Three-dimensional Modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

Metallogenic Correlations for the Fe-Nb-REE Mineralization in the West Mine of the Bayan Obo Deposit, Inner Mongolia, China

The West Mine of the Bayan Obo deposit, located in the northern-central part of Inner Mongolia, China, is enriched in Nb, rare earth elements and iron （Nb-REE-Fe） mineral resources. This paper presents a combined method to explore metallogenic correlation of the Nb-REE-Fe mineralization at the Bayan Obo West Mine. The method integrates factor analysis and Back Propagation （BP） neural network technology into processing and modeling of geological data. In this study, the Nb and REE contents of samples were transformed into discrete values to analyze the correlations among the metallogenic elements. The results show weak mineralization correlations between Nb and REEs. Nb and U are closely related in the geochemical patterns, while Fe is closely related to both Th and Mn. LREEs are an important factor for the mineralization of the Bayan Obo deposit, while Fe and Nb can be considered as the results of passive mineralization. On the basis of a metallogenic correlation analysis, the factors affecting the Fe-REE-Nb mineralization were extracted, and the Nb mineralization model was established by the BP neural network. Based on the BP neural network data computing, the variability of the Nb concentration displays a coupled multi-factor nonlinear relationship, which can be used to reveal the inherent metallogenic elemental regularities and predict the degree of element mineralization enrichment in the mining area.

Application of GIS in Mineral Resource Prediction of Synthetic Information

This paper introduces the formation mechanism and synthetic information prediction of large and superlarge deposits in Shandong Province by analyzing and studying on the GIS platform. The authors established a prospecting model of synthetic information from large and superlarge gold deposit concentration region, and the multi-source spatial database from concentration region of deposits and anomalies. On the basis of the spatial database, a target map layer, a model map layer and a predictive map layer were set up. Based on these map layers, geological variables of the model unit and predictive unit were extracted, then launched location and quantitative prediction of the gold deposit concentration region. The achievement of predicting large and superlarge deposits by the GIS platform has enabled the authors to design automation (or semi-automatic) interpretation subsystems, namely geophysics, geochemistry, geologic prospecting and comprehensive prognosis, and a set of the applicable GIS software for mineral resources prognosis of synthetic information.

Genetic Modeling of GIS-Based Cell Clusters and Its Application in Mineral Resources Prediction

This paper presents a synthetic analysis method for multi sourced g eo logical data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been sta tistical analysis of cells delimitated based on thoughts of random sampling. Tha t might lead to insufficient utilization of local spatial information, for a cel l is treated as a point without internal structure. We now take “cell clusters ”, i. e. , spatial associations of cells, as basic units of statistics, thus th e spatial configuration information of geological variables is easier to be dete cted and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi discriminating model for the clusters via genetic algor ithm. Both the right judgment rates and the in class vs. between class distan ce ratios are considered to form the evolutional adaptive values of the populati on. An application of the method in gold mineral resources prediction in east Xi njiang, China is presented.

Theory and Method of Mineral Resource Prediction Based on Synthetic Information

Metallogenic prognosis of synthetic information uses the geological body and the mineral resource body as a statistical unit to interpret synthetically the information of geology, geophysics, geochemistry and remote sensing from the evolution of geology and puts all the information into one entire system by drawing up digitalized interpretation maps of the synthetic information. On such basis, different grades and types of mineral resource prospecting models and predictive models of synthetic information can be established. Hence, a new integrated prediction system will be formed of metallogenic prognosis (qualitative prediction), mineral resources statistic prediction (determining targets) and mineral resources prediction (determining resources amount).

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

Using Remote Sensing Data to Delineate the Lineaments for Hydrothermal Mineral Prediction in Heqing Area, Northwest Yunnan Province, China

Lineament extraction and analysis is one of the routine work in mapping medium and large areas using remote sensing data, most of which are satellite images. Landsat Enhanced Thematic Mapper （ETM） of 945×1 232 pixels subscene acquired on 21 March 2000 covering the northwestern part of Yunnan Province has been digitally processed using ER Mapper software. This article aims to produce lineament density map that predicts favorable zones for hydrothermal mineral occurrences and quantify spatial associations between the known hydrothermal mineral deposits. In the process of lineament extraction a number of image processing techniques were applied. The extracted lineaments were imported into MapGIS software and a suitable grid of 100 m×100 m was chosen. The Kriging method was used to create the lineament density map of the area. The results show that remote sensing data could be useful to extract the lineaments in the area. These lineaments are closely correlated with the faults obtained through other geological investigation methods. On comparing with field data the lineament-density map identifies two important high prospective zones, where large-scale deposits are already existing. In addition the map highlights unrecognized target areas that require follow up investigation.

Quantitative Prediction of Concentrated Regions of Large and Superlarge Deposits in China

Identification and quantitative prediction of large and superlarge mineral deposits of solid mineral resources using the mineral resource prediction theory and method with comprehensive information is carried out nationwide in China at a scale of 1∶5 000 000. Using deposit concentrated regions as the model units and concentrated mineralization anomaly regions as prediction units, the prediction is performed on GIS platform. The technical route and research method of locating large and superlarge mineral deposits and principle of compiling attribute table of independent variables and functional variables are proposed. Upon methodology study, the qualitative locating and quantitative predicting mineral deposits are carried out with quantitative theory Ⅲ and characteristic analysis, respectively, and the advantage and disadvantage of two methods are discussed. This research is significant for mineral resource prediction in ten provinces of western China.

