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1.
Esmaeiloghli Saeid Tabatabaei Seyed Hassan Carranza Emmanuel John M. 《Natural Resources Research》2021,30(2):989-1010
Natural Resources Research - Recognition and mapping of mineralization-related patterns in geochemical data is a key computational analysis to achieve a predictive model of prospectivity for... 相似文献
2.
A pedogeochemical exploratory survey of gold deposits was carried out in the region of São Sepé (southernmost Brazil). The region comprises a predominantly metamorphosed belt of volcanoclastics, sediments, serpentinites, basalts, gabbros, chert, tuffs, and banded iron formation of the Proterozoic age. The anomalies were identified first by stream sediment heavy mineral survey at the regional scale of exploration. Once spatial continuity was modeled, ordinary block kriging was performed to generate geochemical maps. Indicator block kriging also was used as an alternative in analyzing and interpreting geochemical data. A novel approach is proposed, which combines both ordinary and indicator kriging for delineating geochemical anomalies. Probability maps proved to be appropriate for selecting new sites for further exploration. Gold anomalies in soils trending NE were well defined by geostatistical analysis and subsequently confirmed by drilling. 相似文献
3.
Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods 总被引：2，自引：0，他引：2
Renguang Zuo 《Natural Resources Research》2017,26(4):457-464
Research on processing geochemical data and identifying geochemical anomalies has made important progress in recent decades. Fractal/multi-fractal models, compositional data analysis, and machine learning (ML) are three widely used techniques in the field of geochemical data processing. In recent years, ML has been applied to model the complex and unknown multivariate geochemical distribution and extract meaningful elemental associations related to mineralization or environmental pollution. It is expected that ML will have a more significant role in geochemical mapping with the development of big data science and artificial intelligence in the near future. In this study, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated. More applications are needed to demonstrate the advantage of ML in solving complex problems in the geosciences. 相似文献
4.
Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.
相似文献5.
Geochemical stream sediments survey was conducted in the northwestern part of Wadi Allaqi area, Eastern Desert, Egypt. The
area comprises Precambrian metasediments, intermediate metavolcanics, gabbro, and serpentinites, with intrusive masses of
granites and quartz-porphyry and invaded by several quartz veins. The −1.0-mm size fraction is analyzed for As, Cu, S, Mo,
Pb, Zn, Co, Ni, Rb, Ba, Sr, Nb, V, U, Th, Cr, Zr, La, Ce, Nd, and Y. The geochemical survey is supported by heavy minerals
study in the −0.125 + 0.0625-mm fraction. The geochemical data were statistically investigated using Q-mode cluster and R-mode
factor analyses as well as the enrichment factor. Factors 1 (Zr, Nb, Nd, La, and Y), 2 (V, Sr, and Zn), and 4 (Ba and Rb)
are mainly controlled by the lithological characters of the rocks hosting Au-sulfide mineralizations and their accompanied
hydrothermal alteration zones. In the mineralization Factor 3 (Cu, S, As, Ce, and Mo), arsenic, Cu, S, and Mo are direct indicators,
while Ce is indirect one for the Au-sulfide mineralizations. The Cu–S–As–Mo association with Pb and Zn anomalies in the stream
sediments draining the quartz-porphyry point to its porphyry copper mineralization. Cobalt and Ni (Factor 5) are pathfinders
for the Fe- and Cu-sulfides, whereas Zn and Pb of Factor 8 are additional pathfinders for the Au-sulfide mineralizations.
The southern stream sediments having high U/Th ratios with U–Mo association and draining granites traversed by pegmatites,
as well as the stream sediments draining Um Garayat area and the quartz-porphyry stock with high abundance of monazite, zircon,
epidote, sphene, and ilmenite, could signify sources of U and Th (Factor 7). Two watershed areas have distinct enrichment
factors for arsenic suggesting unexplored extensions of Au-sulfide mineralization linked to the Allaqi shear-zone. The enrichment
of the mineralization Factor 3 in the drainage system is mainly controlled by the prevailed mechanical dispersion for the
hosting heavy minerals in such arid region with minor role of hydromorphic dispersion. The chemistry and mineralogy of the
stream sediments are evidently allied to the drained bedrocks and their hosted mineralizations that signify a promising area
for detailed exploration. 相似文献
6.
