination in a high-altitude lake, providing a comprehensive view of pollutant dynamics. The findings highlight the need for tailored mitigation strategies and comprehensive monitoring programs to protect this fragile region. Applications include informing environmental policy, guiding pollution mitigation efforts, and promoting sustainable tourism practices. This study offers valuable insights into the environmental health of high-altitude lakes and underscores the need for efforts to mitigate anthropogenic impacts.

Geochemical anomalies of hydrocarbon gases in bottom sediments of eostructures of the Laptev-Siberian transition zone of the East Arctic shelf

Geochemical anomalies of hydrocarbon gases in bottom sediments of eostructures of the Laptev-Siberian transition zone of the East Arctic shelf

Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification

Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification

As and Pb Presence within the Meoqui-Delicias Aquifer, Chihuahua, Mexico

This study aimed to determine the amount of As and Pb in the water in the Meoqui-Delicias’ aquifer and their spatiotemporal dynamics. Twenty-one water sampling points were selected. Seventeen samples were from wells and four were from surface water; two were used for human consumption and the rest for agricultural use. The samples were taken from May 2019 to January 2020 in four sampling events, one for each climatological season of the year. The studied geochemical anomalies seem to be linked to the nature and mechanism of volcanic emplacement. Several samples exhibited high concentrations of arsenic ranging from 1.20 to 156.54 ppb, unlike lead, with low values being the maximum value of 26.32 ppb. These elements (As and Pb) are in the water in Naica, part of the mining district where tons of Au, Ag, Pb, Cu, and Zn were obtained. From a geographical standpoint, it is impossible to establish that these elements are related, even though these elements (As and Pb) are present in the water in Naica, a mining zone where tons of Au and Ag were historically mined.

Advanced soil-gas geochemical exploration methods for orogenic gold deposits: A case study of Chalapu deposit, Xizang

Orogenic gold deposits play a significant role in the world’s gold reserves, and their distinctive characteristics pose challenges for exploration. The low sulfide content and narrow, fault-controlled sulfide-quartz veins hinder the use of traditional mineral exploration methods to locate deeply buried ore bodies, particularly in regolith-covered areas. Enhanced methods are required to detect deposits at greater depths. Soil gas composition shows promising potential for mineral exploration by conveying information about sulfur-rich and carbon-rich gases related to deep-seated mineralization into surface soils. However, the prospects of this method for gold deposits remain uncertain. In this study, a novel method was introduced to perform an integrated H2S, SO2, CH4, and CO2 soil-gas geochemical survey on gold ore bodies of different scales at the Chalapu deposit in Tibet. The results unveiled notable gas geochemical anomalies of H2S, SO2, CH4, and CO2 above the multi-layered or thick gold ore bodies. For instance, on exploration line 16 at point A1607, H2S reached the regional maximum value of 1.064 ppm, coinciding with the surface exposure of the thickest gold ore body, measuring 19.81 m. While the presence of CH4 and CO2 anomalies alone may not signify potential mineralized zones, the combined presence of anomalous H2S and SO2 values along with CH4 and CO2 concentrations appears to be effective in identifying mineralization. These combinations of geochemical anomalies not only uncover concealed ore bodies, but also delineate the trend and extension direction in strike and dip of ore bodies. Gas intensity may provide insights into the scale of the ore bodies, with stronger gas anomalies observed in areas with thicker ore bodies at similar depths. Thus, the study reveals that soil-gas exploration shows great promise as an exploration technique for orogenic gold deposits, especially in areas with regolith cover hindering traditional methods from detecting mineralized zones for gold exploration.

