Quantitative Prediction for Deep Mineral Exploration

On reviewing the characteristics of deep mineral exploration, this article elaborates on the necessity of employing quantitative prediction to reduce uncertainty. This is caused by complexity of mineral deposit formational environments and mineralization systems as increase of exploration depth and incompleteness of geo-information from limited direct observation. The authors wish to share the idea of ＂seeking difference＂ principle in addition to the ＂similar analogy＂ principle in deep mineral exploration, especially the focus is on the new ores in depth either in an area with discovered shallow mineral deposits or in new areas where there are no sufficient mineral deposit models to be compared. An on-going research project, involving Sn and Cu mineral deposit quantitative prediction in the Gejiu （个旧） area of Yunnan （云南） Province, China, was briefly introduced to demonstrate how the ＂three-component＂ （geoanomaly-mineralization diversity-mineral deposit spectrum） theory and non-linear methods series in conjunction with advanced GIS technology, can be applied in multi-scale and multi-task deep mineral prospecting and quantitative mineral resource assessment.

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).

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.

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 .

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.

Kernelized fourth quantification theory for mineral target prediction

This paper presents a nonlinear multidimensional scaling model, called kernelized fourth quantifica- tion theory, which is an integration of kernel techniques and the fourth quantification theory. The model can deal with the problem of mineral prediction without defining a training area. In mineral target prediction, the pre-defined statistical cells, such as grid cells, can be implicitly transformed using kernel techniques from input space to a high-dimensional feature space, where the nonlinearly separable clusters in the input space are ex- pected to be linearly separable. Then, the transformed cells in the feature space are mapped by the fourth quan- tifieation theory onto a low-dimensional scaling space, where the sealed cells can be visually clustered according to their spatial locations. At the same time, those cells, which are far away from the cluster center of the majority of the sealed cells, are recognized as anomaly cells. Finally, whether the anomaly cells can serve as mineral potential target cells can be tested by spatially superimposing the known mineral occurrences onto the anomaly ceils. A case study shows that nearly all the known mineral occurrences spatially coincide with the anomaly cells with nearly the smallest scaled coordinates in one-dimensional sealing space. In the case study, the mineral target cells delineated by the new model are similar to those predicted by the well-known WofE model.

考虑Miner线性累计损伤的卷绕头疲劳寿命预测

纺织机卷绕头长期振动会产生零件损伤,这种振动损伤会形成线性积累,当前方法对卷绕头的疲劳损伤以固定阈值为主,未考虑损伤积累问题,所以难以准确预测卷绕头的疲劳寿命。因此提出基于Miner线性累计损伤的纺织机卷绕头疲劳寿命预测方法。分析纺织卷绕头的主要结构,并从轴段单元、刚性盘、机匣、支承部件四方面出发,构建卷绕头整体的动力学方程。构建振动方程明确卷绕头的振动受力,结合Miner线性累计损伤理论,构建高速卷绕头的疲劳寿命预测函数,完成纺织机卷绕头疲劳寿命的预测。实验结果表明:这种估计方法的疲劳强度损失值与实际值更为接近,振动位移分析结果更加精准,且疲劳寿命预测误差降低,平均误差仅为1.475%。

Prediction Approach for Petrothermal Properties in Al Hashimiyya Basalts-Jordan

Thermal conductivity and mineral composition of flood basalt in Al Hashimiyya city were correlated. Representative thin sections were optically analyzed for their mineral constituents and micro fractures. Findings of this study will contribute to a comprehensive understanding of the correlation between selected petrological characteristics of basalts and their heat conduction properties. It found that a 10% increase of opaque and ferromagnesian minerals volume in the studied basalts leads to a thermal conductivity increasing by 0.4 W•m−1•K−1. This may considerably contribute to provide an alternative to direct measurements of the thermal conductivity in Jordan basalts if a sufficient mineralogical data set is achievable. Thus, the prediction of thermal conductivity through modal mineral composition may become a significant feature for efficient geothermal system exploration in basaltic rocks. The results can be brought together into a petrophysical and hydrogeothermal model for better reservoir characterization. Such models will improve the assessment of the basalt’s suitability as a geothermal reservoir for cooling and heating utilizations.

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.

