Detection Of Geochemical Anomalies Research Articles

A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

The process of data-driven clustering to uncover geochemical anomalies linked to sample catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting unique geochemical attributes within a specified study area. The Ordering Points to Identify the Clustering Structure (OPTICS) method can serve as a robust methodology for detecting geochemical anomalies in SCBs. This is attributed to its capacity to effectively manage varying cluster densities, adaptively identify cluster numbers, exhibit resilience to noise, and display minimum sensitivity to parameters. A comparison was conducted in this research between the outcomes of the OPTICS clustering algorithm and two traditional clustering techniques, namely the Gaussian Mixture Model (GMM) and K-means clustering. In the following, the Expectation-Maximization (EM) technique is employed to train the GMM for clustering. Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as two validate statistical metrics implemented to ascertain the optimal number of components (clusters) belong to the GMM. It should be noted that the effectiveness of the clustering algorithms was further assessed using the Calinski-Harabasz (CH) index and the success-rate curves. OPTICS, a density-based clustering approach, was confirmed to be more effective than K-means and GMM for identifying MVT PbZn anomalies in Varcheh district, western Iran. Furthermore, the specified anomalies show a geo-spatial correspondence with the geological facts, and it has been observed that strong anomalies are more discoverable in close proximity to MVT PbZn occurrences. This work suggests a novel anomaly detection approach based on OPTICS, which exhibits superior performance and data-modeling efficiency. The main emphasis is on effectively distinguishing geochemical anomalies from sample data originating from populations with uncertain distributions.

Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics (e.g., artificial intelligence, machine learning, data modeling) into the geochemical survey data pipeline occurs almost entirely within the data usage stage. In this study, we predict elemental concentrations at the data generation stage and explore how predictive analytics can be integrated more thoroughly across the data lifecycle. Inferential data generation is used to modernize lake sediment geochemical data from northern Manitoba (Canada), with results and interpretations focused on elements that are included in the Canadian Critical Minerals list. The results are mapped, interpreted and used for downstream analysis through geochemical anomaly detection to locate further exploration targets. Our integration is novel because predictive modeling is integrated into the data generation and usage stages to increase the efficacy of geochemical surveys. The results further demonstrate how legacy geochemical data are a significant data asset that can be predictively modernized and used to support time-sensitive mineral exploration of critical minerals that were unanalyzed in original survey designs. In addition, this type of integration immediately creates the possibility of a new exploration framework, which we call predictive geochemical exploration. In effect, it eschews sequential, grid-based and fixed resolution sampling toward data-driven, multi-scale and more agile approaches. A key outcome is a natural categorization scheme of uncertainty associated with further survey or exploration targets, whether they are covered by existing training data in a spatial or multivariate sense or solely within the coverage of inferred secondary data. The uncertainty categorization creates an effective implementation pathway for future multi-scale exploration by focusing data generation activities to de-risk survey practices.

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.

Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms

Metallogenic-Factor Variational Autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms

The Anomaly Detector, Semi-supervised Classifier, and Supervised Classifier Based on K-Nearest Neighbors in Geochemical Anomaly Detection: A Comparative Study

The Anomaly Detector, Semi-supervised Classifier, and Supervised Classifier Based on K-Nearest Neighbors in Geochemical Anomaly Detection: A Comparative Study

Recognizing geochemical patterns related to mineralization using a self-organizing map

The recognition of geochemical patterns, including the spatial association of geochemical patterns, geochemical element associations, and geochemical anomalies related to mineralization, requires different methods to meet the corresponding requirements. In this study, a self-organizing map (SOM), an unsupervised learning technique, was employed to realize the whole processes of geochemical pattern recognition in southeastern Hubei Province, China. First, the clustering ability of SOM was adopted to reveal the spatial association of geochemical patterns; then, geochemical elemental associations were adopted according to the association between variables and clusters derived from the correlation analysis between the unified distance matrix (U-matrix) and SOM maps, and finally the quantification error (QE) of the SOM was adopted as an anomalous index to detect multivariate geochemical anomalies related to mineralization. The obtained results show that two of the clusters are spatially closely related to ore-forming geological bodies, that is Precambrian strata and Yanshanian intrusive rocks. The associations of Au–As–Ba–Cd–Sb–V and Au–Ag–Pb–Zn–Bi–Cu–Mo–Na2O–P–Sr–W are responsible for the characterization of the two clusters, and their combination was further used for multivariate geochemical anomaly detection. The extracted multivariate geochemical anomalies are closely spatially correlated with known gold polymetallic deposits. Both the receiver operating characteristic (ROC) and success-rate curves suggest a satisfactory anomaly detection results, which can provide guidance for further mineral exploration. The results of this study also suggest that SOM is an effective and interpretable tool for a better recognition of geochemical patterns related to mineralization.

