History and evaluation of national-scale geochemical data sets for the United States

Six national-scale,or near national-scale,geochemical data sets for soils or stream sediments exist for the United States.The earliest of these,here termed the ＇Shacklette＇ data set,was generated by a U.S. Geological Survey（USGS） project conducted from 1961 to 1975.This project used soil collected from a depth of about 20 cm as the sampling medium at 1323 sites throughout the conterminous U.S.The National Uranium Resource Evaluation Hydrogeochemical and Stream Sediment Reconnaissance（NUREHSSR） Program of the U.S.Department of Energy was conducted from 1975 to 1984 and collected either stream sediments,lake sediments,or soils at more than 378,000 sites in both the conterminous U.S.and Alaska.The sampled area represented about 65%of the nation.The Natural Resources Conservation Service（NRCS）,from 1978 to 1982,collected samples from multiple soil horizons at sites within the major crop-growing regions of the conterminous U.S.This data set contains analyses of more than 3000 samples.The National Geochemical Survey,a USGS project conducted from 1997 to 2009,used a subset of the NURE-HSSR archival samples as its starting point and then collected primarily stream sediments, with occasional soils,in the parts of the U.S.not covered by the NURE-HSSR Program.This data set contains chemical analyses for more than 70,000 samples.The USGS,in collaboration with the Mexican Geological Survey and the Geological Survey of Canada,initiated soil sampling for the North American Soil Geochemical Landscapes Project in 2007.Sampling of three horizons or depths at more than 4800 sites in the U.S.was completed in 2010,and chemical analyses are currently ongoing.The NRCS initiated a project in the 1990s to analyze the various soil horizons from selected pedons throughout the U.S.This data set currently contains data from more than 1400 sites.This paper（1） discusses each data set in terms of its purpose,sample collection protocols,and analytical methods;and（2） evaluates each data set in terms of its appropriateness as a national-scale geochemical database and its usefulness for nationalscale geochemical mapping.

Identifying Pathfinder Elements for Gold in Multi-Element Soil Geochemical Data from the Wa-Lawra Belt, Northwest Ghana: A Multivariate Statistical Approach

A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.

Comparative Study and Spatial-Temporal Distribution of Regolith and Rock Geochemical Data from Xingmeng-North China

1 Introduction Geochemical mapping at national and continental scales continues to present challenges worldwide due to variations in geologic and geotectonic units.Use of the proper sampling media can provide rich information on

Development and application of geochemical data processing system based on fractal theory

GC-GIS system is a geochemical data processing system based on fractal theory. The system realized quantity statistics function by calling Surfer and MapInfo software, and it is compiled with Visual Basic language. This system is designed to integrate the functions both quantity statistics of Surfer and spatial data management of MapInfo. A new algorithm of fractal is added up to GC-GIS. Taking example for Weichang region of Hebei to test the system, the processing results show that the model can match the real distribution of mine well.

The Study of Area-Concentration Fractal Method in Litho-Geochemical Data in Tanurjeh Area, Khorasan Province

Given the scientific progresses as well as the invention of new methods in exploration, it is necessary to conduct some re-investigations in several exploration zones. So, in the present research, geochemical data on Tanurjeh exploration zone, (located in Northern Neishaboor, Khorasane Razavi province) is studied by using some modern statistical methods. Fractal methods are appropriated to study and separate the grades societies in deposits. In this article, litho-geochemical analysis results (ICP) are processed by concentration area fractal method (CA). The distribution diagrams related to the statistical populations are drawn, and anomaly populations of Copper, Gold and Molybdenum are determined besides previous studies (petrography and alteration), the results of statistic methods (CA) and aid presence of the porphyry system in depth.

Deriving big geochemical data from high-resolution remote sensing data via machine learning:Application to a tailing storage facility in the Witwatersrand goldfields

Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.

Application of Wavelet Analysis toInterference Elimination for Geochemical Hydrocarbon Exploration

Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.

