Stream Sediment Geochemical Data Research Articles

The influence of supervised and unsupervised methods in the analysis of low density stream sediments in the regional prospecting of pegmatites in the state of Espírito Santo, Araçuaí Orogen - Eastern Pegmatite Province of Brazil

The state of Espírito Santo is located in Brazil southeastern and it is geologically inserted in the Eastern Brazilian Pegmatite Province in the Araçuaí Orogen. This province is one of the most important pegmatite provinces in the world, known mainly for the occurrence of gem-quality minerals and high grade lithium deposits. Although Espírito Santo has a geological framework with many granite intrusions similar to those hosting mineralized pegmatites in Minas Gerais, it hasn't been acknowledged as a potential pegmatitic area. There are reports of mineralization of beryl (aquamarine), topaz, amethyst, and chrysoberyl in the state's mineral history. I this research we use regional stream sediment geochemical data of the Espirito Santo State, to create prospective geochemical maps to map gemological pegmatites. To do so, primary descriptive statistical analyses as well as Pearson and Spearman correlations and Principal Component Analysis (PCA) were used to select elements associated with pegmatites. The geochemical prospectivity mapping was carried out by supervised and unsupervised approaches. The supervised method was based on integration of the elements by C-A fractal models, used to define the geochemical thresholds, and fuzzy logic methodology to integrate weighted geochemical maps. The unsupervised approach, however, was based on interpolation of the representative principal component scores. All models were validated using the Prediction Area plot (P-A plot). The results of our research showed that unsupervised methodology overperformed the other weighted supervised models, with a prediction rate of 78.14 % and prospectivity of 0.87. The final results also show a pegmatite prospectivity map for the Espirito Santo state, that indicates a great exploration potential for gemological minerals.)

Stream sediment geochemical anomaly mapping based on the dynamic enhanced weighted drainage catchment basin method for mineral exploration

Accurately determining the presence of subterranean deposits is challenging due to the intricate geological processes that give rise to these deposits. The use of stream sediment geochemical data to identify and map geochemical anomalies is a widely employed technique in mineral exploration and offers a robust indicator of the possible presence of mineral deposits. This research presents a comparison between the weighted drainage catchment basin (WDCB) technique and its improved version, the dynamic enhanced weighted drainage catchment basin (DE-WDCB) approach, in the context of mineral exploration. We constructed a model of the catchment basin for the study area, clarified the upstream and downstream relationships between the basins, determined the background value in each basin via the trend surface method, and carried out geochemical anomaly mapping based on the DE-WDCB method. Then we conducted two comparative experiments using the WDCB method, employing both dynamic and nondynamic classification methods for anomaly classification. Finally, we compared the mineralization of the three methods and find that the DE-WDCB method exhibited superior performance in identifying sedimentary manganese ore anomalies, followed by the dynamic grading WDCB method and the nondynamic grading WDCB method. These results indicated that the DE-WDCB method revealed excellent performance when applied to stream sediment geochemistry in prospecting, hence enhancing its utility in mineral resource exploration.

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.

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. The PCA unveiled diverse elemental associations with PC1 showing a Co–Cr–Ni–V–Sn association indicating a lithological influence, PC2 shows an Au–Bi–Cu–W association suggesting epithermal gold mineralization, PC3 shows variability in Zn–V–Co–Sb–Cu–Cr, PC4 shows an Au–Cu–Ba–Sr–Ag association indicating gold 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 neighboring Pb–Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the southeast and west by detecting anomalies associated with two Cu and a Pb–Cu occurrences. 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.

Graph convolutional network for lithological classification and mapping using stream sediment geochemical data and geophysical data