Constructing a high-performance self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies for gold exploration targeting

In an area where mineral deposits have been discovered, both anomaly detection algorithms and supervised classification algorithms can be adopted to detect mineralization-related geochemical anomalies for mineral exploration targeting. However, anomaly detection algorithms themselves cannot use known mineral deposits as the supervisors to improve the performance of geochemical anomaly detection models. Supervised classification algorithms can not properly deal with the extreme class-imbalance of geochemical exploration data in the establishment of classification models. Therefore, neither anomaly detection models nor supervised classification models perform well in the detection of mineralization-related geochemical anomalies. In order to obtain a high-performance mineral exploration targeting model, the self-training algorithm was adopted to construct a self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies in the Chengde area in Hebei Province (China). The self-training model was compared with the support vector classification model, logistic regression model and five dictionary learning models in terms of area under the curve (AUC) and lift index. The AUC value of the self-training model (0.89) is much higher than those of the two supervised classification models (0.81–0.82) and five anomaly detection models (0.79–0.81). The lift index of the self-training model (11.87) is also much higher than those of the two supervised classification models (2.77–4.04) and five anomaly detection models (2.64–3.48). Therefore, the self-training model performs much better than the two supervised classification models and five anomaly detection models in the detection of gold mineralization-related geochemical anomalies. The gold mineralization-related geochemical anomalies detected by the self-training model highly conform to the metallogenic and geological features in the study area. Therefore, it is a prospective method to establish a self-training model to detect mineralization-related geochemical anomalies in an area where mineral deposits have been discovered.

Combining neighbourhood component analysis with dictionary learning algorithms to improve the performance of dictionary learning models for geochemical anomaly detection

In geochemical exploration, a geochemical anomaly detection model is usually established to describe the population distribution of geochemical data, and samples that do not conform to the model are identified as geochemical anomalies. Because the establishment of a geochemical anomaly detection model does not make use of the relationship between geochemical elements and mineralization, its performance for mineral exploration targeting is affected to a certain extent. For this reason, neighbourhood component analysis and dictionary learning algorithms were combined to detect geochemical anomalies associated with gold mineralization in the Chengde area in Hebei Province, China. Neighbourhood component analysis was used to transform geochemical data from the input space into the neighbourhood component space to enhance the separability between the geochemical anomalies associated with gold mineralization and the background. Dictionary learning models for geochemical anomaly detection were established in the neighbourhood component space. The performance of the dictionary learning models established in the neighbourhood component space was compared with that of corresponding models established in the input space in geochemical anomaly detection. The results show that the former models are superior to the latter. In addition, there is a strong consistency between the mineral exploration targeting results and metallogenic characteristics of the study area. Therefore, combining neighbourhood component analysis and dictionary learning algorithms can improve the performance of dictionary learning models in geochemical anomaly detection.

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.

Dictionary learning for multivariate geochemical anomaly detection for mineral exploration targeting