Comparison of results analyzed by Chinese and European laboratories for FOREGS geochemical baselines mapping samples

Geochemical subsoil data obtained from China and European laboratories have been compared in this study. 787 C horizon subsoil samples from FOREGS （Forum of European Geological Surveys） geochemical baselines mapping project were sent to China＇s IGGE （Institute of Geophysical and Geochemical Exploration） laboratory and composited to 190 samples according to the 160 kin x 160 km GNT （Global Terrestrial Network） cells. In addition to the FOREGS elemental analysis package, Au, Pt, Pd, B, Ge, Br, CI, Se, N, Li and F were also analyzed by using the IGGE＇s 76 element analytical scheme. Geochemical data statistics, scatter plotting, and geochemical map compilation tech- niques have been employed to investigate differences between FOREGS and IGGE analytical results. The results of two datasets, the IGGE＇s analysis data for composited samples, and the FOREGS average data of samples in each GNT cell, agree extremely well lor about 23 elements, viz： SiO2, St, Al2O3, Zr, Ba, Fe2O3, Ti, Rb, Mn, Gd, CaO, Ga, MgO, P, Pb, Na2O, Y, Th, As, U Sc, Cr, and Co. There are slight differences between-laboratory biases shown as proportional errors between the datasets for Ni, K2O, Tb, Tl, Cu, S, Sin, La, Ce, Pr, Nd, Eu, Ho, Er, Tin, Yb, Lu, Ta, Nb, HE and Dy. For Cd, Cs, Be, Sb, In, Mo, I, Sn, and Te, the correlation of the two datasets and the similarity of the geochemical maps are fairly good, but obvious biases exist between the two datasets at values near detection limits.

A Study of the Method for the Recognition of Anomalies in Geochemical Hydrocarbon Exploration

The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomalies from lateral anomalies controlled by faults; and (3) how to eliminate interferences. These uncertainties are serious obstacles for the wide acceptance and use of geochemical techniques in hydrocarbon exploration. In this paper, the features of hydrocarbon anomalies were analyzed based on the micro migration mechanisms. In most cases, there are two anomalous populations or point groups, which are produced by two distinct mechanisms: (1) a population that directly reflects oil and gas fields, and (2) one that is related to structures such as faults. Statistical studies show that background anomalous populations and the boundaries between them can be described by the population means, prior probabilities, which are the proportions of population sizes, and covariance matrices, when background and anomalous populations have normal distributions. When this normality condition is met, a series of formulas can be derived. The method is designed on the basis of these allows: (1) univariate anomaly recognition, (2) elimination of interferences, (3) multivariate anomaly recognition, and (4) multivariate anomaly combination which depicts a more representative picture of morphology of the anomalous target than individual anomalies. The univariate and multivariate anomaly recognition can not only separate anomalies from background objectively, but also simultaneously distinguish the two types of anomalies objectively. This method was applied to the hydrocarbon data in Yangshuiwu region, Hebei Province. The interferences from regional variation of background were eliminated, and the interpretation uncertainty was reduced greatly as the anomalous populations were separated. The method was also used in Daxing region within the confines of Beijing City, and Aershan and Jiergalangtu regions in Inner Mongolia.

Application of fractal content-gradient method for delineating geochemical anomalies associated with copper occurrences in the Yangla ore field,China

Fractal and multi-fractal content area method finds application in a wide variety of geological,geochemical and geophysical fields.In this study,the fractal content-gradient method was used on1：10,000 scale to delineate geochemical anomalies associated with copper mineralization.Analysis of geochemical data from the Yangla super large Cu-Pb-Zn polymetallic ore district using the fractal content-gradient method,combined with other geological data from this area,indicates that oreprospecting in the ore district should focus on Cu as the main metal and Pb-Zn and Au as the auxiliary metals.The types of deposits include（in chronological order） re-formed sedimentary exhalative（SEDEX）,skarns,porphyries,and hydrothermal vein-type deposits.Three ore-prospecting targets are divided on a S-N basis：（1） the Qulong exploration area,in which the targets are porphyry-type Cu deposits;（2） the Zongya exploration area,where the targets are porphyry-type Cu and hydrothermal vein-type Cu-Pb polymetallic deposits;and（3） the Zarelongma exploration area,characterized mainly skarn-type ＂Yangla-style＂ massive sulfide Cu-Pb deposits.Our study demonstrates that the fractal content-gradient method is convenient,simple,rapid,and direct for delineating geochemical anomalies and for outlining potential exploration targets.

