Geochemical Anomalies Research Articles

Three-dimensional magnetotelluric modeling of Vulcano Island (Eolie, Italy) and its implications for understanding recent volcanic unrest

This paper presents the results of an original short-period magnetotelluric survey performed on Vulcano Island (Italy). The obtained three-dimensional resistivity model details structures up to 2.5 km depth, hitherto unexplored. The La Fossa caldera area corresponds to a moderate resistive anomaly, which extends down to the resolved depth and likely represents a “conduit-like” structure along which magmatic fluids stall and ascend. Other resistive anomalies characterize volcanic edifices, craters, volcanic conduits, and/or eruptive fissures. In addition, the shallower hydrothermal system is detected as a conductive anomaly. Sharp resistivity contrasts generally characterize caldera faults. A main N‒S alignment characterizes the island sector, where considerable amounts of deep subsurface fluids accumulate and mix with the ascending magmas related to the most recent volcanic dynamics. The volcanological interpretation of such findings significantly contributes to understanding the geophysical and geochemical anomalies detected in the last year, which involved the Vulcano shallow hydrothermal system, highlighting the potential for possible hydrothermal/phreatic eruptive events.

Strong geochemical anomalies following active submarine eruption offshore Mayotte

Submarine volcanic activity releases large amounts of gases and metals in the water column, affecting biogeochemical cycles and ecosystems at a regional and local scale. In 2018, Fani Maoré submarine volcano erupted 50 km offshore Mayotte Island (Comoros Archipelago, Indian Ocean). Active eruptive plumes were observed in May 2019 at and around the summit with acoustic plumes rising 2 km into the water column coupled to strong geochemical anomalies. Between May 2019 and October 2020, three research cruises monitored the eruptive activity. Here, we report spatial and temporal variability of water column chemistry above the volcano, focusing on dissolved gases, trace metal concentrations, and physico-chemical parameters. In May 2019, concentrations above 800 nM in CH4 and H2 were measured throughout the water column, with Total Dissolvable Mn and Total Dissolvable Fe concentrations above 500 nM, and CO2 values of 265 μM. Strong water column acidification was measured (0.6 pH unit) compared to the regional background. From May 2019 to October 2020, we observed a general decrease in gas concentrations, and an evolution of the TDMn/TDFe ratios similar to previously reported values in other submarine volcanic contexts, and consistent with a decrease of the eruptive activity at the volcano. In October 2020, a rebound of high H2 concentrations resulted from new lava flows, which were identified by seafloor observation using deep-towed camera, 5 km further than the volcano summit. During 2 years timespan of our observations (2019–2020), He, CO2 and CH4 concentrations correlate highlighting a magmatic origin of dissolved gases. δ13C-CH4 values of −34‰ vs. vPDB might suggest magma/sediments interaction during the magma ascent, and potential thermal cracking of organic matter, although abiotic methane generation cannot be ruled out given the volcanic context. Weak correlations between H2 and excess of 3He suggest complex processes of H2 from magmatic degassing, lava/seawater interaction, and oxidation processes in the water column. Strong and correlated Fe, Mn and Si water column anomalies are also consistent with fluid-rock reactions induced by acidic fluids rich in magmatic volatiles. Water column acidification appears to be associated with the release of CO2-rich fluids. A year after the main eruptive event, the system seems to be back to steady-state highlighting the buffer capacity and resilience of the seawater column environment.

Analysis of Distribution and Accumulation of Mobile Forms of Heavy Metals and Arsenic in Soils of the Urbanized Territory of Svirsk (Irkutsk Region)

The analysis of heavy metals and arsenic contamination of soils of the urbanized territory of Svirsk (Irkutsk region) is presented. The features of distribution and concentration of mobile forms of elements – toxicants in soils of different functional zones of the city are revealed. It has been established that the main pollutants of the soils of Svirsk are As, Zn, Pb, Cr, Fe and Cd, which have high accumulation rates. Mainly high–contrast polyelement geochemical anomalies Fe–Pb–As–Zn– Cd–Mn and Cu with extremely dangerous pollution levels are formed in the soil cover of industrial zones. The soils of a private residential area are characterized by local contrasting four–element anomalies Zn–As–Cr–Mn with a dangerous and extremely dangerous degree of pollution, multi–storey residential sector – two-element low-contrast anomalies Cr–Cu with a dangerous and moderately dangerous ecological situation.