Lithological mapping plays a vital role in acquiring a comprehensive understanding of subsurface rocks and geological structures. Traditional lithological mapping relies heavily on manual field surveys, with the accuracy of the mapping greatly constrained by the expertise of mappers, and thus this process is commonly slow and costly. In the present study, the graph convolutional network (GCN) method was proposed for efficient and automatic lithological mapping, using various geological survey data (e.g. geochemical and geophysical data). The GCN is not limited by the spatial distribution of sampling data, and hence it can be directly applied to the integrated resampling data that do not adhere to the regular spatial distribution patterns of Euclidean space. To evaluate the potential application of the GCN method in lithological classification and mapping, a case study was conducted in the Maqin region (China). Geochemical and geophysical data were utilized as the basis for lithological classification, as they can provide valuable information on geochemical compositions and physical properties of geological bodies, respectively. The study process was primarily divided into three parts. First, basic pre-processing was conducted on the geochemical data and the geophysical data, including centred log-ratio transformation and resampling. Subsequently, the resampled points were divided into training and testing sets in the proportion of 5.5:1. In the training set, 20% of the points are randomly selected as the validation set. Ultimately, using each resampled point as a node and the integrated geochemical and geophysical data as features, a GCN model and an undirected graph were constructed based on an adjacency distance of 1 km. The graph was employed by the constructed GCN model for lithological classification. The results reveal that the majority of lithological units can be accurately classified with an overall accuracy of 86%, indicating that the GCN model exhibits good applicability in lithological classification.

Enhancing geochemical background estimation using rock weathering

The chemical composition of stream sediments is heavily influenced by weathering processes occurring in upstream lithological units. Despite their importance, these processes are frequently ignored in the examination of stream sediment geochemical data when identifying mineralized zones. Instead, existing geochemical background estimation procedures rely on the area size of upstream rocks as an indicator of their contribution to elemental concentrations of sediments. To address this limitation, a stream sediment geochemical survey was conducted in a pilot area in central Iran, and the physico-mechanical properties of upstream rock types, including elastic velocities, porosity, and Schmidt hammer rebound values, were evaluated. A new weathering index, “Wij,” was developed, utilizing proxies of rock weathering to estimate the geochemical background attributed to lithology at sediment sampling sites. The regression model was constructed by combining weathering proxies (Wij) with other independent variables, including the presence or absence of rocks in catchments, area size (Xij), and the weathering-corrected contributing factor (XijWij). The results suggested the significant capability of weathering proxies in explaining the geochemical variability of major elements such as Al, K, Fe, Ca, and Na. However, the regression model using weathering-corrected contributing factors delivered better overall accuracy than other independent variables. Nevertheless, our results also identified some limitations of the application of regression model in stream sediment data analysis.

Enhancing Geological Understanding and Identifying Gold Anomalies in the Ailaoshan Orogen

AbstractThe Ailaoshan Orogen in the southeastern Tibet Plateau, situated between the Yangtze and Simao blocks, underwent a complex structural, magmatic, and metamorphic evolution resulting in different tectonic subzones with varying structural lineaments and elemental concentrations. These elements can conceal or reduce anomalies due to the mutual effect between different anomaly areas. Dividing the whole zone into subzones based on tectonic settings, ore cluster areas, or sample catchment basins (Scb), geochemical and structural anomalies associated with gold (Au) mineralization have been identified utilizing mean plus twice standard deviations (Mean + 2STD), factor analysis (FA), concentration‐area (C‐A) modeling of stream sediment geochemical data, and lineament density in both the Ailaoshan Orogen and the individual subzones. The FA in the divided 98 Scbs with 6 Scbs containing Au deposits can roughly ascertain unknown rock types, identify specific element associations of known rocks and discern the porphyry or skarn‐type Au mineralization. Compared with methods of Mean + 2STD and C‐A model of data in the whole orogen, which mistake the anomalies as background or act the background as anomalies, the combined methods of FA and C‐A in the separate subzones or Scbs works well in regional metallogenic potential analysis. Mapping of lineament densities with a 10‐km circle diameter is not suitable to locate Au deposits because of the delineated large areas of medium‐high lineament density. In contrast, the use of circle diameters of 1.3 km or 1.7 km in the ore cluster scale delineates areas with a higher concentration of lineament density, consistent with the locations of known Au deposits. By analyzing the map of faults and Au anomalies, two potential prospecting targets, Scbs 1 and 63 with a sandstone as a potential host rock for Au, have been identified in the Ailaoshan Orogen. The use of combined methods in the divided subzones proved to be more effective in improving geological understanding and identifying mineralization anomalies associated with Au, rather than analyzing the entire large area.