Transformation of inherent and extraneous minerals in feed coals of commercial power stations and their density-separated fractions

The main objective of this work is to relate the coalescence of inherent minerals and the fragmentation of extraneous minerals to the slagging propensities of South African pulverised feed coals during combustion.By incorporating the behaviour of inherent mineral matter or extraneous mineral matter in these coals under combustion conditions into ash-deposition prediction methods,the heterogeneous nature of the ash properties,which were disregarded in previous conventional ash deposition predictions,is considered in the study.The mode of occurrence of mineral matter in feed coals plays a crucial role in the formation of high-temperature mineral phases under combustion conditions.The float and sink fractions of the three different coals evaluated in this distinctive alternative approach provide different chemical and mineralogical properties of the derived ashes when subjected to elevated temperatures under oxidising conditions.Formation of significant concentrations of high-temperature minerals(such as mullite and cristobalite)is mainly due to the transformation reactions of extraneous kaolinite and quartz which are not associated with the extraneous fluxing minerals at elevated temperatures.However,the formation of anorthite at elevated temperatures can be attributed to the interaction of either inherent or extraneous fluxing minerals(namely calcite,dolomite,pyrite,and siderite)that are associated with either inherent or extraneous kaolinite in the coal samples under the oxidising condition.Furthermore,the anorthite,mullite,and calcium/magnesium/iron/aluminosilicate and silica glasses in ashes are formed either via crystallisation during the cooling of the hightemperature molten solution or via the solid state reactions.These high-temperature minerals and their glasses present in ashes can therefore be used as the indicators of the slagging propensity of coals.The implementation of results from this unique case study,will be of great significance to other industrial combustion processes to minimise or control ash deposition,slagging,and equipment erosion problems by either blending the density-separated fractions of coals or coals from different mines based on the chemical and mineralogical properties to prepare suitable feed coals.Furthermore,this unique alternative approach can be followed to further evaluate other feed coals in the global power stations during combustion.

Geoscience material structures prediction via CALYPSO methodology

Many properties of planets such as their interior structure and thermal evolution depend on the high-pressure properties of their constituent materials. This paper reviews how crystal structure prediction methodology can help shed light on the transformations materials undergo at the extreme conditions inside planets. The discussion focuses on three areas:(i) the propensity of iron to form compounds with volatile elements at planetary core conditions(important to understand the chemical makeup of Earth's inner core),(ii) the chemistry of mixtures of planetary ices(relevant for the mantle regions of giant icy planets), and(iii) examples of mantle minerals. In all cases the abilities and current limitations of crystal structure prediction are discussed across a range of example studies.

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.

Predictive GIS Model for Potential Mapping of Cu,Pb,Zn Mineralization

ABSTRACT The geologic features indicative of Cu, Pb, Zn mineral deposits in a area are fractures （structure）, and host rock sediments. Datasets used include Cu, Pb, Zn deposit points record, geological data, remote sensing imagery （Landsat TM5）. The mineral potential of the study area is assessed by means of GIS based geodata integration techniques for generating predictive maps. GIS predictive model for Cu, Pb, Zn potential was carried out in this study area （Weixi） using weight of evidence. The weights of evidence modeling techniques is the data driven method in which the spatial associations of the indicative geologic features with the known mineral occurrences in the area are quantified, and weights statistically assigned to the geologic features. The best predictive map generated by this method defines 24 % the area having potential for Cu, Pb, Zn mineralization further exploration work.

Development and validation of a predictive model for spinal fracture risk in osteoporosis patients

BACKGROUND Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine,increasing the risk of fractures.Given its high incidence,especially among older populations,it is critical to have accurate and effective predictive models for fracture risk.Traditionally,clinicians have relied on a combination of factors such as demographics,clinical attributes,and radiological characteristics to predict fracture risk in these patients.However,these models often lack precision and fail to include all potential risk factors.There is a need for a more comprehensive,statistically robust prediction model that can better identify high-risk individuals for early intervention.AIM To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis.METHODS The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined.The patients were selected according to strict criteria and categorized into two groups:Those with fractures(n=40)and those without fractures(n=40).Demographics,clinical attributes,biochemical indicators,bone mineral density(BMD),and radiological characteristics were collected and compared.A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model.The area under the receiver operating characteristic curve(AUROC)was used to evaluate the model’s performance.RESULTS Factors significantly associated with fracture risk included age,sex,body mass index(BMI),smoking history,BMD,vertebral trabecular alterations,and prior vertebral fractures.The final risk-prediction model was developed using the formula:(logit[P]=-3.75+0.04×age-1.15×sex+0.02×BMI+0.83×smoking history+2.25×BMD-1.12×vertebral trabecular alterations+1.83×previous vertebral fractures).The AUROC of the model was 0.93(95%CI:0.88-0.96,P<0.001),indicating strong discriminatory capabilities.CONCLUSION The fracture risk-prediction model,utilizing accessible clinical,biochemical,and radiological information,offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis.The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.