Big data analytics for mining geochemistry of gold mineralization: The Gandy gold deposit, the Toroud-Chah Shirin (TCS) belt, north Iran

Due to the complexity of the gold element, the mineralogical and geochemical investigations are significant to trace the gold behavior in sedimentary, metamorphic, magmatic, and post-magmatic processes and to identify the physicochemical conditions (i.e., pH, Eh, temperature, pressure) of concentration in the ore bodies. The trace elements may be concentrated as the primary or secondary haloes in endogenic and exogenic deposits with multi-formational types. Identification and separation of multi-formational geochemical halos associated with ore bodies have been the most significant challenge of gold deposits. Mining Geochemistry and Big Data analytics have become essential to identify the multi-formational geochemical anomalies for gold exploration in the Commonwealth of Independent States (CIS) countries. During the last three decades, pattern recognition techniques such as Bayesian and Geochemical spectra have been used to determine mineralogical and geochemical types of multi-formational anomalies. Big Data analytics has been used for integrating multi-type geoscience data and pattern recognition techniques to assess geochemical anomalies. In this study, Big Data analytics were used to generate the mineralogical and geochemical type (MGT) quantitative models based on the Bayesian, geochemical spectrum and hybrid methods for investigating the distribution of trace element contents of pyrite, galena, and sphalerite in the Gandy Gold Deposit, the Toroud-Chah Shirin (TCS) belt, north Iran. The multi-formational geochemical anomalies associated with gold mineralization in the Gandy deposit indicated a multi-MGT anomaly superposition, which is a combination of two MGTs: Au + Polymetals and Au + sulfide. The results of this study demonstrated a new perspective on the distribution of geochemical anomalies and the multi-MGT geochemical modeling for gold exploration in the TCS belt. Consequently, the instigated approach is greatly conceivable and applicable to reveal contrasting near-ore multi-formational geochemical haloes for gold exploration in many other metallogenic provinces.

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the Sahlabad area, South Khorasan province, East Iran. In this investigation, 709 stream sediment samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and geostatistical and machine learning techniques. Subsequently, hierarchical analysis (HA), Spearman’s rank correlation coefficient, concentration–area (C–A) fractal analysis, Kriging interpolation, and descriptive statistics studies were performed on the geochemical dataset. Machine learning algorithms, namely K-means clustering, factor analysis (FA), and linear discriminant analysis (LDA) were employed to deliver a comprehensive geochemical model of copper mineralization in the study area. The identification of trace elements and the predictor composition of copper mineralization, the separation of copper geochemical communities, and the investigation of the geochemical behavior of copper vs. its trace elements were targeted and accomplished. As a result, the elements Ag, Mo, Pb, Zn, and Sn were distinguished as trace elements and predictors of copper geochemical modeling in the study area. Additionally, geochemical anomalies of copper mineralization were identified based on trace elements. Conclusively, the nonlinear behavior of the copper element versus its trace elements was modeled. This study demonstrates that the integration and synchronous use of geostatistical and machine learning methods can specifically deliver a comprehensive geochemical modeling of ore mineralization for prospecting mineral anomalies in metallogenic provinces around the globe.

Geochemical characteristics of natural springs within the Lenglongling fault zone related to the Menyuan Ms 6.9 earthquake on January 8, 2022, NW China