Dictionary learning is usually used to train an overcomplete dictionary composed of basis vectors (i.e., atoms) to best sparsely represent the training dataset. The sparse representation of a data point is a sparse linear combination of the atoms derived from the well-trained overcomplete dictionary. The sparse representations of all the training data points constitute a supporting set of the population distribution of the training dataset. In this paper, geochemical exploration data was used as the training data of the five dictionary learning algorithms to train overcomplete dictionaries. The sparse representations of all the geochemical data points were used to describe the (complex) geochemical background, and the Euclidean distance between a data point and its sparse representation was used to express the anomality of the data point in multivariate geochemical anomaly detection. For demonstration purposes, the Chengde district in Hebei Province (China) was used as the case study area, and five dictionary learning models were established for multivariate geochemical anomaly detection for gold mineral exploration targeting. The performances of the five dictionary learning models were compared with that of the k-nearest neighbor (KNN) model, combined KNN model, and Gaussian mixture model (GMM) in gold mineral exploration targeting. The results show that (a) the five dictionary learning models are superior to the KNN model, the combined KNN model, and the GMM model either in terms of receiver operating characteristic curve (ROC) or in terms of area under the curve (AUC), and (b) the gold prospective areas optimally delineated in the study area accounts for a small proportion of the area, but contains most of the known gold deposits, and have spatial correlation with the favorable ore-forming factors such as the outer contact zone of multistage magmatic intrusion and the NE-trending fault zone. Therefore, the dictionary learning algorithms are feasible techniques of multivariate geochemical anomaly detection for mineral exploration targeting. It is worth to further test the usefulness of the dictionary learning algorithms in different areas with complex geochemical backgrounds.

Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Québec and Labrador

Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limitations are the dependence on sample stoichiometry (e.g., the existence of minerals), the necessity of log-ratio transformations when dealing with compositional data, and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping. In this study, we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML. We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province (Québec and Labrador), Canada. The region is known for its REEs endowment and the data were gathered for prospectivity mapping. A comparison with the established multivariate hybrid data- and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort, our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications. The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region. These findings have potentially wider implications on exploration target generation, where project risks (financial, environmental, political, etc.) and geochemical anomalies must be quantified using robust and effective data-driven approaches. In addition, our methodology is more replicable and objective, as manual geoscientific interpretation is not required during the detection of geochemical anomalies.

Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwana Supercontinent). Wedemonstrate that a variety of trace elements, including most of the lanthanides, chalcophile, lithophile, and siderophile elements, can be predicted with excellent accuracy. This finding reveals that there are reliable, high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks. Since the major and minor elements are used as predictors, prediction performance can be used as a direct proxy for geochemical anomalies. As such, our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations. Compared to multivariate compositional data analysis methods, the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies. Because we do not use multivariate compositional data analysis techniques (e.g. principal component analysis and combined use of major, minor and trace elements data), we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space. Therefore, we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data. The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping, or be used as a pre-processor to reduce the detection of false geochemical anomalies, particularly where the data is of variable quality. • Machine learning can predict trace element concentrations from major and minor element geochemical data. • Various machine learning algorithms can accurately predict trace element concentrations. • Log-ratio transformation of geochemical data is not always necessary when using machine learning. • Prediction residuals (difference between predicted and actual quantities) can be used to map geochemical anomalies.

Combining the outputs of various k-nearest neighbor anomaly detectors to form a robust ensemble model for high-dimensional geochemical anomaly detection

Machine learning techniques provide useful methods for high-dimensional geochemical anomaly detection for mineral exploration targeting. However, the instability of the machine learning models often leads to the uncertainty of high-dimensional geochemical anomaly detection result. Combining various individual models to form an adaptive ensemble anomaly detector is a feasible way to enhance the robustness of machine learning anomaly detectors. In this study, the average method, maximization method, average of maximum (AOM) method, and maximum of average (MOA) method were adopted to combine the outputs of various k-nearest neighbor (KNN) anomaly detectors to improve the robustness of the KNN models in the high-dimensional geochemical anomaly detection in the Baishan district (Jilin Province, China). The effectiveness of the four combination algorithms for high-dimensional geochemical anomaly detection was evaluated by comparing the ensemble models obtained by using the four combination algorithms with the single KNN model, Gaussian mixture model (GMM), one-class support vector machine (OCSVM), and isolation forest (IForest) in the case study. It is found that the four ensemble models (a) perform similarly well in high-dimensional geochemical anomaly detection, and (b) perform better than the single KNN model, GMM, OCSVM, and IForest in high-dimensional geochemical anomaly detection. Therefore, the average method, maximization method, AOM method, and MOA method are potentially useful algorithms for combining the outputs of various KNN models to form robust ensemble models for high-dimensional geochemical anomaly detection.