Expert System for Statistical Prediction of Mineral Deposits on Middle and Large Scales

The expert system for statistical prediction of mineral deposits on middle and large scales takes the system of scientific exploration theories, criteria and methods proposed by Professor Zhao Pengda as the field expert knowledge. At present the developed system focuses on two aspects: synthetic exploration and quantitative exploration. Among the three basic theories for the prediction of deposits, it highlights the applications of seeking anomaly theory. This system is characteristic in the determination of geological background, the study of geological anomalies and the delineation of geological background, the study of geological anomalies and the delineation of mineralization anomalies. The system combines closely the knowledge base, method base and database .integrates the input and output information of multi - sources and mul-ti - variables , data , graphs and imagine processing system and inquiring system as a whole . So the system can meet in general all kinds of demands in statistical prediction of mineral deposits . Since the statistical prediction of mineral resources is a kind of systematic engineering pro ject , a further study should be carried out on the fields of theoretical exploration and ster eo - exploration on the basis of unceasingly perfecting the above-mentioned fields in order to establish a comprehensive intelligent system for scientific exploration , to provide new methods , new techniques and new ideas for fast prospecting appraisal of mineral resources .

GIS Predictive Model for Producing Hydrothermal Gold Potential Map Using Weights of Evidence Approach in Gengma Region, Sanjiang District, China

Gengma region, Sanjiang district is known to have some large-scale gold deposits. GIS predictive model for hydrothermal gold potential was carried out in this region using weights of evidence modeling technique. Datasets used include large-scale hydrothermal gold deposit records, geological, geophysical and remote sensing imagery. Based on the geological and mineral characteristics of areas with known gold occurrences in Sanjiang, several geological features were thought to be indicative of areas with potential for the occurrence of hydrothermal gold deposits. Indicative features were extracted from geoexploration datasets for use as input in the predictive model. The features include host rock lithology, geologic structures, wallrock alteration and associated (volcanic-plutonic) igneous rocks. To determine which of the indicative geological features are important spatial predictors of area with potential for gold deposits, spatial analysis was done through the modeling method. The input maps were buffered and the optimum distance of spatial association for each geological feature was determined by calculating the contrast and studentized contrast. Five feature maps were converted to binary predictor patterns and used as evidential layers for predictive modeling. The binary patterns were integrated in two combinations, each of which consists of four patterns in order to avoid over prediction due to the effect of duplicate features in the two structural evidences. The two produced potential maps define almost similar favorable zones. Areas of intersections between these zones in the two potential maps placed the highest predictive favorable zones in the region.

A Preliminary Review of the Metallogenic Regularity of Nickel Deposits in China

The nickel deposits mainly distributed in 19 provinces and autonomous regions in China are 339 ore deposits/occurrences, including 4 super large-scale deposits, 14 large-scale deposits, 26 middle- scale deposits, 75 small-scale deposits, and 220 mineralized occurrences. The prediction types of mineral resources of nickel deposits are magmatic type, marine sedimentary type and regolith type. The formation age is from the Neoarchean to the Cenozoic with two peaks in the Neoproterozoic and the late Paleozoic. The nickel deposits formed in the Neoproterozoic are located on the margin of the North China Block and Yangtze Block, and those formed in the late Paleozoic are mainly distributed in the Central Asian Orogenic Belt （CAOB）, Emeishan and the Tarim Large Igneous Provinces （LIPs）. Magmatic nickel deposits are mainly related with broken-up continental margin, post-collision extension of the orogenic belt and mantle plume. According to different tectonic backgrounds and main characteristics of magmatism, the Ni-Cu-Co-PGE metallogenie series types of ore deposits related with mantle-derived mafic-ultramafic rocks can be divided into 4 subtypes： （1） the Ni-Cu-Co- PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in the broken-up continental margin, （2） the Ni-Cu-Co-PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in mantle plume magmatism, （3） the Ni-Cu-Co- PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in the subduction of the orogenic belt, and （4） the Ni-Cu-Co-PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in post-collision extension of the orogenic belt. We have discussed in this paper the typical characteristics and metaliogenic models for Neoproterozoic Ni-Cu-（PGE） deposits related with broken-up continental margin, Cambrian marine sedimentary Ni-Mo-V deposits related with black shale, early Permian Ni-Cu deposits related with post-collision extension of the orogenic belt, late Permian Ni-Cu-（PGE） deposits related with Large Igneous Provinces （LIPs）, and Cenozoic Ni-Au deposits related with regolith. The broken-up continental margin, mantle plume and post-collision extension of the orogenic belt are important ore- forming geological backgrounds, and the discordogenic fault, mafic-ultramafic intrusion, high MgO primitive magma （high-MgO basaltic magma）, deep magmatism, sulfur saturation and sulfide segregation are 6 important geological conditions for the magmatic nickel deposits.

Aspects of Regional and Worldwide Mineral Resource Prediction

The purpose of this contribution is to highlight four topics of regional and worldwide mineral resource prediction:(1)use of the jackknife for bias elimination in regional mineral potential assessments;(2)estimating total amounts of metal from mineral potential maps;(3)fractal/multifractal modeling of mineral deposit density data in permissive areas;and(4)worldwide and large-areas metal size-frequency distribution modeling.The techniques described in this paper remain tentative because they have not been widely researched and applied in mineral potential studies.Although most of the content of this paper has previously been published,several perspectives for further research are suggested.