Geofluids from natural springs connect with the crust and/or mantle in many cases, and their geochemical anomalies could be significant for the study on faults activity and even earthquakes. Several natural springs are distributed along the Lenglongling fault zone (LLLFZ) in the northeastern margin of the Tibetan Plateau, where the Ms 6.9 Menyuan earthquake occurred on January 8th, 2022. Based on chemical and isotopic compositions (δD, δ18O, δ13C, and 3He/4He) of water and gas samples, the origin of geofluids and their potential correlation with fault activity even including earthquakes are preliminarily assessed in this paper. The δ13CCO2 values and 3He/4He ratios showed that the gas originating from the crust was associated with the metamorphism of carbonate rocks, whereas the δ18O and δD values of water samples indicated that the natural springs were predominantly infiltrated with precipitations from local mountains ranging 3.7 - 5.5 km in height. Obvious changes of Ca2+ and HCO3− concentrations in SZK spring waters in the surface rupture zones were observed in a short period (about three months) after the main shock, in contrast to those of the GSK springs far from the surface rupture zones. Such variations might be correlated with the stress increase prior to the 2022 Menyuan Ms 6.9 earthquake. The mechanical fracturing of surrounding limestone rocks during the slipping movement of LLLF could facilitate the water-rock interactions. Compared to three-month observations after the main shock, relatively higher concentrations of HCO3− and heavier δ18OH2O values of the LHG springs were also observed in the short-term period. The shallow stored formation water might be squeezed along the cracks and rose to the surface during earthquake tremors, causing a sandblasting water phenomenon.

Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

A mineral prospectivity map (MPM) focusing on gold mineralization in the Larder Lake region of Northern Ontario, Canada, has been produced in this study. We have used the Random Forest (RF) algorithm to use 32 predictor maps integrating geophysical, geochemical, and geological datasets from various sources that represent vectors to gold mineralization. It is evident from the efficiency of classification curves that MPMs generated are robust. The unsupervised algorithms, K-means and principal component analysis (PCA) were used to investigate and visualize the clustering nature of large geochemical and geophysical datasets. We used RQ-mode PCA to compute variable and object loadings simultaneously, which allows the displays of observations and the variables at the same scale. PCA biplots of the Larder Lake geochemical data show that Au is strongly correlated with W, S, Pb and K, but inversely correlated with Fe, Mn, Co, Mg, Ca, and Ni. The known gold mineralization locations were well classified by RF with the accuracy of 95.63 %. Furthermore, partial least squares-discriminant analysis (PLS-DA) model combines 3D geophysical clusters and geochemical compositions, which indicates the Au-rich areas are characterized with low to mid resistivity – low susceptibility properties. We conclude that the Larder Lake-Cadillac deformation zone (LLCDZ) is relatively more fertile than the Lincoln-Nipissing shear zone (LNSZ) with respect to gold mineralization due to deeper penetrating faults. The intersection of the LLCDZ and network of high-angle NE-trending cross faults acts as key conduits for gold endowments in the Larder Lake area. This study innovatively combined multivariate geological, geochemical, and geophysical datasets via machine learning algorithms, which improves identification of geochemical anomalies and interpretation of spatial features associated with gold mineralization.

Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru

Stream sediments record water–rock interactions in tributaries followed by fluid mixing in larger downstream catchments, but it can be difficult to determine the relative contributions of each tributary. A good way to analyze this problem is sediment fingerprinting, which allows researchers to identify the source of sediments within a basin and to estimate the contribution of each source to the watershed. Herein, we developed a workflow using the frequentist model FingerPro v1.3 to quantify the sediment source contribution in a semiarid watershed. We applied an unmixing model algorithm to an ICP-MS geochemical database containing information on 32 elements in 362 stream sediment samples. By modeling the source contributions to these mixed samples, we infer that the main sediment contribution comes from the upper portion of the catchment (61–70%), followed by the middle (21–29%) and lower (8–10%) parts, with geochemical anomalies (As and Cu) being closely related to mining sites. Results from this study can be helpful for future management decisions to ensure a better environment in this semiarid watershed.

Delineation of Integrated Anomaly with Generative Adversarial Networks and Deep Neural Networks in the Zhaojikou Pb‐Zn Ore District, Southeast China

AbstractGeochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert's knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb‐Zn deposit, Southeast China. Three hundred fifty two samples were collected, and each sample consisted of 26 variables covering elemental composition, geological, and tectonic information. At first, generative adversarial networks were adopted for data augmentation. Then, DNN was trained on sets of synthetic and real data to identify an integrated anomaly. Finally, the results of DNN analyses were visualized in probability maps and compared with traditional anomaly maps to check its performance. Results showed that the average accuracy of the validation set was 94.76%. The probability maps showed that newly‐identified integrated anomalous areas had a probability of above 75% in the northeast zones. It also showed that DNN models that used big data not only successfully recognized the anomalous areas identified on traditional geochemical element maps, but also discovered new anomalous areas, not picked up by the elemental anomaly maps previously.

Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data

The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb × Zn/Cu × Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces.

A New Anisotropic Singularity Algorithm to Characterize Geo-Chemical Anomalies in the Duolong Mineral District, Tibet, China

With the increasing exploitation of mineral resources by humans, exploring non-traditional areas for hidden resources such as deep earth and sediment-covered regions has become a significant challenge in the field of mineral exploration. Geochemical data, as a crucial information carrier of geological bodies, serves as one of the direct and effective sources for quantitative analysis of regional geological evolution and mineralization prediction studies. It plays an indispensable role in geographic information system (GIS)-based mineral exploration. Due to the neglect of spatial distribution characteristics and the variability of statistical features with spatial metrics in traditional statistical methods, this paper employs fractal/multifractal and the local singularity analysis to identify geochemical anomalies from background and characterize geochemical distributions associated with porphyry Cu-Au mineralization in the Duolong mineral district, Tibet, China. A novel algorithm for estimating the singularity index, which takes anisotropy into consideration, is proposed and practically applied to the Duolong district. By comparing with the isotropic singularity index, this new method objectively identifies anisotropic geochemical signatures and investigates non-linear behaviors of ore-forming elements, making it more practical and effective in geo-anomaly extraction. Furthermore, the current method is capable of indicating variations in geochemical distributions at different scales through directional arrows marking analytical windows. The summed-up direction of these multi-scale vectors effectively demonstrates migration trends of ore materials at each location within the study area. The new method can pinpoint the location of ore-forming element accumulation and migration directions, unlocking valuable insights from complex datasets. This promises to revolutionize our understanding of how minerals are formed and distributed within the Earth’s crust.

An interpretable attention branch convolutional neural network for identifying geochemical anomalies related to mineralization

Geochemical anomalies play a crucial role in geochemical prospecting as they directly indicate the existence of mineralization. Deep learning algorithms (DLAs) have received increasing attention in the domain of multivariate geochemical anomaly recognition due to their extraordinary ability to extract complex and nonlinear geochemical patterns. However, the black-box nature inherent in DLAs typically prevents one from understanding the potential operational mechanisms behind the recognition procedure, resulting in lack of confidence in the identification results. In this study, visualization techniques and attention mechanisms were integrated into a convolutional neural network (CNN) as an attention branch in order to construct an interpretable attention branch convolutional neural network (ABCNN) model for geochemical anomaly identification. Specifically, the class activation mapping was applied to visualize the spatial features extracted by the model and to identify important regions during model inference. The attention mechanism was further incorporated into a CNN to generate attention maps for enhancing spatial features associated with mineralization and interpreting the recognition results based on attention weights. A case study for identifying multivariate geochemical anomalies linked to Au polymetallic mineralization in the western Henan Province of China was presented to illustrate and validate the feasibility of the ABCNN model. The geochemical anomalies delineated by the ABCNN model coincide well with known gold deposits and ore-controlling geological features. The comparative studies between the ABCNN and a traditional CNN further manifested that the incorporation of an attention mechanism can both effectively improve the model interpretability and performance.

Metallogeny and exploration indicators of Sn and Ag deposits in the Southern Great Xing'an Range, China: Application of knowledge-driven CoDA based on geology and geochemistry