Esmaeiloghli Saeid Tabatabaei Seyed Hassan Carranza Emmanuel John M. Hosseini Shahram Deville Yannick 《Natural Resources Research》2021,30(6):3925-3953
Natural Resources Research - This contribution proposes a spatially weighted factor analysis (SWFA) to recognize effectively the underlying mineralization-related feature(s) in geochemical signals.... 相似文献
7.
黄河中下游泥沙通量变化规律 总被引：2，自引：1，他引：1
根据黄河中下游多个主要水文控制站50多年的实测资料,利用水文学、泥沙运动力学、河床演变学和经验统计相结合的分析方法,分析黄河中下游泥沙通量的变化规律。结果表明,黄河中下游泥沙通量递减的同时泥沙粒径变细,人为因素影响巨大;中下游泥沙通量变化具有阶段性、季节性和自相关性。黄河下游河道泥沙冲刷与淤积均具有上段变化大、下段变化小的特点,这是由河道下游上宽下窄、上陡下缓特征决定的;下游河道输沙能力影响因子中,河道水量尤其是汛期水量起主要作用。 相似文献
8.
Natural Resources Research - Mineral exploration targets can be delineated through multivariate analysis. These targets are usually recognized as anomalies in the procedure of data mining using a... 相似文献
9.
Hooshang H. Asadi Atefeh Sansoleimani Moslem Fatehi Emmanuel John M. Carranza 《Natural Resources Research》2016,25(4):417-429
The Salafchegan area in central Iran is a greenfield region of high porphyry Cu–Au potential, for which a sound prospectivity model is required to guide mineral exploration. Satellite imagery, geological geochemical, geophysical, and mineral occurrence datasets of the area were used to run an innovative integration model for porphyry Cu–Au exploration. Five favorable multi-class evidence maps, representing diagnostic porphyry Cu–Au recognition criteria (intermediate igneous intrusive and sub-volcanic host rocks, structural controls, hydrothermal alterations, stream sediment Cu anomalies, magnetic signatures), were combined using analytic hierarchy process and technique for order preference by similarity to ideal solution to calculate a final map of porphyry Cu–Au potential in the Salafchegan area. 相似文献
10.
Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.
相似文献11.
Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information
from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented,
precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS
tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in
exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis
techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral
exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent
data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during
recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential
mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively
analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential. 相似文献
12.
The working group Mathematical Geology of the Freie Universität Berlin was formed in 1971. We review quantitative methods used by the working group since 1983 to treat mineral exploration problems. The methods applied vary from elementary statistical analysis of multivariate exploration data to optimal strategies for selecting favorable targets, and from multiobjective decision-making for additional drill hole locations to expert systems in exploration.The methods applied are directly related to the level of information at each stage of the exploration process. Special emphasis was placed on the relationship between and evaluation of subjective and objective data. Case studies illustrating the various methods are presented for different kinds of mineral deposits and exploration environments. 相似文献
13.
We analysed modern mass‐accumulation patterns on the western Adriatic mud wedge (Italy), an elongated belt of shelf mud formed by coalesced prodeltas of the Adige, Po, and Apennine rivers, as part of an integrated strategy aimed at producing a quantitative sediment budget model for muddy continental shelves sourced by multiple compositionally distinct fluvial systems. Sediment provenance and source‐specific accumulation rates of surface sediments were quantified by combining results of grain‐size analysis and geochemical analysis of specific size fractions with bulk mass accumulation rates. Statistical classification algorithms adapted to compositional data were used to partition the total (geochemical) variation of sediment properties into size‐related and provenance‐specific factors. We identified geochemically distinct fluvial end‐member sediment types in two different grain‐size fractions, which were grouped into sediments derived from the Apennine rivers, and sediments derived from the Po and Adige rivers. Compositional fingerprints (end‐member compositions) of each source area were estimated by taking into account relative rates of fluvial sediment supply from rivers as predicted by numerical modelling. The end members allow us to explain geochemical compositional variation of mud‐wedge surface sediments in terms of provenance and size‐selective dispersal, and map mass accumulation rates of sediments from individual source areas (grain size<63 μm), as well as bulk sand accumulation rates (grain size>63 μm) across the western Adriatic mud wedge. The source‐specific rates of fine‐grained sediment supply derived from geostatistical estimates of mass‐accumulation rates were used to calibrate the numerical model of sediment supply to present‐day conditions. 相似文献
14.
