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.

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.

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.

Identification of the Original Tectonic Setting for Oceanic Andesite Using Discrimination Diagrams:An Approach Based on Global Geochemical Data Synthesis

Systematical analyses of data from GEOROC and PetDB database show that large amount of Cenozoic andesites occurred in the various oceanic environments such as mid-oceanic ridge,plumerelated island and oceanic arc.In this study,we employed the geochemical data of 351 mid-ocean ridge andesites(MORA),2539 plume-related andesites(PRA)and 3488 oceanic arc andesites(OAA)from the database to discuss the relationship between andesite tectonic settings and their geochemical features,thereby making an attempt to construct tectonic discrimination diagrams.Based on the data-driven pattern,all available elements were employed to derive logratios for the possible coordinates,and the overlap-rate calculation was adopted to evaluate the discrimination effect of more than 330000 prospective diagrams.Finally,four tectonic discrimination diagrams have been successfully established to identify MORA,PRA and OAA,which can be utilized to identify the original settings of andesite with an age range from Cenozoic to Archean a certain extent.Of these diagrams,PRA is mainly distinguished by high LREE/HREE ratio due to enriched mantle source.Whereas,OAA is mainly characterized by high LILE/HFSE ratio,which reveals that fluids derived from subducted slab play an important role in forming oceanic arc andesites.Consequently,the petrogenesis of andesites is closely related to their tectonic settings.However,it should be noted that those andesites formed in both continental and oceanic environments cannot be effectively distinguished using these diagrams.We strongly recommend integrating the discrimination diagrams result with other geological information to reach a comprehensive interpretation of evolution history with those ancient andesites.This paper presents a case study which suggests that data-driven method is a powerful tool for solving geological problems in this’big data’era.

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.

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.

Geochemical Variations of the Late Paleozoic Granitoids from the Baolidao Arc-Accrection Belt in Southeastern Segment of Central Asia Orogenic Belt:Implications for Tectonic Transition from Early Carboniferous to Early Permian

Compositional changes in successively erupted felsic rocks can be used to infer physical changes in lower crustal conditions and to enhance the understanding of the tectonic regime.This study presents geochronological,geochemical and isotopic data for two I-type granitic plutons in the Sonid Left Banner of the Central Asian Orogenic Belt.Our new data,together with compiled I-type granitoid data,reveal the presence of magma compositional transition at~305 Ma in the Baolidao arc-accretion belt.The early stage granitoids(330-305 Ma)are medium-K calc-alkaline with higher Sr/Y ratios.The late stage granitoids(305-270 Ma)are high-K calc-alkaline with lower Sr/Y ratios.The two-stage granitoids have roughly similar predominately positive Sr-Nd-Hf isotope values,but with a decreasing trend from the early to late stages.Geochemical data indicate that the early stage granitoids were generated by dehydration melting of juvenile mafic crust at amphibole-dominated depths.In contrast,the late stage granitoids were produced by dehydration melting of a mixed lithology containing juvenile K-rich mafic lower crust and supracrustal materials at the plagioclase-stable crustal level.We propose that the compositional transition of these granitoids can be linked with different slab behaviors of the northward subducting Paleo-Asian oceanic plate,and also with the back-arc tectonic settings.