A New Discovery of Cu Mineralization in the North Qaidam, Tibet via Log-Ratio, Robust Factor Analysis, and Spectrum–Area Modeling

The Qinghai–Tibet Plateau, abundant in mineral resources, is a treasure trove for geological explorers. However, exploration has been hindered by the presence of dense vegetation, weathering layers, and desert cover, particularly in the North Qaidam region. As a result, there is an urgent need to develop efficient geochemical composition methods. In a study of stream sediment geochemical data from the Lüliangshan area of the North Qaidam, the log-ratio transformation was used for data processing, Robust Factor Analysis (RFA) was used for model construction, and the spectrum–area (S-A) model was used for anomaly separation. The outcomes identified two distinct groups of element combinations associated with mineralization. The first group consisted of Au + Ag + Pb + Sb + Hg linked to tectonic-altered rock-type Au (antimony) deposits, while the second group consisted of Cu + Zn + Co and was closely associated with Cu-Ni sulfide deposits. The S-A fractal filtering technique amplified weak anomalies and minimized the area of anomalies against strong backgrounds. The study successfully detected substantial Cu mineralization in the source areas of geochemical anomalies in the Lüliangshan region. Consequently, the log-ratio–RFA–S-A fractal model has been proven to be an effective combination of methods for identifying and extracting geochemical anomalies from stream sediment samples and for mineral exploration in covered areas.

Identifying high potential gold mineralization using geological and stream sediment geochemical data: A case study from western Dangreyongcuo area in the central Lhasa terrane, Tibet, China

Northern Tibet exhibits abundant placer gold deposits, with a longstanding history of mining activities. Despite the widespread placer gold occurrences, the reports on the primary rock gold deposits or occurrences remain scarce. The central Lhasa terrane in northern Tibet hosts well-developed Zenong Group volcanic rocks, indicating great potential for the exploration of porphyry-epithermal copper (gold) deposits. Here, we select the Dangreyongcuo area in this region to investigate its metallogenetic potentiality, through a comprehensive 1:50,000 stream sediment geochemical survey, analyzing 12 elements (Au, As, Sb, Ag, Te, Cu, Pb, Zn, W, Sn, Bi, and Mo), and a detailed geological mapping. We aim to identify gold-related mineralization anomalies and provide clues for further bedrock gold exploration in this region. Based on the stream sediment geochemical data, zones 1–4, have been delineated using single-element indicator Au and element association indicator Au + As + Sb, which was determined through robust principal component analysis (RPCA) and using the exploratory data analysis (EDA) method. Subsequent validation exercises evaluating these four delineated zones reveal that Zone 1, Xinlong-Langmeila area, is the most promising prospect due to its high element concentrations, large size, and marked coincidence of element assemblages (also referred to as high-large-coincidence anomalies; HLCAs) and its characteristics are in line with those of epithermal deposits. Notably, the Xinlong deposit represents the first documented epithermal gold deposit hosted within the Zenong Group volcanic rocks. The identification of this deposit offers valuable insights and strategic directions for the broader understanding of Zenong Group volcanic rocks across the central Lhasa terrane. Additionally, subsequent strategies have been proposed for mineral exploration at both Xinlong camp and regional scales.

Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders

Lithium is a strategic metal for high-technology industries that plays a vital role in realizing electromobility and effective energy storage for smartphones and electric/hybrid vehicles, in addition to being important in accumulating energy from renewable sources. Motivated by this, the development of state-of-the-art workflows is imperative to make Li exploration more efficient and guarantee a sustainable supply to the demanding markets in the future. In this paper, a computational protocol was described to address two critical challenges raised in the Li exploration: (i) selection of mineralization-related pathfinder variables and (ii) recognition of multi-element geochemical anomalies related to ore-forming processes. Robust sparse principal component analysis (SPCA) was employed to reduce active (non-zero) loading coefficients and straightforward selection of Li pathfinder variables. The selected variables were then fed into the training network of stacked autoencoders (SAE) to learn deep representations of multivariate input signals and quantify reconstruction errors linked to Li-vectoring geochemical anomalies. As a representative example, the proposed workflow, i.e., SPCA + SAE, was implemented in the R programming language and applied to a real-case experiment with stream sediment geochemical data pertaining to the Moalleman district, NE Iran. Moreover, compositional robust principal component analysis (RPCA) and Mahalanobis distance (MD) technique were adopted to constitute two comparative models, RPCA + SAE and SPCA + MD, as benchmarks to judge the competence of the SPCA + SAE model in variable selection and anomaly detection, respectively. A performance appraisal by success-rate curves and relevant area under the curves (AUCs) indicated that the SPCA + SAE model brings, by calculating the highest AUC, spatial patterns that are more promising for vectoring towards Li-mineralized grounds. Moreover, AUCSPCA+MD was measured to be greater than AUCRPCA+SAE, suggesting robust variable selection is a more important paradigm than deep anomaly detection for Li exploration targeting. Student's t–statistic method was eventually applied to the anomaly map from the SPCA + SAE model to define relevant thresholds for narrowing down prospective areas for the next round of Li exploration within the study area.

Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches

Cluster analysis can be used to organize samples and generate ideas regarding the multivariate geochemistry of given dataset. Traditional clustering techniques have the drawbacks of high computational complexity and poor adaptability to big data. Hence, there has recently been much focus on creating better clustering algorithms. Although many clustering algorithms have been applied, and some produce notable clustering results, the performance efficiency of algorithms is often highly dependent on the values the user chooses for the parameters. Currently, density-based spatial clustering of applications with noise clustering (DBSCAN) is widely utilized in image processing, bioinformatics, and social network analysis owing to its ability to detect clusters of various shapes. Even though partitional clustering techniques may be effective when the number of clusters K is known in advance, they cannot implement non-convex clustering and rapidly return to a local optimum. This study proposes the concept of DBSCAN clustering for stream sediment geochemical data. In this respect, the geochemical data collected from Varcheh district, SW Iran, were processed using the clr transformation before applying DBSCAN. Then, PCA was used to minimize the dimension of variables and specify the mineralization-related elements. In the following, one of the PCs connected with mineralization (PC2) was chosen for further analysis. DBSCAB, Mean-shift and Fuzzy K-means algorithms were used to monitor the multi-element geochemical anomalies linked to MVT PbZn deposits in the study area. According to Davies-Bouldin and Silhouette as two validation metrics, it can be deduced that the three SCB models are advantageous, however, the model generated by DBSCAN is preferable to the model generated by Mean-shift and Fuzzy K-means.

Geochemical prospectivity mapping using compositional balance analysis and multifractal modeling: A case study in the Jinshuikou area, Qinghai, China

To effectively map the prospecting targets related to deposit-type sought, local indicators of spatial association (LISA), compositional balance analysis (CoBA), and multifractal modeling were applied to process stream sediment geochemical data that are collected from the Jinshuikou area, Qinghai province, China. In this study, 14 elements, including Zn, Cu, Ni, Mo, Co, V, Ti, Sb, Ag, Pb, Sn, Hg, Cd, and W, were analyzed in 493 samples. The result of LISA shows that the ore-forming elements Zn and Cu have a close spatial autocorrelation, which supports the construction of balance b1 related to the Zn-Cu mineralization assemblage. A traditional contrast additive method was applied to compare with balance b1 for geochemical anomaly identification. The results clearly indicate that balance b1 significantly assisted with not only reduction of geochemical data internal imbalance caused by extreme value, but also intensification of Zn-Cu assemblage geochemical anomalies associated with Zn polymetallic mineralization. The high anomalies of the two balances, b5 (Co-V-Ti) and b8 (Sn-Pb-Ag-Sb), reflect well the distribution of basic and intermediate-acid rocks in the area, respectively. The geochemical distribution pattern of the balance b1 can be depicted by multifractal parameters such as singular index α, as well as asymmetry index R, which are useful for describing the spatial heterogeneity of the balance b1. The singularity indices of balance b1 were classified by the concentration-area (C-A) fractal model to produce the high potential areas of the Zn-Cu assemblage. The high potential areas were obviously correlated with all Zn polymetallic occurrences. So, combined with the metallogenic geological characteristics of the study area, five prospective prediction targets were mapped. The anomaly verification demonstrates that the Zn concentration has reached the lowest industrial grade and cut-off grade corresponding to the target area I-1 and I-2, respectively, and Cu also has certain metallogenic potential. There is good potential for Zn-Cu mineralization as represented by this integrated method.

