Geochemical Survey Data Research Articles

Prediction of the fluoride contents of different crop species via the random forest algorithm.

Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators-bioavailable P, bioavailable Zn, leachable Pb, and Sr-were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model's prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency.

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

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

Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification

The identification of geochemical anomalies is crucial in mineral exploration. However, the limited sample size, high-dimensional features, and mixed geochemical information make identifying geochemical anomalies a significant challenge. Machine learning algorithms (MLAs), especially those with spatial and spectrum branches, have been proven to be a high efficiency tools for detecting geochemical anomalies related to mineralization. The spatial branch MLAs take two-dimensional images (pixel-patches) as input and mainly capture the spatial characteristics of geochemical patterns. The spectrum branch MLAs take one-dimensional sequence data (pixels) as input and mainly consider the elemental concentration and assemblies. Simultaneously considering the spatial patterns and geochemical concentrations of the geochemical survey data can mitigate geochemical concentration variations arising from objective factors and amplify subtle mineralization anomalies. This study proposes an unsupervised spatial-spectrum autoencoder (AE), namely dual-AE, which consists of a graph convolutional autoencoder (GCN-AE) and a long short-term memory network autoencoder (LSTM-AE) for geochemical anomalies identification. The spatial branch is constructed using the GCN-AE, which can effectively capture spatial geochemical patterns and extract spatial relationships between neighboring pixels. The spectrum branch consists of an LSTM-AE that can study geochemical elemental assemblies within a single pixel. A key ore-controlling factor was added into the dual-AE as a soft constraint to construct a geologically constrained dual-AE. A case study was conducted to recognize geochemical anomalies associated with iron polymetallic mineralization in Southwest Fujian Province, China. The obtained results demonstrated that (1) the unsupervised spatial-spectrum deep learning algorithm serves as a potent method for detecting geochemical anomalies related to mineralization, (2) the geologically constrained unsupervised spatial-spectrum dual-branch model can improve the accuracy and interpretability of geochemical anomaly identification, and (3) the identified anomalous areas can provide essential clues for further mineral exploration.

Methodology for evaluating soil carbon stock changes based on regional geochemical survey data: A case study in Huangpi, China

Soil carbon pools play a crucial role in the Earth's ecosystem, acting as significant carbon sinks and sources. Through a detailed analysis of soil carbon content using data from the Huangpi District in Wuhan, China, this research employs geochemical survey data, field replicates, and spatial autocorrelation information to establish an assessment model for soil carbon stocks. The model addresses the sources of errors and their effects on carbon pool changes, using both traditional statistical theories and geostatistical models to detect changes in carbon density and estimate carbon sources and sinks with minimized error ranges. Key findings indicate that sampling errors, influenced by small-scale spatial variability, are the primary source of observational inaccuracies in assessing total soil carbon and organic carbon, accounting for over 90% of the variation. Meanwhile, analytical errors are more significant when quantifying soil inorganic carbon content due to its lower concentrations. From 2001 to 2022, no significant changes were observed in the soil organic carbon stock in Huangpi District, while a modest increase in inorganic carbon was noted. The study highlights that increasing sample density beyond a certain threshold does not significantly affect carbon stock estimates or their error ranges, emphasizing the stability of the block kriging method in estimating regional carbon stocks.

Co-enrichment of selenium and cadmium in soils of southern China and its implication for the safe utilisation of selenium-rich lands

Dietary deficiency of selenium (Se) is a global health threat related to low Se concentrations in crops. Southern China has abundant Se-rich land resources, but the cadmium (Cd) pollution problem is prominent, which may lead to Cd exceeding the safety standard in Se-rich crops. Therefore, it is important to delineate Se-rich land without Cd pollution in order to develop green Se-rich agriculture. Based on soil/sediment geochemical survey data covering 2.3 million km2 of southern China, this study analysed the concentration and spatial distribution of Se and Cd, discussed their enrichment mechanisms and proposed suggestions for the safe use of Se-rich land. The results showed that the soil/sediments in southern China were significantly enriched in Se and Cd, and the median values were 0.31 mg·kg−1 and 221 μg·kg−1, which were 1.8 times and 2.5 times the national soil background values, respectively. In addition, a significant positive correlation was observed between Se and Cd, indicating that Cd contamination in Se-rich soils frequently exceeded the pollution limit. According to statistics, Se-rich land accounted for 32.13 % of southern China. However, due to the co-enrichment of Se and Cd, areas without Cd pollution accounted for only one-third of the total Se-rich area. In particular, the areas with co-enrichment of SeCd are primarily distributed in Yunnan, Guizhou, Guangxi, western Hubei, northwestern Zhejiang, western Hunan, southern Hunan and northern Guangdong. The enrichment of Se is predominantly associated with parent rocks (black and carbonate rocks). At the same time, the enrichment of Cd is influenced by the parent rocks and PbZn mineralisation and mining activities. Three recommendations for managing Se-rich land were proposed: imposing restrictions on the utilisation of heavily contaminated Se-rich land for agricultural production, adopting a rational approach towards utilising lightly polluted Se-rich land and actively promoting the development of Se-rich agriculture in uncontaminated Se-rich areas. In the future, it is necessary to develop technologies that simultaneously enhance Se absorption while inhibiting Cd absorption in order to safely exploit Se-rich lands affected by Cd pollution.

Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.

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.

A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration

Geochemical survey data provide rich information on geochemical elemental concentrations and their spatial patterns in relation to mineralization or pollution. A geochemical data cube can be generated using geochemical raster maps which can be obtained by interpolating geochemical samples into raster maps. In these maps, each pixel contains a geochemical spectrum that records the characteristics of a specific location. The spatial structure information that captures the relationships between neighboring pixels and the studied pixel is also visible in these raster maps. Therefore, the simultaneous consideration of the geochemical spectrum and spatial patterns is vital for mining a geochemical data cube for mineral exploration. In this study, a hybrid architecture of two deep learning algorithms, consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN), is proposed to extract joint spectrum–spatial features from geochemical survey data for supporting mineral exploration. A 1DCNN, in which the input data were pixels, was used to model the geochemical spectrum characteristics of the studied pixel. This involved all available geochemical major and trace elements and considered both positive and negative geochemical anomalies. A GCN with input data as graphs was used to capture the spatial patterns related to mineralization. A physically constrained deep learning model was then built by adding geological domain knowledge to the proposed hybrid model to improve the performance and interpretation of the hybrid model. A case study for identifying mineralization-related geochemical anomalies in northwestern Hubei Province, China, was employed to validate the proposed physically constrained hybrid deep learning model. Comparative studies with 1DCNN + GCN, 1DCNN, and GCN, the physically constrained hybrid method can effectively extract and fuse geochemical spectrum and spatial features hidden in geochemical survey data, and perform better in geochemical anomaly recognition.

A high-performance voting-based ensemble model of graph convolutional extreme learning machines for identifying geochemical anomalies related to mineralization

How to effectively identify mineralization anomalies from geochemical survey data is one of the key problems that must be properly solved in geochemical exploration. Although supervised machine learning and deep learning techniques have been adopted to solve this problem, the performance of the established mineralization anomaly identification models is not satisfactory because the mutual relationship between neighborhood data points is not taken into account in the modeling of geochemical exploration data. The graph convolutional extreme learning machines (GCELMs) provide a new technique for modeling the graph data transformed from geochemical exploration data. To improve the performance of mineralization anomaly identification algorithms, the GCELM techniques were adopted to establish the supervised classification models for identifying mineralization anomalies from geochemical exploration data. To improve the robustness of the GCELM models in mineralization anomaly identification, an ensemble classification model called voting-based GCELM (V-GCELM) model was established by voting the outputs of multiple GCELM models. The Baishan area of Jilin Province (China) was taken as the study area for carrying out an experiment of mineralization anomaly identification from geochemical exploration data. The synthetic minority over-sampling technique (SMOTE) was used to alleviate the class-imbalance of the training data. The GCELM model, V-GCELM model and extreme learning machine (ELM) model were established for identifying mineralization anomalies, and their performances were compared in terms of the receiver operating characteristic (ROC) curves, area under the curves (AUCs) and lift indices. The results show that the V-GCELM model has the best performance, and its AUC and lift index are 0.9989 and 28.6, respectively. The GCELM model has the second-best performance, and its AUC and lift index are 0.9977 and 20.1, respectively. The ELM model has the worst but still high performance, its AUC and lift index are 0.9402 and 9.2, respectively. The mineralization anomalies identified by the three models have close spatial correlation with the polymetallic deposits found in the study area, and spatially controlled by the main controlling factors in the study area. Therefore, GCELM and V-GCELM are promising techniques for building the high-performance classification models for identifying mineralization anomalies from geochemical exploration data.

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.

Relationship between spatio-temporal evolution of soil pH and geological environment/surface cover in the eastern Nenjiang River Basin of Northeast China during the past 30 years

Soil pH plays an important role in crop growth and soil fertility. The status, variations and controlling factors of soil pH are of great significance to understand soil acidification and alkalization processes and to make decisions on maintaining soil productivity and ecological functions. In this paper, we analyzed the distribution and spatio-temporal variation characteristics of soil pH in the Nenjiang River Basin and the geochemical driving mechanism of soil pH distribution pattern, based on the National Multi-objective Regional Geochemical Survey data of topsoil, the Second National Soil Survey data and Normalized Difference Vegetation Index (NDVI). The results demonstrated that the topsoil pH had changed from neutral and alkaline levels to acidic and strongly alkaline levels in the past 30 years. NDVI decreased with the increase of soil pH when soil pH > 8.0, and decreased with the decrease of soil pH when soil pH < 5.0. There were significant differences in soil pH with various surface cover types. The weathering products of rocks and minerals rich in K2O, Na2O, CaO and MgO from the Daxing'an Mountains and Xiaoxing'an Mountains entered into the southern low plain and were enriched in different parts by water transportation, river and lake deposition.