对托素湖沉积岩芯采用高分辨率XRF扫描分析法进行地球化学元素测试,运用多元统计分析法中的相关性分析、聚类分析及因子分析判别出不同沉积组分,揭示了托素湖沉积物中Si、Al、K、Ti、Ca、Sr、S、Cl、Br等元素的地球化学特征及其所指示的环境意义。同属强烈迁移型元素Ca、Sr在沉积物中主要受托素湖中内生碳酸盐的影响;而外源碎屑元素Si、Al、K、Ti主要受托素湖流域侵蚀的控制,其变化受托素湖流域降水量的控制,一定程度上指示了流域的干湿波动和极端气候事件发生的幅度与频率。 相似文献
15.
Venkataraman G. Madhavan B. Babu Ratha D. S. Antony Joju P. Goyal R. S. Banglani S. Roy S. Sinha 《Natural Resources Research》2000,9(1):27-42
This study involves the integration of information interpreted from data sets such as LandsatTM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura—Dariba,Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights ofevidence method and (2) a fuzzy logic algorithm to derive spatial models to target potentialbase-metal mineralized areas for future exploration. Of the 24 layers considered, five layers(graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0–2000 mcorridor (L4-NESW), Cu 200–250 ppm, and Pb 200–250 ppm) have been identified from theBayesian approach on the basis of contrast. Thus, unique conditions were formed based onthe presence and absence of these five map patterns, which are converted to estimate posteriorprobabilities. The final map, based on the same data used to determine the relationships, showsfour classes of potential zones of sulfide mineralization on the basis of posterior probability.In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SWlineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain thefinal potential map showing four classes of favorability index. 相似文献
16.
Structural Analysis for Gold Mineralization Using Remote Sensing and Geochemical Techniques in a GIS Environment: Island of Lesvos,Hellas 总被引：2，自引：0，他引：2
Rokos D. Argialas D. Mavrantza R. St.-Seymour K. Vamvoukakis C. Kouli M. Lamera S. Paraskevas H. Karfakis I. Denes G. 《Natural Resources Research》2000,9(4):277-293
Exploration for epithermal Au has been active lately in the Aegean Sea of the eastern Mediterranean Basin, both in the islands of the Quaternary arc and in those of the back-arc region. The purpose of this study was the structural mapping and analysis for a preliminary investigation of possible epithermal gold mineralization, using remotely sensed data and techniques, structural and field data, and geochemical information, for a specific area on the Island of Lesvos. Therefore, Landsat-TM and SPOT-Pan satellite images and the Digital Elevation Model (DEM) of the study area were processed digitally using spatial filtering techniques for the enhancement and recognition of the geologically significant lineaments, as well as algebraic operations with band ratios and Principal Component Analysis (PCA), for the identification of alteration zones. Statistical rose diagrams and a SCHMIDT projection Stereo Net were generated from the lineament maps and the collected field data (dip and strike measurements of faults, joints, and veins), respectively. The derived lineament map and the band ratio images were manipulated in a GIS environment, in order to study the relation of the tectonic pattern to both the alteration zoning and the geomorphology of the volcanic field of the study area. Target areas of high interest for possible mineralization also were specified using geochemical techniques, such as X-Ray Diffraction (XRD) analysis, trace-element, and fluid-inclusion analysis. Finally, preliminary conclusions were derived about possible mineralization, the type (high or low sulfidation), and the extent of mineralization, by combining the structural information with geochemical information. 相似文献
17.
