Scaling Properties of Feldspar and Quartz in Micro-images of Ideal Granites

The properties of feldspar and quartze are studied in this article from a fractal point of view using gray-scale micro-images of granite samples collected at the Fangshan (房山) granite body in Hebei (河北) Province, China, which can be regarded as an ideal granite in the sense of Vistelius. We found that there exist power-law relationships between the eigenvalues of the gray-scale matrices and their ranks for the feldspar and quartz. The fractal model used here is a λ-R model similar to the N-λ model proposed by Qiuming Cheng in 2005. Meanwhile, we found that average variances for the gray-scale matrices of feldspar are larger than those of quartz on the same sections, and this may be useful for auto-identification of feldspar and quartz as well as other minerals.     

A Continuum One——Dimensional Model for Radionuclide Migration Testing in Unsaturated Loess

Unsaturated radionuclide migration experiments were conducted in a pit inside thetesting hall.Several types of radionuclides were used in the experiments.Tritium wasused as a tracer for water movement in unsaturated loess.Other kinds of radionuclideswere also used in order to obtain fundamental parameters for radionuclide migration sothat further environmental assessment of low—level radioactive waste disposal can be car-ried out.Mechanisms governing unsaturated flow in loess,that is,principles ofone—way lateral flow,are presented qualitatively in this paper.And a continuumone—dimensional model for radionuclide migration testing is developed based on the ex-periments conducted under the particular conditions at the test site.The data measuredfrom the tests were compared with solutions of this one—dimensional model.Resultsshow that this model is feasible for modeling radionuclide migration in unsaturatedloess.     

An Analysis for Cross Beam-Ground Anchor Reinforcement

With the rapid development of water facilities, hydroelectric projects, highways and railways in China, beam-anchor reinforcement has been widely used to stabilize slopes in recent years. But the theory for the design of beam-anchor reinforcement is far behind the application. Cross beam-ground anchor reinforcement is a combination of beams and anchors forming a new structure to prevent slope sliding. The forces in the beams are discussed using theoretical analysis and numerical modeling. The Winkler model is used to analyze the beams, and reasonable values of λ, length, spacing and cantilevered length for the beams are determined through a theoretical analysis. A three-dimensional finite element method is adopted to model the interaction of the beams and soils and a structure analysis is applied to treat the beams and to study the stress distribution in external and internal beams. The analytical results show that it is better to satisfy λ≥2π, the spacing between anchors ls should be lsλ<π/2 and cantilever length should be (0.3-0.5)ls for the optimum design. The numerical results show that the same design can be used for all beams in different directions, including the internal and external beams. The application of the analytical method for reinforcement beam analysis is acceptable. It is better to choose a safety coefficient of 1.3 for design based on the analytical method for safety.     

http://www.sciencedirect.com/science/article/pii/S1674987110000162

<正>Fault-block structures of the Altay-Sayan folded area(ASFA) southeastern Siberia of Russia were used as the basis for creating a 3-D model.The surface structures were projected to depths by previous correlations between long and deep faults,with all layers and deformation factors defined. The mean deformation factor(Ds) is 0.12 unit/km~3 in the upper layer,0.012 unit/km~3 in the intermediate layer,and 0.007 unit/km~3 in the lower layer of the 3-D ASFA neotectonic model.Ds allows correlation of the three distinguished layers with rheological bodies that differ in their potential for accumulating elastic energy.3-D modeling can be used as a methodological approach to projections in seismic prone areas such as the Krasnoyarsk region,for earthquake-hazard monitoring.     

青藏铁路L型挡土墙土压力的试验与分析

Considering the only retaining structure L-type retaining wall used in Golmud-Lhasa section of Qinghai-Tibet Railway, the earth pressure and frost-heaving force was tested in a frost-thaw circle for one year, and several different analysis models were studied. Compared with site test and theory analysis, it was found that the actual earth pressure is much larger than the designed earth pressure. Hence,a revised analysis model of earth pressure is put forward, which could include another possible force except slide triangle or frost heaving force. The model in this paper is only consider the thrust force other than failure sliding wedge. This model could be used as reference for the design and construction of similar projects.     

