“Three-Component” Digital Prospecting Method：A New Approach for Mineral Resources Quantitative Prediction and Assessment

“Three-component” method consists of three clase-connected aspects: geological anomaly,diversity of mineralization and mineral deposit spectrum. All these three concepts are not new separately, but it is a new approach to combine these three aspects in one single concept for quantitative mineral resources prediction and assessment and it is also the first time to conduct a more detailed study in each aspect. Investigation and clarification of geological anomalies, diversity of mineralization and spectrum of mineral deposits are realized by digitization and quantification of ore forming controlling factors, oreexisting symbols or marks, characteristics of mineralization and regulation of ore-genesis and laws of distribution. These procedures lead to construction of a “digital model“ for mineral resources prediction andassessment.     

A Correlation Analysis of Tourism Based on Gray Theory——A case of Xingwen County

Tourism industry has already become an important part of national economy,which not only played a positive role in the development of the economy,but promoted the adjustment and optimization of industrial structure.The paper analyzes the correlation between the tourism industry and the other industry through changes of the Xingwen County tourism industrial output value in the past seven years.With the Gray Model,the correlation degree among the tourism industry and agriculture,industry,transport,wholesale and retail trade,postal and telecommunications industries is measured.The analysis result indicates that the order of the corresponding correlation degree:catering accommodation is 0.691> wholesale and retail is 0.617 > agriculture is 0.616 > Postal and telecommunications industry is 0.610> Transport is 0.602> Industry is 0.537.In accordance with the actual conditions of the industry in Xingwen,the industrial development policies and measures are put forward,which help the tourism industry Xingwen their rapid,coordinated and highly efficient operation.     

Study on Fractal Feature of Fault Structure and Its Geological Implications Based on Remote Sensing——A Case Study of Jiuyi Mountain Area, Southern Hunan

以九嶷山地区ETM+遥感影像数据为信息源,通过信息提取与解译快速获取该区断裂构造.运用分形理论的盒维数法对断裂构造体系进行分形研究,结果表明,区内断裂构造在0.05～2.75km尺度范围内具有良好的自相似性和分形特征,总体分维值D=1.1155、R2=0.9964,分维值接近中国大地构造单元稳定区分维值;不同走向断裂分维值D在0.9184～1.0441间、R2介于0.9983 ～0.9996间,各分维值D不仅定量描述了各走向断裂分布的复杂性和构造演化的成熟度,而且还反映了断裂的成矿作用:NE向断裂分维值最大,D值在1.0441附近时主要表现为导矿构造;SN向断裂分维值次之,D在0.9870附近时主要表现为控矿构造;NW向断裂分维值偏小,D在0.9502附近时主要表现为含矿构造.     

Generalized Bootstrap Method for Assessment of?Uncertainty in Semivariogram Inference

The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap.     

An Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its VerificationEI北大核心CSCD

Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings. © 2020, Science Press. All right reserved.     

A Deep Learning and Intelligent Recognition Method of Image Data for Rock Mineral and its ImplementationEI北大核心CSCD

Mineral identification is an important task of geological survey, which can be a big challenge in complex geological conditions. In this research, the color and brightness are distinguished using mineral knowledge firstly, which is used to bring out the texture features more obviously. Next, the deep learning model and transfer learning method are applied to build a mineral identification model with enhanced features. Meanwhile, a color model for mineral identification using K-means is also established. Finally, a comprehensive model combining deep learning model and color model is built and a framework of identifying minerals is proposed. In the model establishing process, 6203 images with 19 labels are trained and the top-1 and top-3 accuracy reach 72.2% and 91.2%, respectively. Furthermore, a mineral identification application is developed on the Android system. The application is made up of three modules including mineral knowledge, mineral identification and data storage, which provide real-time recognition, mineral knowledge and data storage. © 2020, Science Press. All right reserved.     

Digital Geological Mapping of Sinus Iridum Area of the Moon Based on the Chang’E-I Data

Based on the CCD images,IIM data and DEM data of China＇s lunar exploration project （Chang＇E-Ⅰ） and related processed and analytic results,an integrated study of regional geology of Sinus Iridum and its adjacent area was conducted,and a series of relevant researches and analyses were carried out,including analysis of impact craters and their extrusive and accumulative materials,division of stratigraphic and tectonic units and classification of rock types,integrated analysis of chronology and lunar evolution history.In consideration of crater＇s shape features,quantity and preserving status of filling materials,the lunar impact craters can be divided into 7 types and 11 subtypes,and the accumulative materials of craters are divided into 6 types and 9 accumulative groups.According to the content and distribution of TiO2 and image characteristics,the basalts are divided into high-TiO2,medium-TiO2 and low-TiO2 basalts.Discussion was made on division of tectonic units and evolution features in the study area.The geological map of the Sinus Iridum Quadrangle （LQ-4） at a scale of 1∶2.5 M was preliminarily compiled with the ArcGIS system,and the spatial database of the map was established.Related technical specification,procedure and method for lunar geological mapping have been worked out,so as to lay a foundation for the forthcoming geological mapping of the global Moon in China by using the data of Chang＇E-Ⅱ and also for comprehensive study and geological mapping of other celestial bodies in the future.     

Fitting Data Under Omnidirectional Noise: A Probabilistic Method for Inferring Petrophysical and Hydrologic Relations

While geophysicists recognize that all measurements of media properties are subject to noise, methods for fitting petrophysical relations commonly employ regression-based formulas, which assume no error in one of the properties being related (the “independent" variable). To derive a more rigorous method for fitting such relations, we take a probabilistic viewpoint on the problem of fitting petrophysical relations to sample correlation data. Unlike prior approaches, we take into account the fact that noise is present in both measured properties. Under basic assumptions, we derive a new objective function for such problems which is proportional to the data likelihood and which can be used for both model parameter optimization and model selection. We present several numerical experiments outlining the utility of our method, and compare results of our method against results of other commonly used methods, such as kriging, regression, and total distance minimization. The results of our applications using the maximum likelihood technique appear visually accurate, and we also provide quantitative comparisons of performance that suggest the method produces more desirable results.