There have been recent breakthroughs in exploration for tin (Sn) and silver (Ag) mineral resources in the Southern Great Xing'an Range, making this region a world-class Sn and Ag metallogenic area. However, thick surface coverage and hidden deposits make exploration difficult, and conventional geological and geochemical prospecting methods have severely limited efficacy. In order to further extract geological and geochemical information on the basis of the existing research, to serve the regional mineral exploration, the knowledge-driven CoDA method is used to identify geochemical associations for inferring matallogenic information. Meanwhile, the fractal C-A model is used to determine geochemical anomalies, the catchment basin geochemical method is used to delineate anomaly boundary. Especially, the data-driven CoDA method is employed for comparison analysis with the knowledge-driven one. As a result, seven groups of compositional balances are proposed and verified, and geological bodies closely related to Sn and Ag mineralization are identified. These occurred in Dashizhai Formation strata, being calcareous rocks and granites, especially A-type granite, which is most closely related to mineralization. Meanwhile, Sn and Ag mineralization areas were identified and found to be consistent with known deposits. Finally, this paper proposes a novel model of regional porphyry Sn–Cu metallogenic systems and delineates exploration targets for Ag–Zn–Pb–Cu deposits and Sn–polymetallic deposits based on the analysis of catchment basins.

Graph inference algorithms as high-performance tools for detecting geochemical anomalies related to mineralization from geochemical exploration data

Geochemical exploration data usually consists of a small number of labeled data (cells containing known deposits) and a large number of unlabeled data (cells containing none of known deposits). How to make full use of a small number of labeled data and a large number of unlabeled data to build a high-performance anomaly detection model for identifying mineralization anomalies from geochemical exploration data is a challenge faced by geochemical exploration practitioners. The graph inference algorithms including label propagation and label spreading can efficiently build high-performance semi-supervised classification models by learning from sparse labeled and abundant unlabeled data in machine learning. For this reason, the two graph inference algorithms were used to build the semi-supervised classification models to detect Fe-mineralization anomalies from stream sediment survey data of 1: 50,000 scale in the Chengde area (Hebei Province, China). The label propagation model and label spreading model were compared with the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. The data processing time of the label propagation algorithm and label spreading algorithm is 64.26 s and 28.21 s, respectively; and the data processing time of the Gaussian mixture model and logistic regression algorithm is 37.79 s and 18.98 s, respectively. The area under the receiver operating characteristic curves (AUCs) of the label propagation model and label spreading model are 0.979 and 0.999, respectively; and the AUCs of the Gaussian mixture model and logistic regression model are 0.800 and 0.817, respectively. Therefore, the data modeling efficiency of the label propagation model and label spreading model is comparable to that of the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. However, the performance of the label propagation model and label spreading model are significantly better than that of the Gaussian mixture model and logistic regression model. It can be concluded that the two graph inference algorithms are potentially useful high-performance tools for detecting mineralization anomalies from geochemical exploration data.

The graph attention network and its post-hoc explanation for recognizing mineralization-related geochemical anomalies

Deep learning algorithms have become a cutting-edge technology for mining geochemical survey data to identify geochemical patterns related to mineralization. Similarities in the origins of the same types of mineral deposits may result in similarities in the observed geochemical anomalies to some extent. However, image-based models have limited ability to model the relationships between samples because fixed-size pixel patches are not connected. Graphs have enormous potential to capture spatial information, which can model the complex nonlinear spatial relationship between vertices and edges and effectively measure the spatial relationships between metallogenic information and geochemical survey sites. The graph-based model considers both labeled and unlabeled data as inputs, allowing the relationships between individual samples to be incorporated into the network. In this study, we used a graph-based model, the graph attention network (GAT), to recognize geochemical anomalies associated with gold mineralization in the Xiaoqinling–Xiong'ershan region of the western Henan Province, China. The GNNExplainer, a method for explaining the predictions of graph-based deep learning tasks, was used to determine the importance of the input features of the trained GAT model. The results indicated that the model regards Bi, Cd, Mn, Zn, and Mo as important geochemical elements for mineralization. Meanwhile, a comparative study of GAT with a convolutional neural network suggests that the graph-based model can be applied to effectively identify mineralization-related geochemical patterns, and geochemical anomalies recognized by GAT are more representative of gold mineralization.