三维成矿预测关键问题

三维成矿预测是当前深部找矿预测和勘查的重要方法和手段,其方法体系和实践应用均已取得大量成果,但同时存在若干关键科学技术问题,导致其进一步发展受到制约。本文从多尺度三维成矿预测方法体系不完善、不确定性分析与优化研究薄弱、三维成矿预测要素挖掘存在瓶颈、缺少针对三维成矿预测的三维深度学习模型和方法等关键问题出发,对目前三维成矿预测领域相关方面的研究进展进行综合分析,并提出针对上述关键问题可能的解决方案和研究方向。预期未来三维成矿预测领域的研究工作将创新发展出多种方法,实现对三维预测信息的深度挖掘;构建形成适用的三维深度学习模型和训练方法,有效增强三维成矿预测结果的预测能力;通过系统性地开展三维成矿预测不确定性研究,进一步优化预测过程和结果,有效提高三维成矿预测方法的可靠性和准确性;形成面向多尺度三维成矿预测的方法体系,更有效地指导矿集区-矿田-勘查区块(矿床)等不同级别的深部矿产资源找矿勘查工作。相关关键问题的解决将进一步深化和完善三维成矿预测理论和方法体系,促进三维成矿预测理论方法的实践应用,显著提升深部找矿预测和勘查工作的效率与水平,助力深部找矿突破。

基于本体指导的矿产预测知识图谱构建研究

在基于数据驱动范式的科学研究中,构建知识图谱已被证明是获取和表征知识的有效手段之一。然而,目前已构建的矿产资源预测领域知识图谱仍然存在诸多挑战和局限性,有待进一步解决和完善。首先,针对矿产预测的本体构建问题研究相对较少,尤其是该领域现有知识图谱的本体层普遍缺乏时空语义,限制了对于矿产资源时空特征的有效表示与分析。其次,现有图谱构建方法主要为面向数据层面的文本抽取,而缺乏对于复杂逻辑关系的本体层建设,以及本体层与数据层之间的有效关联。以上问题会导致构建的地学知识图谱缺乏深层次的语义信息,难以满足矿产资源预测对表达复杂地学概念和关系的需求。针对上述问题,本研究以综合信息矿产预测理论为指导,旨在构建可应用于矿产预测任务的复杂语义知识图谱。具体而言,首先通过对矿产预测的理论和方法进行解析构建初始化领域本体,然后选择成熟的地质时间本体和地理空间本体对初始本体进行本体融合和扩展,通过嵌入时空语义有效表达地矿产资源的时空特征。此外,重点关注了本体层与数据层之间的关联建设,通过建立丰富的语义关系,实现知识图谱中各个节点之间的有效连接与信息共享。实验结果表明,采用本文所提出的方法构建的图谱在知识丰度和置信度等指标上均优于其他现有方法。这一研究为矿产预测领域提供了更为深入和全面的数据资源建设的方法支撑,有助于推动该领域的进一步发展和应用。

河南银洞坡金矿“三位一体”找矿预测地质模型及应用示范

河南银洞坡金矿位于河南省桐柏县朱庄镇,为一特大型金矿床,同体共生银、铅锌。运用勘查区找矿预测方法与理论,构建成矿地质体、成矿构造与成矿结构面和成矿作用特征标志为主的“三位一体”找矿预测地质模型。野外调查和矿床学研究认为,矿床成因为与早白垩世岩浆作用有关的中低温岩浆热液矿床,主成矿阶段温度介于200~350℃之间,碳、氢、氧、硫同位素研究表明,成矿物质和成矿流体主要来源于岩浆。确定成矿地质体为矿区深部隐伏早白垩世花岗岩体;成矿结构面为岩性界面(硅/钙面)、褶皱成矿构造系统结构面、沉积成岩结构面;成矿作用特征标志主要为矽卡岩化、钾化、黄铁绢英岩化、硅化、铁白云石化、萤石矿化、碳酸盐化。由此建立了银洞坡金矿找矿预测地质模型,圈定了找矿靶区,施工的钻孔见矿率达到85%,对该地区及邻区进一步找矿具有现实的指导意义。

基于投影寻踪回归方法的微压过滤冲洗池水头损失与过滤效率预测模型

以微压过滤冲洗池的水头损失与过滤效率为考核指标,考虑进水流量、矿化度、含沙量与滤网孔径4个因素进行正交试验设计,采用投影寻踪回归方法PPR分析各个因素对考核指标的影响权重,选取20组试验数据建立含沙微咸水条件下微压过滤冲洗池水头损失和过滤效率的预测模型,探究了含沙微咸水对微压过滤冲洗池的水头损失和过滤效率的影响。研究结果表明,影响微压过滤冲洗池水头损失的因素由大到小依次为进水流量、含沙量、矿化度、滤网孔径;影响过滤效率的因素由大到小依次为含沙量、进水流量、矿化度、滤网孔径;构建的PPR预测模型的预测精度整体合格率达100%;当进水流量为6~7 m^(3)/h、含沙量为0.5~1.0 g/L、矿化度为0~2.0 g/L及滤网孔径为0.125~0.180 mm时,水头损失存在最小值;当进水流量为9~10 m^(3)/h、含沙量为1.75~2.00 g/L、矿化度为0~3.0 g/L及滤网孔径为0.125~0.150 mm时,过滤效率存在最大值。物理模型的试验结果可为微压过滤冲洗池的实际应用提供研究基础。