The Delineation of Copper Geochemical Blocks and the Identification of Ore-Related Anomalies Using Singularity Analysis of Stream Sediment Geochemical Data in the Middle and Lower Reaches of the Yangtze River and Its Adjacent Areas, China

The middle and lower reaches of the Yangtze River and its adjacent areas contain abundant mineral resources, especially porphyry–skarn–stratabound Cu–Au–Mo–Fe deposits, and still have great potential for mineral prospecting. In this paper, geochemical blocks and local singularity mapping methods were used to delineate the spatial distribution pattern of Cu and identify the geochemical anomalies related to Cu deposits. Six copper geochemical blocks, each with an area of more than 1000 km2, were all spatially consistent with the locations of the five Cu ore districts (Edongnan, Jiurui, Anqing-Guichi, Tongling, and Ningzhen) and one ore field (Dexing) in the study area. Thus, geochemical blocks delineated with low-density geochemical data can effectively track the locations of ore districts or large ore deposits. Most of the known Cu deposits in the study area were located in anomalous areas with singularity indices less than 1.741 in the Cu singularity map. The singularity analysis could reduce the anomalous areas and identify the geochemical anomalies related to Cu deposits effectively. Geochemical blocks combining a local singularity mapping method is an effective tool for identifying prospecting targets.

Prediction of Au-Associated Minerals in Eastern Thailand Based on Stream Sediment Geochemical Data Analysis by S-A Multifractal Model

Conducted within the scope of geochemical exploration in eastern Thailand, this study aims to detect geochemical anomalies and potential mineral deposits. The objective was to interpret intricate spatial dispersion patterns and concentration levels of deposit pathfinder elements, specifically arsenic (As), copper (Cu), and zinc (Zn), using a comprehensive array of stream sediment geochemistry data. Methodologies involved integrating multifractal properties and traditional statistics, facilitated by the GeoDAS and ArcGIS platforms as instrumental analytical tools. In total, 5376 stream sediment samples were collected and evaluated, leading to the development of an in-depth geochemical map. The results indicated distinct geological units marked by substantially elevated average values of the aforementioned elements. Identification of geochemical anomalies was achieved through the spatial distribution method and the subsequent application of the spectrum-area (S-A) multifractal model. An intriguing link was found between high As concentrations and gold deposits in the area, suggesting As as a viable pathfinder element for gold mineralization. The anomaly maps, generated from the stream sediment data, spotlighted potential zones of interest, offering valuable guidance for future mineral exploration and geological inquiries. Nonetheless, it is vital to recognize that the increased values noted in these maps may be influenced by regional geological factors, emphasizing the necessity for a diverse set of analytical methods for accurate interpretation. This study’s significance lies in its pioneering use of the S-A multifractal model in geochemical data analysis. This innovative approach has deepened our comprehension of geochemical dispersion patterns and improved the precision of mineral exploration.

GWOKM: A novel hybrid optimization algorithm for geochemical anomaly detection based on Grey wolf optimizer and K-means clustering

Identifying the geochemical signatures of valuable mineral deposits using regional geochemical data from stream sediments is a challenging task due to the intricate characteristics of geological formations. Our team is currently investigating the potential of unsupervised clustering analysis (CA) and hybridization with the grey wolf optimizer (GWO) in developing multi-element geochemical models using stream sediment data. To cluster the geochemical data and uncover any unusual patterns, we opted to use the K-means (KM) algorithm due to its straightforward implementation, fast computation speed, and capacity to handle the large datasets. Despite its benefits, the KM method also has notable limitations, such as the random selection of cluster centroids. This can result in higher systematic uncertainty in unsupervised geochemical modeling and longer computation times. To mitigate this concern, we have introduced a new hybrid approach, grey wolf optimizer with K-means so-called the GWOKM algorithm to enhance the delineation of multi-elemental patterns in stream sediment geochemical data. This method incorporates the grey wolf optimization algorithm with KM to optimize the identification of both anomalies and backgrounds using factor analysis and sample catchment basin modeling techniques. This approach was utilized to detect anomalous multi-elemental geochemical patterns indicative of porphyry and skarn copper deposits in the Baft area, Kerman belt, Iran. Upon comparison of the geochemical models derived from KM and GWOKM clustering methods, the latter outperformed the former, as evidenced by its higher prediction rate. The outcomes affirm the efficacy of unsupervised KM clustering analysis (CA) as a means of breaking down geochemical anomaly-background populations. Moreover, the integration of clustering methods with optimization algorithms has proven to be successful for enhancing the credibility of mineralized areas, which could be advantageous in future exploration phases. Based on the results, the GWOKM approach generates more reliable and efficient geochemical anomaly targets.