Most regional geochemistry data reflect processes that can produce superfluous bits of noise and, perhaps, information about the mineralization process of interest. There are two end-member approaches to finding patterns in geochemical data—unsupervised learning and supervised learning. In unsupervised learning, data are processed and the geochemist is given the task of interpreting and identifying possible sources of any patterns. In supervised learning, data from known subgroups such as rock type, mineralized and nonmineralized, and types of mineralization are used to train the system which then is given unknown samples to classify into these subgroups.To locate patterns of interest, it is helpful to transform the data and to remove unwanted masking patterns. With trace elements use of a logarithmic transformation is recommended. In many situations, missing censored data can be estimated using multiple regression of other uncensored variables on the variable with censored values.In unsupervised learning, transformed values can be standardized, or normalized, to a Z-score by subtracting the subset's mean and dividing by its standard deviation. Subsets include any source of differences that might be related to processes unrelated to the target sought such as different laboratories, regional alteration, analytical procedures, or rock types. Normalization removes effects of different means and measurement scales as well as facilitates comparison of spatial patterns of elements. These adjustments remove effects of different subgroups and hopefully leave on the map the simple and uncluttered pattern(s) related to the mineralization only.Supervised learning methods, such as discriminant analysis and neural networks, offer the promise of consistent and, in certain situations, unbiased estimates of where mineralization might exist. These methods critically rely on being trained with data that encompasses all populations fairly and that can possibly fall into only the identified populations. 相似文献
18.
Mahyar Yousefi 《Natural Resources Research》2017,26(4):429-441
In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from using interpolation methods in the modeling of anomalies. The pixel-based method is a discrete field modeling of geochemical landscapes for mapping lithogeochemical anomalies. In this method, the influence area of each composite rock sample is the whole area covered by a pixel where the materials of the sample were taken from. In addition to the pixel-based method, because delineation of mineral exploration target areas using geochemical data is a challenging task, the application of metal zoning concept is demonstrated for vectoring into porphyry mineralization systems. In this regard, different geochemical signatures of the deposit-type sought were mapped in a model. Application of the proposed pixel-based method and the metal zoning concept is a powerful tool for targeting areas with potential for porphyry copper deposits. 相似文献
19.
地貌格局与流域侵蚀产沙过程关系定量分析——以黄河中游河龙区间为例 总被引：2，自引：0，他引：2
以河龙区间42个流域为对象,在流域地貌格局信息提取和侵蚀产沙过程特征指标计算及其相互关系分析的基础上,探讨地貌格局对流域侵蚀产沙过程的影响。结果表明:①在河道系统水平,河流数量、长度等几何特征指标和河流分叉率(Rb12)、分级率(Rd32)、相邻级别间的河流长度比等形状特征指标与流域侵蚀模数显著相关;②在流域系统水平,坡度粗糙度、相对高差、圆度比、高长比是影响流域侵蚀产沙过程的主要指标,其中坡度粗糙度是最根本的解释变量;③各地貌格局因子间相互作用复杂,且对侵蚀过程的影响要强于泥沙输移过程,其通径分析模型对流域侵蚀模数、输沙模数和泥沙输移比变化的解释度分别为65%、33%和20%。这对正确认识影响流域侵蚀产沙过程的格局因素和建立准确的过程模型,具有重要参考价值。 相似文献
20.
Posterior probabilities of occurrence for Zn-Pb Mississippi Valley Type (MVT) mineralization were calculated based on evidence
maps derived from regional geology, Landsat-TM, RADARSAT-1, a digital elevation model and aeromagnetic data sets in the Borden
Basin of northern Baffin Island, Canada. The vector representation of geological contacts and fault traces were refined according
to their characteristics identified in Landsat-TM, RADARSAT-1, DEM, slope, aspect, and shaded relief data layers. Within the
study area, there is an association between the occurrence of MVT mineralization and proximity to the contact of platformal
carbonates and shale units of the adjacent geological formation. A spatial association also tends to exist between mineralization
and proximity to E-W and NW-SE trending faults. The relationships of known MVT occurrences with the geological features were
investigated by spatial statistical techniques to generate evidence maps. Supervised classification and filtering were applied
to Landsat-TM data to divide the Society Cliffs Formation into major stratigraphic subunits. Because iron oxides have been
observed at some of the MVT occurrences within the Borden Basin, Landsat-TM data band ratio (3/1) was calculated to highlight
the potential presence of iron-oxides as another evidence map. Processed Landsat-TM data and other derived geological evidence
maps provided useful indicators for identifying areas of potential MVT mineralization.
Weights of evidence and logistic regression were used independently to integrate and generate posterior probability maps showing
areas of potential mineralization based on all derived evidence maps. Results indicate that in spite of the lack of important
data sets such as stream or lake sediment geochemistry, Landsat-TM data and regional geological data can be useful for MVT
mineral-potential mapping. 相似文献