Identifying geochemical anomalies using a new method of Yang Chizhong-spatial scan statistic

Identifying anomalies from geochemical data by modeling of the background and statistical evaluation of anomalies is a major concern in geochemical exploration. This study developed a novel method (namely YangScan) for extracting geochemical anomalies by using Yang Chizhong filtering and a spatial scan statistic. The Yang Chizhong filtering, a progressive filtering method on the principle of weighted moving mean of binomial coefficient, was used to construct the background whose concavity and convexity are consistent with the original data. After removing the background from the original data, a spatial scan statistic based on multidirectional optimization was developed to identify arbitrary shaped and statistically significant anomalies from the residuals. YangScan was tested on both simulated datasets and a stream sediment geochemical dataset collected in the Northwestern Jiaodong Peninsula, Eastern China. The experimental results evaluated by geology consistency, recall, and weights of evidence contrast show that YangScan outperforms the two comparision methods (i.e., the trend surface analysis method and k-nearest neighbor anomaly detector) in identifying weak and irregularly-shaped geochemical anomalies. The distribution of geochemical anomalies linked to Au mineralization identified by YangScan is highly correlated with the trending of known Au deposits and ore-controlling structures. Therefore, YangScan is a powerful tool for identifying geochemical anomalies and can provide a useful reference for mineral exploration.

Surface geochemical anomaly pattern of medium-to-high temperature geothermal systems in South China and its application

The existence of thermal storage will correspondingly increase the temperature of surrounding strata and promote the continuous expansion, volatilization, upward migration, and loss of gas in the strata. As a result, a low-concentration gas field will be formed in the strata above geothermal reservoirs. Geothermal reservoirs could in turn heat formation water and increase the solubility of soluble inorganic salts in the surrounding rocks and the total dissolved solids (TDS) content in the formation water. Since water can strongly wet and permeate strata, the dissolved inorganic salts migrate into upper strata along with water, giving rise to the formation of a high-concentration inorganic salt field in the strata above geothermal reservoirs. A higher geothermal reservoir temperature corresponds to more significant characteristics mentioned above. Therefore, a medium-to-high temperature geothermal system has a surface geochemical anomaly pattern of high inorganic salt concentrations and low gas concentrations (also referred to as the high-salt and low-gas pattern). This pattern is applied to the surface geochemical exploration of the two geothermal fields in Guangdong Province, i.e., the Huangshadong geothermal field in Huizhou City and the Xinzhou geothermal field in Yangjiang City, revealing low-concentration gas fields above both. The application results also show that the exposed thermal spring water in both geological fields has higher concentration of dissolved inorganic salt than the surface water and nearby seawater, forming high-amplitude anomalies on the surface above geothermal reservoirs. These characteristics, as well as the measured temperature at known geothermal wells, verify the validity of the high-salt and low-gas pattern of medium-to-high temperature geothermal systems proposed in this study. Moreover, the high-salt and low-gas pattern proposed predicts three favorable medium-to-high temperature geothermal zones in the surface geochemical exploration of the Shiba Basin near the Huangshadong geothermal field.

Biogeochemical fractal characteristics of trace elements in the Shizhuyuan polymetallic mining area and their significance for prospecting

Shizhuyuan is a world-class W–Sn supergiant polymetallic deposit, with a large area of potential mineralization around it. Samples were taken of Dicranopteris dichotoma, which is widely distributed in the Shizhuyuan mining area. Biogeochemical and Number–Size fractal methods were used to evaluate the biogeochemical characteristics of trace elements in the samples and the feasibility of indicating subsurface mineral deposits. The results reveal that D. dichotoma not only has strong uptake of the active state of metal elements (Zn, Mo, Cd, Sn, etc.) in the soil, but also show the geochemical anomalies of Zn, Fe, Tl, Ag, Pb, U, As, Bi, Cd, Mo and Sn in the study area. The Number–Size fractal method can effectively identify the concentration populations of biogeochemical target elements and obtain convincing anomaly thresholds. Analysis of the metal content of D. dichotoma is an effective tool for biogeochemical exploration of Pb–Zn–U hydrothermal deposits in the study area. Zinc as well as U are target elements, and Tl appears to be an effective pathfinder element for Pb–Zn–U mineralization. In addition, 260–340 m of Line 3 # and 1100–1200 m of Line 8 # are the key areas for future exploration of Pb–Zn–U hydrothermal deposits.