High-Resolution Geochemical Significance of Lowest Triassic at Majiashan Section, Chaohu, Anhui Province, China

The Permian Triassic boundary (PTB) and the lowest Triassic in the Yangtze region are considered to be the sediments of dysaeroxic and even anoxic environments, due to the dark thin bedded fine deposits, the highly developed parallel beddings with pyrites, the suppression of bio disturbance, and the monotonous fossils. However, the trace fossils there show a rather weak effect of the anoxic event. Meanwhile, the high resolution geochemical data are analyzed with 2 cm interval in the PTB and the lowest Triassic at the Majiashan Section, Chaohu, Anhui Province. The results show that the water depth of Chaohu region in the earliest Triassic was shallow, which might be a feature of the neritic environment. The high resolution geochemical proxies for anoxia have some contrary results. The geochemical data often indicate the dysaeroxic and even anoxic environments during that time, whereas other proxies (such as w (V)/ w (Cr), w (Ni)/ w (Co)) denote that they are normal marine sediments.

An ice-ocean model study to explore climate change mechanisms in comparison with interannual-to-decadal variability of geochemical tracers

One way to identify the mechanisms that are crucial to Arctic climate change is to use existing data that exhibit interannual-to-decadal variability in the sea ice and ocean interior due to atmospheric forcing. Since around 1960s, valuable geochemical data of the ocean interior, together with atmospheric and sea ice data, have been analyzed and examined in a coupled ice-ocean model with an idealized configuration of the Arctic Basin. This is fundamentally driven by negative salt flux, in addition to atmospheric circulation and cooling. This strategy has a clear advantage over more sophisticated models with higher resolution that require extensive data collections for verification. Around 1990, the dominant atmospheric mode shifted from the Northern Annular Mode （NAM） to the Arctic Dipole Mode （ADM）. The variability of sea ice cover was explained by these two modes sequentially and reproduced in the model. In particular, the geochemical fields indicated a movement of the Transpolar Drift Stream due to the NAM and an oscillation of the Pacific water between the Atlantic and Pacific sides due to the ADM. Both these features were reproduced reasonably well by the oceanic tracers in the model, including the time lags of about one third of the oscillation periods. Thus, this strategy can suggest methods and locations for monitoring oceanographic responses to Arctic climate change.

Bathymetric and Geochemical Analysis of Lake Al-Saad, Abha, Kingdom of Saudi Arabia Using Geoinformatics Technology

This study investigates the potential for remote sensing of lake water bathymetry and geochemical by 1) examining the empirical based technique for retrieving depth information from passive optical image worldview-2 satellite data, 2) performing atmospheric correction, 3) assessing the accuracy of spectrally based depth retrieval under field condition via field measurement, 4) producing bathometry and geochemistry mapping by examining spectral variations for identifying pairs of wavelengths that produce strong linear correlation coefficient between the band ratio. The results indicate that optical remote sensing of bathymetry and geochemical investigation is not only feasible but more accurate under conditions of typical lake water, supporting field survey. The Pearson correlation matrix (R) between the examined water samples/depth and the TOA reflectance values of the worldview-2 (WV-2) satellite data have been investigated and found good correlation. The models developed using the combination of different band pairs also show high accuracy. Cartographical maps were generated depending on the linear correlation coefficient between the measured parameters and the TOA reflectance values of the worldview-2 data. The investigation shows that dissolved oxygen (DO) of the lake water is slight lower than the permissible limit of Saudi standards for lake water. The shallow water has high DO concentration, whereas the deeper shows significantly lower down. Electrical conductivity measurements serve as a useful indicator of the degree of mineralization in the water sample. All the samples which have EC exceed limit. The spatial distribution of EC and TDS inferred that the EC and TDS concentration is the highest at the eastern part of the lake whereas concentration drops down towards the southern side. This study confirms that remote sensing incorporated with GIS and GPS could afford an integrated scheme for mapping water quality and bathometry of the surface water.

Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns

In exploration geochemistry,advances in the detection limit,breadth of elements analyze-able,accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas.While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data,especially where modern data is considerably different than legacy data,it is an expensive exercise.The risk associated with modernizing such legacy data lies within its uncertainty in return(e.g.,the possibility of new discoveries,in primarily greenfield settings).Without any advanced knowledge of yet unanalyzed elements,the importance of re-analyses remains ambiguous.To address this uncertainty,we apply machine learning to multivariate geochemical data from different regions in Canada(i.e.,the Churchill Province and the Trans-Hudson Orogen)in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses.Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data(e.g.,prospectivity mapping).Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.

基于水系沉积物地球化学背景特征的生态环境评价

本文通过对额尔古纳地区1∶25万区域化探数据分析研究,探讨用水系沉积物地球化学背景特征评价研究区生态环境。参照生态环境地球化学评价方法,对研究区的生态环境进行评价,得出研究区的生态环境总体较好,局部存在轻度至重度污染,并重点圈定了F、As元素土壤污染区域。特征植物营养元素相互间搭配平衡关系较差,说明该区的土壤环境不能很好地为植物生长提供适宜的营养补给条件。测区生命必需元素均在正常范围之内,适宜人类生活和工作。综合分析认为,造成研究区内污染状况的原因可能与矿业开采、人类活动及地质背景等因素有关。区域化探数据结合生态环境地球化学评价方法可以应用于内蒙古生态环境评价。

地球化学数据库管理平台的建设与思考

为更好地发挥地球化学数据及方法在异常分析及震情跟踪研判中的作用,提高地球化学数据获取的便利性及数据存储的可靠性,规范异常核实及流动观测等地球化学样品的采集及检测流程,实现地球化学数据的自动分析及相关图件的自动绘制,中国地震台网中心建立了基于Linux系统的地球化学数据库管理平台,该平台应用数据库、地理信息系统(GIS)、WebGIS等技术,集成了样品送检、实验室检测、报告产出、数据存储筛选下载分析、图件绘制等功能,利用Piper图、Na-K-Mg三角图、同位素分析等方法,分析不同样品的地球化学特征,并给出相关图件。平台自推广以来,收集了大量的地球化学数据,提升了数据的可利用性,降低了地球化学数据分析的技术门槛,在异常性质分析及震情跟踪研判中发挥了作用。

基于移动GIS的地球化学野外采集系统的设计与实现

在传统地球化学野外调查工作中,尤其在样品数量多、自然环境恶劣的工区,采集工具携带不便、内业数据整理过程繁琐势必会影响地球化学野外调查的质量和精度。地球化学野外调查的信息化和智能化是时代的要求,更是方法技术发展的必然趋势。本文综合移动GIS技术、数据库等技术,映照地球化学野外调查工作的流程,基于ArcGIS和Android平台研发了一套涵盖工作布置、野外采集以及质量检查等功能的地球化学野外调查软件。系统实现了地球化学野外调查全流程的信息化和智能化,改变了地球化学野外数据的采集方式,节省了一线野外工作的时间,提高了内业数据整理的效率以及野外调查信息的质量和精度,推动了地球化学野外调查的数字化进程。

Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms （the hierarchical, k-means, and fuzzy cmeans algorithms） and six data transformation methods （the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT） are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of columnor variable-based （R-type） cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based （Q-type） cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy cmeans clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

地质矿产勘查中的综合地球化学分析技术

地球化学分析技术在有色金属矿产勘查中发挥核心作用,通过精准定位矿体及评估资源量,显著提高了勘探的经济效益与效率。分析了该技术在多种有色金属资源开发中的应用,涵盖采样、分析技术与数据处理等关键环节。文章强调现代仪器与技术在提升数据解释精度方面的贡献,支持矿业公司在复杂地质条件下做出精准的开采决策。此外,探讨了技术进步如何助力行业面对挑战,优化资源配置,并推动矿产资源的可持续开发。