Leucogranite mapping via convolutional recurrent neural networks and geochemical survey data in the Himalayan orogen

Geochemical survey data analysis is recognized as an implemented and feasible way for lithological mapping to assist mineral exploration. With respect to available approaches, recent methodological advances have focused on deep learning algorithms which provide access to learn and extract information directly from geochemical survey data through multi-level networks and outputting end-to-end classification. Accordingly, this study developed a lithological mapping framework with the joint application of a convolutional neural network (CNN) and a long short-term memory (LSTM). The CNN-LSTM model is dominant in correlation extraction from CNN layers and coupling interaction learning from LSTM layers. This hybrid approach was demonstrated by mapping leucogranites in the Himalayan orogen based on stream sediment geochemical survey data, where the targeted leucogranite was expected to be potential resources of rare metals such as Li, Be, and W mineralization. Three comparative case studies were carried out from both visual and quantitative perspectives to illustrate the superiority of the proposed model. A guided spatial distribution map of leucogranites in the Himalayan orogen, divided into high-, moderate-, and low-potential areas, was delineated by the success rate curve, which further improves the efficiency for identifying unmapped leucogranites through geological mapping. In light of these results, this study provides an alternative solution for lithologic mapping using geochemical survey data at a regional scale and reduces the risk for decision making associated with mineral exploration.

The applicability of G-BASE stream sediment geochemistry as a combined geological mapping, and prospective exploration tool for As-Co-Cu-Ni mineralisation across Cumbria, UK

Stream sediment geochemistry is a useful tool to analyse the geochemistry of the local geology within the source catchment area. This has significant applicability within the field of mineral exploration where understanding regional lithological geochemistry and how this is reflected in stream sediment geochemistry is needed, facilitating the identification of critical metal deposits. Successful identification of these deposits is essential to help tackle the deficit of these metals supply chains, especially for cobalt. This is in order to meet future carbon-neutral technological demand as part of global initiatives towards a more environmentally sustainable society.We make use of the UK Geochemical Baseline Survey of the Environment (G-BASE) dataset to demonstrate this stream sediment geochemical data has the potential to be used as a useful tool for isolating potential critical metals in host rocks across the UK Lake District. We reduced the dimensionality of the G-BASE stream sediment data, creating geochemical maps that identify a combination of volcanic, sedimentary, and plutonic lithologies lining up geological boundaries from established 50 k scale geological maps of the area. This was conducted through a combined statistical and mapping approach within QGIS and ioGAS.Furthermore, we derived average ore metal concentrations (Ag, As, Bi, Co, Cu, Mo, Ni, Sn, Zn) for the Skiddaw Group and the Borrowdale Volcanic Group, two established host lithologies for As-Co-Cu-Ni mineralisation. Average concentrations of Co in the Skiddaw have been modelled to be ~63 ppm, and ~ 28 ppm in the Borrowdale volcanics. These values, combined with As, Cu, and Ni modelled concentrations, and other available exploration-related data (structural maps, underlying batholith topography, mining history etc.) have allowed us to identify 10 prospective areas of interest for possible As-Co-Cu-Ni mineralisation. This workflow has strong applicability within critical metal exploration in the UK and other, prospective regions across the globe.