Application of multivariate geostatistical simulation and fractal analysis for detection of rare-earth element geochemical anomalies in the Esfordi phosphate mine, Central Iran

The present study was conducted to determine different geochemical anomalies of rare-earth elements (REEs) using a combined approach of stepwise factor analysis (SFA), sequential Gaussian simulation (SGS), and concentration–area (C-A) fractal modeling based on surface lithogeochemical samples obtained from the Esfordi phosphate mine (Central Iran). The Esfordi mine is one of the important mines in the Bafq metallogenic zone due to average and maximum grades of 0.5 and 1.7%, respectively for REEs. With SFA operating in two steps, the REEs were placed in the first factor of the second stage (F1-2). Then, SGS and C-A fractal modeling were performed on F1-2 factor scores for classification of anomalies. A log-ratio matrix was used to evaluate the correlation of these results with anomalous lithogeochemical samples, as well as to determine the relationship of anomalies with rock types and mineralized units and, finally, to validate the results of the SGS–fractal modeling. The results confirmed an appropriate correlation between F1-2 anomalies and high concentration in further rock samples. The main anomalies were found to have good correlation with an apatite–iron unit and in general with other apatite-bearing units based on overall accuracy values. The apatite-bearing units with high values of REEs were located in the northern and central parts of the mine. The results of the SFA, SGS and C-A fractal modeling show that this hybrid approach can be useful in determining anomalies with high accuracy.

Detection of Multivariate Geochemical Anomalies Using the Bat-Optimized Isolation Forest and Bat-Optimized Elliptic Envelope Models

Isolation forest and elliptic envelope are used to detect geochemical anomalies, and the bat algorithm was adopted to optimize the parameters of the two models. The two bat-optimized models and their default-parameter counterparts were used to detect multivariate geochemical anomalies from the stream sediment survey data of 1:50 000 scale collected from the Helong district, Jilin Province, China. Based on the data modeling results, the receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the two bat-optimized models and their default-parameter counterparts. The results show that the bat algorithm can improve the performance of the two models by optimizing their parameters in geochemical anomaly detection. The optimal threshold determined by the Youden index was used to identify geochemical anomalies from the geochemical data points. Compared with the anomalies detected by the elliptic envelope models, the anomalies detected by the isolation forest models have higher spatial relationship with the mineral occurrences discovered in the study area. According to the results of this study and previous work, it can be inferred that the background population of the study area is complex, which is not suitable for the establishment of elliptic envelope model.

Detection of multivariate geochemical anomalies associated with gold deposits by using distance anomaly factors

The outliers of the statistical population are usually far from most samples in the observation space. Therefore, the total distance from a sample to all remaining samples in the population can be used to quantitatively represent the anomaly level of the sample. The greater the total distance between the sample and all the remaining samples, the more likely the sample is to be an outlier. Accordingly, the total distance of each sample in the population can be used to define a distance anomaly factor for each sample for outlier detection. The spatial distance between each pair of samples in the population can be measured by different distance measures, such as the Mahalanobis distance, Manhattan distance, Euclidean distance, and kernel Euclidean distance. According to different distances, different distance anomaly factors can be defined for each sample in outlier detection. These distance anomaly factors are potentially useful in geoscientific data procession. For demonstration purposes, the distance anomaly factors were used to detect multivariate geochemical anomalies associated with gold deposits from the stream sediment survey data in the Jinchanggouliang district, Inner Mongolia, China. Receiver operating characteristic curve and area under the curve were used to evaluate the performance of distance anomaly factors for geochemical anomaly detection. The results show that the distance anomaly factors perform better than continuous restricted Boltzmann machine and one-class support vector machine in the detection of multivariate geochemical anomalies associated with gold deposits. The Youden index was used to determine the optimal threshold to separate geochemical anomalies from geochemical background. The geochemical anomalies detected by the distance anomaly factors occupy 6.2–14.4% of the study area while contain 70–91% of the known gold deposits. The geochemical anomalies detected by the continuous restricted Boltzmann machine occupy respectively 3.1% of the study area while contain 52% of the known gold deposits. The geochemical anomalies detected by the one-class support vector machine occupy respectively 8.5% of the study area while contain 70% of the known gold deposits. Therefore, the distance anomaly factors are potentially useful techniques for geochemical anomaly detection.