Subsurface fluid flow: A case study from the Indo-Gangetic peripheral foreland basin

Subsurface fluid flow involves migration of fluids from source to surface through a wide range of geologic structures, and thus plays a crucial role in unlocking potential hydrocarbon plays. Such studies have been mainly carried out in the marine environment by means of high-resolution seismic data in unravelling the fluid flow system and remains poorly documented for on-land data. This research attempts to explore the fluid flow activity in onshore petroliferous Indo-Gangetic peripheral foreland basin of India by using high-quality 3D seismic reflection data. Several seismic attributes have been utilised for efficiently describing the patterns of subsurface fluid flow structures. The attribute-responses of fluid flow are fused into a single attribute, called the Fluid Cube meta-attribute, by designing a workflow based on artificial neural network, which has enabled to elucidate the subsurface fluid migration routes. The subsurface fluid flow features are imaged as relatively vertical mounded structure with conical vent-like morphology. Internally, these features are associated with moderately distorted reflections. The reflections at the top are vertically stacked with medium to high amplitudes. The Fluid Cube meta-attribute demonstrates that subsurface fluid vertically migrates from the Neoproterozoic strata through minute fracture networks and weaker strata of the overlying Tertiary succession. The analysis of surficial geochemical anomalies corroborates quite reasonably with these observations. Thus, the Ganga peripheral foreland basin could be considered a promising area with potential leads that can be unlocked for hydrocarbon exploration. The analysis presented through this research could be efficiently carried out over other onshore basins worldwide.

Towards an improved discrimination and correlation of Permian-Lower Triassic sediments in Central Europe: A chemostratigraphic approach

The stratigraphic classification and correlation of Permian-Lower Triassic clastic beds in the Central European Basin System traditionally rely on geophysical, bio-, and lithostratigraphic methods. However, in basin margin areas, the accuracy of these tools is diminished due to the heterogeneous distribution of stratigraphic markers, lack of index fossils, and greater climatic and tectonic disturbance. To overcome these limitations, this study introduces a more versatile, multifaceted and chemostratigraphy-based approach. By integrating results from bulk X-ray fluorescence analysis, sandstone petrography, Raman heavy mineral analysis, lithofacies characterisation and borehole gamma-ray logs, we produce robust multi-stratigraphic schemes for selected sequences in the Franconian Basin (SE Central European Basin System). Hierarchical chemostratigraphic zones, comprising five chemozones and sixteen subzones, were designed using key elemental ratios (Ga/Rb, Rb/K, Na/K, Ca/Mg, Cr/Ti, Th/Ti, Nb/Th, Ti/Nb, P/Ti, Zr/P, Zr/Y), whose changes through time reflect modifications in the mineralogical composition of sedimentary rocks. To ensure precise interpretations of geochemical data, we established links between mineralogical and chemical components through statistical processing and direct comparison with mineralogical assemblages. Our results demonstrate that chemostratigraphic boundaries can identify major changes in palaeo-depositional environments and sediment sources induced by eustatic, tectonic and climatic disturbances. For example, changes in the P/Ti and Zr/P ratios across the CP3-CT1 document the increased abundance in apatite-rich granitic detritus, resulting from the exposure of new source lithologies in the catchment area due to Early Triassic synsedimentary tectonism. Since these perturbations had a regional impact on sedimentation, their geochemical fingerprints are correlated across the basin, regardless of lithofacies variability or distance between sections. Moreover, integrating characteristic geochemical signals into multi-stratigraphic schemes enables more precise placement of stratigraphic boundaries, identification of locally deposited/preserved stratigraphic markers, and overall refinement of outdated classification schemes. The chemostratigraphic approach introduced in this study offers a solution to the limitations of conventional stratigraphic tools, allowing robust characterisations and correlations of Permian-Triassic sequences in the marginal regions of the Central European Basin System. In addition, the multi-analytical workflow for processing geochemical data, refining stratigraphic schemes and attributing geochemical anomalies to environmental perturbations represents a further step towards more precise applications of chemostratigraphy to the depositional record.