Mapping of geological complexity and analyzing its relationship with the distribution of gold deposits in the Guangxi Gold Ore Province, Southern China

The Guangxi gold ore province contains >370 gold deposits with total resources of >260 t Au. Factor analysis (FA) of geochemical data, and calculation of lineament density within 10 km, 20 km, and 30 km circular search grids across a geological map, were applied in Guangxi to highlight geological complexity and to analyze its relationships with the distribution of gold deposits. The FA of stream sediment geochemical data for 42 elements, subjected first to center logarithmic transformation (clr), yield several element combinations that allowed distinction of metallogenic signatures from geological backgrounds. The element combinations of the first and second factors were Cd–Co–Cr–Cu–Li–Mn–Ni–P–Sb–Y–Zn–Mg and Be–Bi–La–Nb–Pb–Sn–Th–U–W–Zr–Al, and the spatial distributions of their factor scores overlap the Devonian to Permian strata and granite, respectively. The distribution of factor scores of mineralization-related element combination, Ag–As–Au–Mo, coincides with 70 % of gold deposits. The lineament density mapping can highlight different-scale faults and lithological contacts by using circular search grids with different diameters. The C–A fractal modeling for lineament density has indicated four populations, namely low, moderate-low, moderate-high, and high lineament densities. About ∼80 to ∼90 % of the known gold deposits exist in areas with moderate-low to moderate-high lineament densities. This highlights the importance of distinguishing geological complexity based on faults and lithological contacts, which are commonly the pathways for the focused fluid flow and gold ore formation. The deposit-scale geological complexity mapping has predicted several exploration targets, where high-grade gold orebodies have been prospected successfully by drilling in 2018. The geological complexity mapping is a kind of geochemical and geological complementation for resource potential assessment.

Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization

In recent years, deep autoencoder networks (DANs) have shown enormous potential to achieve state-of-the-art performance for recognizing multi-element geochemical anomalies related to mineralization. By training a DAN, multi-element signatures of geochemical background are learned by higher-level representations of input signals, providing key references to quantify reconstruction errors linked to complex patterns of metal-vectoring geochemical anomalies in non-linear Earth systems. However, the learning of geochemical background representations may be suppressed by redundant mutual information from inter-element correlations and by mixed information of elemental concentration data caused by multiplicative cascade geo-processes. To deal with these issues, we conceptualized an idea of a new deep learning architecture called Info‒DAN, chaining the information maximization (Infomax) processor to the training network of stacked autoencoders. Infomax is an adaptive learning algorithm from information theory paradigms which aims at maximizing the information flow (joint entropy) passed through a feed-forward neural network processor. It was adopted to encode original multi-element data into independent source signals associated with different geochemical sub-populations and to prevent the dilution of background representations caused by inter-element information redundancy. The recovered source signals were then fed into a DAN processor to assist in modeling the improved representations of geochemical background populations and in enhancing complex anomaly patterns. The Info‒DAN technique was applied to stream sediment geochemical data pertaining to the Moalleman district, NE Iran, for performance appraisal in recognition of metal-vectoring geochemical anomalies. Evaluation tools comprising success-rate curves and prediction-area plots indicated that anomaly patterns derived from Info‒DAN, compared to those from a stand-alone DAN, reveal a stronger spatial correlation between ore-controlling fractures/faults and locations of known metal occurrences. The findings suggest that, thanks to the proposed algorithm, complex patterns of geochemical anomalies can be quantified with improved generalization accuracy as well as practical insights for vectoring towards metal exploration targets.

Single‐element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

AbstractThe identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral exploration. The various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical data. To map anomalies in a discrete field model of such data, two corrections are required: background correction and downstream dilution correction. Topography and geomorphology are important factors in variations of element content in stream sediments. However, few studies have considered, through the use of digital terrain analysis, the influence of geomorphic features in downstream dilution correction of stream sediment geochemical data. This study proposes and demonstrates an improvement to the traditional downstream dilution correction equation, based on the use of digital terrain analysis to map single‐element anomalies in stream sediment geochemical landscapes. Moreover, this study compares the results of analyses using discrete and continuous field models of stream sediment geochemical data from the Xincang area, Tibet. The efficiency of the proposed methodology was validated against known mineral occurrences. The results indicate that catchment‐based analysis outperforms interpolation‐based analysis of stream sediment geochemical data for anomaly mapping. Meanwhile, the proposed modified downstream dilution correction equation proved more effective than the original equation. However, further testing of this modified downstream dilution correction is needed in other areas, in order to investigate its efficiency further.