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.

Geochemical survey data cube: A useful tool for lithological classification and geochemical anomaly identification

Geochemical survey data play a critical role in geological studies, mineral exploration, and environmental applications by providing information on geological events and processes such as mineralization and pollution. A typical geochemical survey dataset contains the analysis of multiple elements. For example, the National Geochemical Mapping Project of China comprises 39 major and trace element concentrations. Multiple geochemical maps can be generated by interpolating geochemical samples into raster maps to constitute a geochemical survey data cube in which elements are sorted by their atomic numbers. A geochemical spectrum can be created using these geochemical maps in which each pixel that records geochemical characteristics. In this study, a convolutional neural network (CNN) that considers the geochemical spectrum and spatial pattern of geological objects was employed to mine a geochemical survey data cube, aiming of geological mapping and geochemical anomalies identification associated with mineralization in the eastern part of Hubei Province of China. The results showed that (1) a geochemical survey data cube which built based on various geochemical exploration data provided vital information on mineralization process and the formation of geological features; and (2) a CNN had a strong ability to recognize high-level features in the geochemical survey data cube, and it showed excellent performance in mineral exploration and related geological studies.

Multivariate Cross-Validation and Measures of Accuracy and Precision

Cross-validation and performance measures are standard components in the evaluation of a geostatistical model. These are well established in the univariate case, but measures for multivariate geostatistical modeling have not received as much attention. In the case of a single target variable, the univariate approaches remain valid, but in the fully multivariate case where a vector of variables needs to be estimated, the evaluation needs to be based on all estimates simultaneously. An extension of cross-validation and associated performance measures to the fully multivariate case is presented and discussed for the case of regionalized compositions. The method is demonstrated by validating geostatistical models for two case studies: a sample drawn from a geochemical survey data set estimated with cokriging, and an application of direct sampling multiple-point simulation.

Geological Mapping via Convolutional Neural Network Based on Remote Sensing and Geochemical Survey Data in Vegetation Coverage Areas

Geological mapping in vegetation coverage areas is a challenging task. In this study, convolutional neural networks (CNNs) were employed for geological mapping in a vegetation coverage area based on remote sensing images and geochemical survey data. The Gram-Schmidt fusion technology was first applied to fuse Sentinel-2A and ASTER remote sensing images to enhance the spatial resolution and enrich spectral information of remote sensing data. The fused remote sensing images were then organically integrated with geochemical survey data according to the correlations between the geochemical element contents and spectral reflectance of the objects. A case study of mapping six lithologic units in Jilinbaolige, Inner Mongolia, China was implemented using a CNN model based on the fused data. The classification map obtained an overall accuracy of 83.0%, which exhibited a better performance in contrast to random forest (RF) model. The results showed that CNNs can take full advantage of the spatial features of fused data and solve the problems of the ‘salt and pepper phenomenon’ against the shallow machine learning algorithms, and the fusion of remote sensing and geochemical data can provide rich diagnostic information for geological mapping.

Prospecting terrain for surface geochemical exploration of oil and gas in West Siberia

ABSTRACT The relevance of this research is necessitated by the need to advance geochemical prospecting methods for raw hydrocarbon deposits. The study aimed to design new migration models by the interpretation of surface geochemical survey data. In particular, the relationship behavior needs to be identified between the hydrocarbon migration intensity and the presence of oil-bearing reservoirs within the section. To reveal quantitative relationships between the oil show and the oil-bearing capacity of the section, a substantial body of geochemical data across different fields in West Siberia was processed herein. Field-type and in-office operations were being performed by the snow survey procedure. The СН4–С6Н14 saturated hydrocarbons were employed as the major measured parameter. No direct quantitative connection was found between the intensity of surface hydrocarbon anomalies and the section productivity. Nonetheless, the two basic factors were found to affect the structure of the surface geochemical field: first, oil-bearing reservoirs present in the section and, second, fluid-resistant properties of the above-productive strata. Despite a direct quantitative relationship between the oil-bearing capacity of the section and the intensity of surface hydrocarbon anomalies being absent, the large sample made it possible to determine quantitative measures of the surface geochemical field that credibly characterize territories whose sections hold oil-bearing horizons.

Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data

Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data

Electrical resistivity structure of Danau Ranau geothermal prospect area based on integrated 3-D Inversion of impedance tensor and tipper vector

Subsurface electrical resistivity distribution needs to be revealed to explore the geothermal potential in The Danau Ranau prospect area, as the components in geothermal systems could be easily characterized by their resistivity contrasts. The magnetotelluric method is a geophysical method commonly used to perform this task. This paper performs a three-dimensional inversion based on the complete component of magnetotelluric transfer functions, including impedance tensor and tipper vector. Prior dimensionality analysis using phase tensor revealed that the MT data contains a high dimensionality distortion and shows significant horizontal geoelectrical variations. The analysis suggests using the MT full components in the inversion process to effectively recover all the geoelectrical features in such a geologically complex area. The three-dimensional inversion was then carried out after taking control of the model mesh dimension and the regularization parameter. The 3-D resistivity model showed an overburden layer with varying resistivity in the shallow depth. In the subsequent layer, a conductive anomaly representing impermeable cap rock is distributed from the center to the northern and western parts of the study area, bordered by faults and manifestations. The reservoir zone is located under the center of Mount Seminung at an approximate depth of 1500 m. The heat source is expected to be the residual eruption magma of the volcano, which sits far under the reservoir zone and is unreachable by the MT signal. All the findings agree with the existing geological and geochemical survey data and can explain the hydrothermal activities in the Danau Ranau geothermal prospect area.

Geochemical and Petrological Survey in Northern Ethiopia Basement Rocks for Investigation of Gold and Base Metal Mineralization in Finarwa Area and Its Surrounding Southeast Zone of Tigray, Ethiopia

The study is accompanied in northern Ethiopian basement rocks, Finarwa area and its surrounding areas, south eastern Tigray. The objective of the study is to amendment field geology, mineralogy, and geochemical characteristics to deliberate about the gold and base metal mineral potential investigations. From the field observations the geology of the area haven been described and mapped based on mineral composition, texture, structure and colour of both fresh and weather rocks.The ore mineral under microscope are commonly base metal sulphides pyrrhotite, Chalcopyrite, pentilandite occurring in variable proportions. Galena, chalcopyrite, pyrite and gold mineral are hosted in quartz vein. The base metal sulfides occur as disseminated, vein filling and replacement. Geochemical analyses result from inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectrometry (AAS) indicates the threshold of geochemical anomalies is directly related to the identification of mineralization information. From samples stream sediment samples and the soil samples indicated that the most promising mineralization occur in the prospect area are gold(Au), copper (Cu) and zinc (Zn). This is also supported by the abundance of chalcopyrite and sphalerite in some highly altered samples. The stream sediment geochemical survey data shows relatively higher values for zinc compared to Pb and Cu. The moderate concentration of the base metals in some of the samples indicates availability base metal mineralization in the study area requiring further investigation. In addition the sulphide concentrations in the research area are related to different stages of mineralization and are controlled both by structures and lithology. They are well established in the slate-phyllite and metavolcanics along the shear zones and faults as disseminated and vein filling. The rock and soil geochemistry shows significant concentration of gold with maximum value of 0.33ppm and 0.97 ppm in the south western part of the study area.

Effect of structural features of sedimentary cover on gas composition of permafrost table in northern West Siberia

Here, we analyzed geochemical indices of gases from core samples of permafrost table and evaluated data acquired from electric survey at the field of the Nadym-Pursk petroleum-bearing region. The Nadym-Pursk region is located in the northern West Siberia that is presently the major object of raw hydrocarbon (HC) exploration. The geologic exploration practice has demonstrated that the seismic survey efficiency is notably influenced by heterogeneous rocks of permafrost table, especially within permafrost distribution zones. The exploration efficiency for oil and gas can be enhanced using data from surface geochemical surveys that rely on vertical HC migration from deposits. The adequate interpretation of both seismic and geochemical survey data requires insights on the structure of permafrost table and on processes occurring therein. This study aimed to explore if geologic structural features of the territory affect the composition of gases dispersed in the permafrost table and identify the role of non-geological factors. The gas compositional features of the permafrost table were associated with HC migration channels, frost mounds, and probable gas hydrates within the permafrost distribution zone. The distribution of deep (He and H2) and catagenic (light methane homologues) gases stems from tectonic activity. This conclusion was made from the isotopic assay of samples from several holes, from the correlation between methane and homologues, from the presence of migration channels as per electric survey data, and essentially from the presence of deep gases. The enhanced content of methane is due to its active migration from both diagenetic zone (biogenic methane) and Cenomanian gas pay zones, which along with the respective geotemperature regime of subpermafrost strata is a favorable factor for gas hydrate generation. The natural connections between data from frost mounds point to their distinct genesis: the compositional genesis of gases is influenced by deep degassing and gas accumulation from diagenetic zones.