Quantitative Modelling of Multiphase Lithospheric Stretching and Deep Thermal History of Some Tertiary Rift Basins in Eastern China

The stretching process of some Tertiary rift basins in eastern China is characterized by multiphase rifting. A multiple instantaneous uniform stretching model is proposed in this paper to simulate the formation of the basins as the rifting process cannot be accurately described by a simple (one episode) stretching model. The study shows that the multiphase stretching model, combined with the back-stripping technique, can be used to reconstruct the subsidence history and the stretching process of the lithosphere, and to evaluate the depth to the top of the asthenosphere and the deep thermal evolution of the basins. The calculated results obtained by applying the quantitative model to the episodic rifting process of the Tertiary Qiongdongnan and Yinggehai basins in the South China Sea are in agreement with geophysical data and geological observations. This provides a new method for quantitative evaluation of the geodynamic process of multiphase rifting occurring during the Tertiary in eastern China.     

A Comprehensive Model for Evaluating Coalbed Methane Reservoirs in China

Coalbed methane reservoir （CBMR） evaluation is important for choosing the prospective target area for coalbed methane exploration and production. This study aims at identifying the characteristic parameters and methods to evaluate CBMR. Based on the geological surveys, laboratory measurements and field works, a four-level analytic hierarchy process （AHP） model for CBMR evaluation is proposed. In this model, different weights are prioritized and assigned on the basis of three main criteria （including reservoir physical property, storage capacity and geological characteristics）, 15 sub-criteria, and 18 technical alternatives; the later of which are discussed in detail. The model was applied to evaluate the CBMR of the Permo-Carboniferous coals in the Qinshui Basin, North China. This GIS-based fuzzy AHP comprehensive model can be used for the evaluation of CBMR of medium-high rank （mean maximum vitrinite reflectance 〉0.5 %） coal districts in China.     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

REE Tetrad Effects in Rare—metal Granites

Described in this paper are the characteristics of tetrad effects of REE in rare-metal granites.Based on the analytical data and experimental geochemical data available,it is pointed out that the tetrad effects of REE in the granites are produced in the metal-fluid system.Intense fractional crystallization of granitic melt(containing REE accessary minerals)and its interaction with volatile-rich(F,Cl)fluid are the major factors leading to the tetrad effects of REE.From this,this paper presents a composite genetic model for high-degree fractional crystallization-volatile-rich fluid metasomatism of rare-metal granites.With the model,quantitative calculations have been made.Meanwhile,it is pointed out that the tetrad effects of REE can be used as an important indicator to distinguish mineralized granites from barren ones.     

Omnibus Weights of Evidence Method Implemented in GeoDAS GIS for Information Extraction and Integration

Weights of evidence (WofE) is an artificial intelligent method for integration of information from diverse sources for predictive purpose in supporting decision making. This method has been commonly used to predict point events by integrating point training layer and binary or ternary evidential layers (multiclass evidence less commonly used). Omnibus weights of evidence integrates fuzzy training layer and diverse evidential layers. This method provides new features in comparison with the ordinary WofE method. This new method has been implemented in a geographic information system-geophysical data analysis system and the method includes the following contents: (1) dual fuzzy weights of evidence (DFWofE), in which training layer and evidential layers can be treated as fuzzy sets. DFWofE can be used to predict not only point events but also area or line events. In this model a fuzzy training layer can be defined based on point, line, and areas using fuzzy membership function; and (2) degree-of-exploration model for WofE is implemented through building a degree of exploration map. This method can be used to assess possible spatial correlations between the degree of exploration and potential evidential layers. Importantly, it would also make it possible to estimate undiscovered resources, if the degree of exploration map is combined with other models that predict where such resources are most likely to occur. These methods and relevant systems were validated using a case study of mineral potential prediction in Gejiu (个旧) mineral district, Yunnan (云南), China.     