Earth Observation Data Utilisation for Rock Phosphate Exploration in Jhabua, Madhya Pradesh, India

ABSTRACT In this study, we processed Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) data using partial subpixel mapping methods for delineating surface occurrences of fertilizer mineral i.e., phosphorite in dolomite and carbonate-bearing chert in the Aravalli Supergroup. We applied feature-oriented principal component analysis to derive an image composite for delineating the spatial extent of carbonate rich zones within the Lunaveda Group of rocks. Subsequently, we used image spectra of the surface exposure of rock phosphate of the existing mine at Khatama area as the reference for implementing adaptive coherence estimator (ACE) algorithm in the VNIR -SWIR bands of ASTER data to derive the abundance map of phosphorite. We confirmed the presence of phosphate in the identified phosphate anomaly zones during field surveys by implementing rapid colorimetric test that is proven for identifying the presence of phosphate in the samples. We also used the available surface geochemical data of P2O5 to establish the synergy between geochemical data and identified geochemical anomaly.

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.

Effectiveness of LOF, iForest and OCSVM in detecting anomalies in stream sediment geochemical data

This paper compares three unsupervised machine-learning algorithms – local outlier factor (LOF), Isolation Forest (iForest) and one-class support vector machine (OCSVM) – for anomaly detection in a multivariate geochemical dataset in northeastern Iran. This area contains several Au, Cu and Pb–Zn mineral occurrences. The methodology incorporates single-element geochemistry, multivariate data analysis and application of the three unsupervised machine-learning algorithms. Principal component analysis unveiled diverse elemental associations for the first seven principal components (PCs): PC1 shows a Co–Cr–Ni–V–Sn association indicating a lithological influence; PC2 shows a Au–Bi–Cu–W association suggesting epithermal Au mineralization; PC3 shows variability in Zn–V–Co–Sb–Cu–Cr; PC4 shows a Au–Cu–Ba–Sr–Ag association indicating Au and polymetallic mineralization; PC5 reflects Zn–Ag–Ni–Pb related to hydrothermal mineralization; and PC6 and PC7 show element associations suggesting epithermal and intrusive-related polymetallic mineralization. It was found that OCSVM performed slightly better than LOF and iForest in detecting anomalies associated with known Cu occurrences, and it successfully delineated dispersion from all known Au occurrences. LOF outperformed iForest and OCSVM in identifying all four Pb–Zn occurrences, and the three methods substantially limited the areas of the anomaly class. The analysis showed that LOF produced a less cluttered anomaly map compared to the isolated patterns in the iForest map. LOF was accurate in identifying anomalies associated with Au–Pb mineralization, while iForest detected anomalies associated with Pb–Zn–Cu occurrences and neighbouring Pb–Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the SE and west by detecting anomalies associated with two Cu occurences and a Pb–Cu occurrence. This study examined the influence of contamination fraction on detection of geochemical anomalies, revealing a noteworthy rise in the count of mineral occurrences delineated by anomalies when the contamination fraction increases from 5 to 10%. However, even with a 35% contamination fraction, some Cu occurrences remained outside the anomaly category, indicating potentially overlooked geochemical signals from mineral occurrences due to sampling schemes.

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.

Geochemical anomaly separation based on geology, geostatistics, compositional data and local singularity analyses: A case study from the kuh panj copper deposit, Iran

This study combines geochemical anomaly separation with geostatistical approaches and compositional data analysis. To have a reasonable model for abnormal areas, suggesting the optimal drilling coordinates, one needs to consider some effective geological controllers on the mineralization such as lithology, alteration, and tectonic processes. The lithogeochemical samples at 608 locations were processed from the copper porphyry mineralization area of Kuh Panj, Iran, to illustrate the proposed approach. The heterogeneity of rock type domains was detected using contact analysis. Based on this, plurigaussian simulation made it possible to model the uneconomic dykes of the area. Copper-molybdenum grades and filler as geochemical components were transformed into multivariate normal data using isometric logarithmic ratio-flow anamorphosis. Hierarchical rock type-grade simulation reproduced the grade behavior at hard rock type boundaries. Selection of a dense and regular simulation grid minimized the smoothing effect caused by interpolation of the singularity index values. Finally, unsupervised machine learning identified the anomaly zone by clustering the results of the singularity analysis. Validation using drilling data and the geological map shows that the anomaly separation, considering the geological conditions and the compositional nature of the geochemical data, can provide a reliable model of the geochemical anomalies in the Kuh Panj deposit.