Detection of geochemical anomalies using a fractal-wavelet model in Ipack area, Central Iran

The purpose of this study is to detect geochemical anomalies utilizing a Wavelet-Number (W-N) fractal model based on combination of a two-dimensional Discrete Wavelet Transformation (DWT) signal analysis and a Concentration-Area (C-A) fractal modeling. The DWT was carried by Sym8 as a selected wavelet pattern (by energy matching method) for Zn and Pb based on rock samples collected from the Ipack area, Central Iran. In addition, the DWT was decomposed by continuous wavelet coefficients at five levels and a final image was synthesized. Furthermore, the DWT data were classified using a fractal-wavelet model for delineation of both Zn and Pb anomalies from background levels in this area. Moreover, a Spectrum-Area (S-A) fractal modeling and classical statistics analysis were carried out and their results were compared with the wavelet-fractal modeling. As a result, the main Zn and Pb anomalies are located in the central and southern parts of the Ipack area. The results obtained by the proposed fractal-wavelet modeling are in connection with drilling for Zn and Pb exploration.

Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China

Identifying significant geochemical anomalies is a fundamental task in mineral exploration. In terms of stochastic process, a specific ore-forming process can be regarded as a small probability event since the deposit type sought rarely occurs in the crust of the earth. Therefore, machine learning algorithms for anomaly detection are suitable for identification of mineralization-related geochemical anomalies. However, identification of significant geochemical anomaly signatures is often hindered by irrelevant and/or redundant variables in a geochemical dataset. It is then necessary to extract significant features by means of dimensional reduction before applying unsupervised machine learning algorithms for anomaly detection. In this study, six unsupervised machine learning methods including isolation forest (IF) and five hybrid models that combined IF with dimensional reduction techniques were implemented and evaluated in identifying Ag–Pb–Zn polymetallic mineralization-related geochemical anomalies using stream sediment geochemical data from north-western Zhejiang Province, China. The five hybrid models involved combining IF with hierarchical clustering (HC-IF), principal component analysis (PCA-IF), stacked denoising autoencoder (SDAE-IF), both HC and PCA (HC-PCA-IF) and both HC and SDAE (HC-SDAE-IF). Anomalies identified by these six methods were evaluated by the receiver operating characteristic (ROC) curve method. The spatial overlay of known mineral deposits and the identified geochemical anomaly maps showed that a strong spatial association existed between the locations of discovered mineral deposits and high geochemical anomaly areas. Furthermore, the area under the curve (AUC) values suggested that the HC-SDAE-IF hybrid model performed best and the HC-IF and HC-PCA-IF models performed better than the IF, PCA-IF and SDAE-IF models. This study demonstrated that: (i) IF is a useful and efficient unsupervised machine learning algorithm for detection of Ag-Pb-Zn polymetallic mineralization-related geochemical anomalies in the study area; and, (ii) the proper manipulation of dimensional reduction and extraction of mineralization-related geochemical variables can not only significantly improve the performance of unsupervised machine learning algorithms including deep learning methods in geochemical anomaly detection, but also be instrumental in clarifying the geological meaning of the identified geochemical anomaly. In this regard, reducing dimensionality and extracting mineralization-related variables (i.e. elements) from raw geochemical data is strongly recommended before applying unsupervised machine learning methods, particularly the sophisticated deep learning algorithms, in geochemical anomaly detection.

Structural and non-structural statistical methods: implications for delineating geochemical anomalies

ABSTRACT Geochemical sampling media, be they rock chip, soil or stream sediment samples, routinely collected in mineral exploration, are key to detecting mineralisation-related geochemical dispersion patterns. Notwithstanding the diversity of methods applied to geochemical anomaly detection, they can be broadly divided into two major groups, namely structural and non-structural techniques. The former group covers those methods in which threshold values are assigned based on sample location while the sample locations are not required in the application of the non-structural techniques. In this study, rock chip samples from the Shadan porphyry copper–gold deposit are used to address the question as to how structural and non-structural methods can separate geochemical populations for the purpose of a deposit-scale study. It was revealed that the two structural techniques used in this study, concentration-area (C-A) fractal and U-spatial statistic methods, outperformed the non-structural techniques employed in this study.