Evidential belief functions for data-driven geologically constrained mapping of gold potential,Baguio district,Philippines

A data-driven application of the theory of evidential belief to map mineral potential is demonstrated with a redefinition of procedures to estimate evidential belief functions. The redefined estimates of evidential belief functions take into account not only the spatial relationship of an evidence with the target mineral deposit but also consider the relationships among the subsets of spatial evidences within a set of evidential data layer. Proximity of geological features to mineral deposits is translated into spatial evidence and evidential belief functions are estimated for the proposition that mineral deposits exist in a test area. The integrated maps of degrees of belief for the proposition that mineral deposits exist in a test area is classified into a binary mineral potential map. For the Baguio district (Philippines), the binary gold potential map delineates (a) about 74% of the training data (i.e., locations of large-scale gold deposits) and (b) about 64% of the validation data (i.e., locations of small-scale gold deposits). The results demonstrate the usefulness of a geologically constrained mineral potential mapping using data-driven evidential belief functions to guide further surficial exploration work in the search for yet undiscovered gold deposits in the Baguio district. The results also indicate the usefulness of evidential belief functions for mapping uncertainties in the geologically constrained integrated predictive model of gold potential.     

Chatham Rise nodular phosphate — Modelling the prospectivity of a lag deposit (off-shore New Zealand): A critical tool for use in resource development and deep sea mining

After almost five decades of episodic exploration, feasibility studies are now being completed to mine the deep-water nodular phosphate deposit on the central Chatham Rise. Weights of evidence (WofE) and fuzzy logic prospectivity models have been used in these studies to help in mapping of the exploration and resource potential, to constrain resource estimation, to aid with geotechnical engineering and mine planning studies and to provide background geological data for the environmental consent process. Prospectivity modelling was carried out in two stages using weights of evidence and fuzzy logic techniques. A WofE prospectivity model covering the area of best data coverage was initially developed to define the geological and environmental variables that control the distribution of phosphate on the Chatham Rise and map areas where mineralised nodules are most likely to be present. The post-probability results from this model, in conjunction with unique conditions and confidence maps, were used to guide environmental modelling for setting aside protected zones, and also to assist with mine planning and future exploration planning. A regional scale fuzzy logic model was developed guided by the results of the spatial analysis of the WofE model, elucidating where future exploration should be targeted to give the best chance of success in expanding the known resource. The development work to date on the Chatham Rise for nodular phosphate mineralisation is an innovative example of how spatial data modelling techniques can be used not only at the exploration stage, but also to constrain resource estimation and aid with environmental studies, thereby greatly reducing development costs, improving the economics of mine planning and reducing the environmental impact of the project.     

深部水热型地热潜力区的GIS预测模型——以土耳其西安纳托利亚地区为例

开发地热资源有着很大的不确定性和很高的经济风险,需要建立可靠的数学预测模型以确定潜在地热区。通过对地震震中、断层、布格重力异常、磁异常和红外遥感5个与地热密切相关的因素进行整合,建立了地热潜力区的数学预测模型;并以土耳其西安纳托利亚地区为例,对模型的优劣性进行分析评价。运用指数叠加模型和证据权重模型分别建立地热潜力区图,预测成功指数表明证据权重模型的预测结果更为准确。其中,中高潜力区总面积为26 529 km²,占总面积的31.14%,包含39个地热点,占总地热点的50.65%。预测结果显示潜在地热区位于Ayd?n,Denizli,Manisa和Bal?kesir,其中前2个地区的地热已经被开发利用。本研究可以在地热勘探阶段确定潜在地热区,降低经济风险。     

A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China

Forest fire is known as an important natural hazard in many countries which causes financial damages and human losses; thus, it is necessary to investigate different aspects of this phenomenon. In this study, performance of four models of linear and quadratic discriminant analysis (LDA and QDA), frequency ratio (FR), and weights-of-evidence (WofE) was investigated to model forest fire susceptibility in the Yihuang area, China. For this purpose, firstly, a forest fire locations map was prepared implementing MODIS satellite images and field surveys. Then, it was classified into two groups including training (70%) and validation (30%) by a random algorithm. In addition, 13 forest fire effective factors were prepared and used such as slope degree, slope aspect, altitude, Topographic Wetness Index (TWI), plan curvature, land use, Normalized Difference Vegetation Index (NDVI), annual rainfall, distance from roads and rivers, wind effect, annual temperature, and soil texture. Using the training dataset and effective factors, LDA, QDA, FR, and WofE models were applied and forest fire susceptibility maps were prepared. Finally, area under the curve (AUC) of receiver operating characteristics (ROC) was implemented for investigating the performance of the models. The results depicted that WofE had the best performance (AUC = 82.2%), followed by FR (AUC = 80.9%), QDA (AUC = 78.3%), and LDA (AUC = 78%), respectively. The results of this study showed the high contribution of altitude, slope degree, and temperature. On the other hand, it was seen that slope aspect and soil had the lowest importance in forest fire susceptibility mapping. From the AUC results, it can be concluded that FR, WofE, LDA, and QDA had acceptable performance and could be used for forest fire susceptibility mapping at the regional scale.     

GIS-based groundwater spring potential assessment and mapping in the Birjand Township,southern Khorasan Province,Iran

Three statistical models—frequency ratio (FR), weights-of-evidence (WofE) and logistic regression (LR)—produced groundwater-spring potential maps for the Birjand Township, southern Khorasan Province, Iran. In total, 304 springs were identified in a field survey and mapped in a geographic information system (GIS), out of which 212 spring locations were randomly selected to be modeled and the remaining 92 were used for the model evaluation. The effective factors—slope angle, slope aspect, elevation, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, lithology, land use, and distance to river, road, fault—were derived from the spatial database. Using these effective factors, groundwater spring potential was calculated using the three models, and the results were plotted in ArcGIS. The receiver operating characteristic (ROC) curves were drawn for spring potential maps and the area under the curve (AUC) was computed. The final results indicated that the FR model (AUC?=?79.38 %) performed better than the WofE (AUC?=?75.69 %) and LR (AUC?=?63.71 %) models. Sensitivity and factor analyses concluded that the bivariate statistical index model (i.e. FR) can be used as a simple tool in the assessment of groundwater spring potential when a sufficient number of data are obtained.     

Prospectivity for epithermal gold–silver deposits in the Deseado Massif,Argentina

Previous prospectivity modelling for epithermal Au–Ag deposits in the Deseado Massif, southern Argentina, provided regional-scale prospectivity maps that were of limited help in guiding exploration activities within districts or smaller areas, because of their low level of detail. Because several districts in the Deseado Massif still need to be explored, prospectivity maps produced with higher detail would be more helpful for exploration in this region.We mapped prospectivity for low- and intermediate-sulfidation epithermal deposits (LISEDs) in the Deseado Massif at both regional and district scales, producing two different prospectivity models, one at regional scale and the other at district-scale. The models were obtained from two datasets of geological evidence layers by the weights-of-evidence (WofE) method. We used more deposits than in previous studies, and we applied the leave-one-out cross validation (LOOCV) method, which allowed using all deposits for training and validating the models. To ensure statistical robustness, the regional and district-scale models were selected amongst six combinations of geological evidence layers based on results from conditional independence tests.The regional-scale model (1000 m spatial resolution), was generated with readily available data, including a lithological layer with limited detail and accuracy, a clay alteration layer derived from a Landsat 5/7 band ratio, and a map of proximity to regional-scale structures. The district-scale model (100 m spatial resolution) was generated from evidence layers that were more detailed, accurate and diverse than the regional-scale layers. They were also more cumbersome to process and combine to cover large areas. The evidence layers included clay alteration and silica abundance derived from ASTER data, and a map of lineament densities. The use of these evidence layers was restricted to areas of favourable lithologies, which were derived from a geological map of higher detail and accuracy than the one used for the regional-scale prospectivity mapping.The two prospectivity models were compared and their suitability for prediction of the prospectivity in the district-scale area was determined. During the modelling process, the spatial association of the different types of evidence and the mineral deposits were calculated. Based on these results the relative importance of the different evidence layers could be determined. It could be inferred which type of geological evidence could potentially improve the modelling results by additional investigation and better representation.We conclude that prospectivity mapping for LISEDs at regional and district-scales were successfully carried out by using WofE and LOOCV methods. Our regional-scale prospectivity model was better than previous prospectivity models of the Deseado Massif. Our district-scale prospectivity model showed to be more effective, reliable and useful than the regional-scale model for mapping at district level. This resulted from the use of higher resolution evidential layers, higher detail and accuracy of the geological maps, and the application of ASTER data instead of Landsat ETM + data. District-scale prospectivity mapping could be further improved by: a) a more accurate determination of the age of mineralization relative to that of lithological units in the districts; b) more accurate and detailed mapping of the favourable units than what is currently available; c) a better understanding of the relationships between LISEDs and the geological evidence used in this research, in particular the relationship with hydrothermal clay alteration, and the method of detection of the clay minerals; and d) inclusion of other data layers, such as geochemistry and geophysics, that have not been used in this study.     

Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method

A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches. The present study is an attempt to utilise a multivariate statistical method called binary logistic regression (BLR) analysis for LSZ mapping in part of the Garhwal Lesser Himalaya, India, lying close to the Main Boundary Thrust (MBT). This method gives the freedom to use categorical and continuous predictor variables together in a regression analysis. Geographic Information System has been used for preparing the database on causal factors of slope instability and landslide locations as well as for carrying out the spatial modelling of landslide susceptibility. A forward stepwise logistic regression analysis using maximum likelihood estimation method has been used in the regression. The constant and the coefficients of the predictor variables retained by the regression model have been used to calculate the probability of slope failure for the entire study area. The predictive logistic regression model has been validated by receiver operating characteristic curve analysis, which has given 91.7% accuracy for the developed BLR model.     

基于GIS的证据权模型在秦岭～松潘金矿潜力预测中的应用

数据驱动的证据权法被用来进行金矿潜力制作。为了确定秦岭~松潘金矿的潜力区,需利用地质、地球化学、地球物理等数据。数据采集、图形处理、空间分析都是在GIS平台上进行的。预测结果表明,证据权法在综合不同空间数据上是有效的,最终的预测图件圈出了最有利的矿化区,可用于进一步勘查研究。     

A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis

A prospecting cost-benefit strategy is developed by quantitatively defining the prospecting cost and benefit in mineral potential mapping. Suppose that some mineral deposits have been discovered in a study area of a set of grid cells, the prospecting cost and benefit of a “unique” condition can be defined as the percentage of non-deposit-bearing and deposit-bearing cells within the “unique” condition, respectively. By replacing the false positive and true positive rates in the receiver operating characteristic (ROC) curve analysis with the prospecting cost and benefit, the Youden index, likelihood ratio, and lift index can be computed and used to express the mineral potential of the “unique” condition. Thus, the mineral potential mapping in a study area can be implemented by identifying all the possible “unique” conditions and then computing their mineral potential indicators such as the Youden index, likelihood ratio, and lift index. By integrating an automatic “unique” condition searching algorithm with the techniques for computing the mineral potential indicators for each “unique” condition, the following prospecting cost-benefit strategy is developed for mineral potential mapping: (a) select map patterns closely associated with the discovered mineral deposits using their mineral potential indicators, (b) automatically search for all the possible “unique” conditions, (c) evaluate the mineral potential of each “unique” condition using its mineral potential indicators, and (d) assess mineral potential mapping performance using the mineral potential indicator diagrams. For demonstration purposes, the Baishan district in Southern Jilin Province in China, which has a complex geological setting, is chosen as a case study area. The weights of evidence (WofE) modeling posterior probability, Youden index, likelihood ratio, and lift index are applied in the mineral potential mapping and their performance are assessed using their ROC curves, cumulative lift charts, and Youden and likelihood ratio diagrams. The results show that (a) the likelihood ratio and lift index perform similarly well and (b) the posterior probability performs a little bit worse than the likelihood ratio and lift index while a little bit better than the Youden index. Therefore, the prospecting cost-benefit strategy provides a common paradigm for both mineral potential mapping and the performance assessment.