Binbin He

Mineral prospectivity mapping integrating multi-source geology spatial data sets and logistic regression modelling

A method of integrating multi-source geology spatial data sets and logistic regression modelling for mineral prospectivity mapping is described in this paper. Logistic regression model describes the relationship between a dependent variable, which is a binary variable representing the presence or absence of the mineral deposits, and k independent variables represent ore-controlled geological features such as faults, lithology, geochemical anomaly, which may be continuous or discrete or any combination of both types. A case study was selected located in East Kunlun region of Qinghai Province, China. Multi-source geospatial data contain geological data, geophysical data, geochemical data and remotely sensed data. The potential prospectivity map was produced by logistic regression on the resulting revised binary map patterns, same as in weights of evidence modelling, two logistic probability thresholds of 0.52 and 0.58 were used to classify the study area into three classes of high potential, moderate potential, and low potential areas, which high potential areas contain 54% of the total gold deposits, covering 7.3% of the total area. Moderate potential area contains 8% of the gold deposits, covering 8% of the total area. Low potential areas contain 38% of the gold deposits, covering 84.7% of the total area.

Mineral Potential Mapping for Cu-Pb-Zn Deposits in the East Kunlun Region, Qinghai Province, China, Integrating Multi-source Geology Spatial Data Sets and Extended Weights-of-Evidence Modeling

Weights-of-evidence modeling has been extensively applied in mineral exploration and has also been used to examine the relationship between point patterns and map patterns. Generally, the evidence to be integrated is binary, whereas the real world consists of continuous and multi-class data, and the reclassification of these data into a binary form can result in the loss or distortion of information. However, extended weights-of-evidence modeling can be used to reduce the loss of useful information. This paper integrates multi-source geological spatial data (geological, geophysical, geochemical, and remote sensing data) for decision-making processes, aided by GIS spatial analysis techniques. Mineral-potential mapping for Cu-Pb-Zn deposits is proposed using extended weights-of-evidence modeling and generalized weights-of-evidence modeling in East Kunlun region of Qinghai Province, China. The study area was classified into three target zones of high potential, moderate potential, and low potential, based on the sum of weights rather than posterior probability. Through the comparison of the results derived from the two models, extended weights-of-evidence modeling demonstrated a better prediction rate.

A spatial data mining method for mineral resources potential assessment

On the basis of multi-source geology spatial database and traditional spatial data mining, a spatial data mining method for mineral resources potential assessment was proposed in this paper, which the spatial characteristics and uncertainty of geology data were reasonable to consider. The method mainly include continuous geological spatial data discretization, spatial relationship abstracting and attribute transforming, mining metallogenic association rules and quality assessment, comprehensive evaluation of metallogenic association rules and potential assessment. Finally, the experiment of mineral potential assessment for iron deposits was performed in Eastern Kunlun, Qinghai province, China, using spatial data mining method and weights-of-evidence model, respectively. The results indicate that the prediction accuracy of spatial data mining was obvious higher than weights-of-evidence models, the method is suitable for mineral resources potential assessment and its effectiveness is good.

Assessing spatial-temporal variation of heavy metals contamination of sediments using GIS 3D spatial analysis methods in Dexing mines, Jiangxi province, China

Dexing mines located in the east of China. They have been exploited for more 20 years and the environment pollution in this area has become very severe. It is very important to assess the spatial-temporal variation of regional environment contamination for monitoring the environment quality variation and contamination trend. 330 Sediment samples in Dexing region were collected and analyzed for As, Hg, Cd, Cr, Zn, Cu and Pb in 2004 and 1989. The maximum of As, Hg, Cd, Cr, Zn, Cu and Pb concentration in 2004 in sediments were up to 9, 4, 4.6, 1.5, 5.9, 6.3 and 5.6 times higher than theirs level in 1989, respectively. Meanwhile, the geoaccumulation index (Igeo) was used to assess the environment quality. In the end, spatial-temporal contrast was performed using GIS 3D spatial analysis from original testing data and geoaccumulation index. The contrast results indicated that there existed slightly contamination of As, Cd, Cu and Pb in 1989, mainly concentrated within little scope along the mid-lower courses of Dexing river and there were different extents of contamination of As, Cd, Zn, Cu and Pb in sediments in 2004, especially in the areas of the Dexing river, Dawu river and middle-lower courses Lean river, while few contamination occurred in those areas in 1989. All the contrast results indicated that the extent and scope of heavy metals contamination of sediments in 2004 were bigger than that of 1989.

Mining metallogenic association rules combining cloud model with Apriori algorithm

Spatial data mining refers to extracting and “mining” the hidden, implicit, valid, novel and interesting spatial or non-spatial patterns or rules from large-amount, incomplete, noisy, fuzzy, random, and practical spatial databases. Spatial association rules mining is extracted implicit association rules from spatial database. Many metallogenic association rules lie in geology spatial database. In this paper, a method for mineral resources prediction is proposed, which mainly including uncertainty transmission between qualitative and quantitative geology spatial data using cloud model, metallogenic association rules extracting using Apriori algorithm, and comprehensive assessment of rules. At last, an experiment of iron resources prediction is performed in Eastern Kunlun Mountains, China. The results indicated that the method proposed in this paper is suitable for regional metallogenic prediction.

Hydrothermal alteration mapping using aster data in East Kunlun Mountains, China

ASTER has higher spatial resolution in the VNIR region and higher spectral resolution in the shortwave infrared (SWIR) region in comparison with ETM+, which provide us an opportunity to distinguish various clay-alteration minerals accurately. In this paper, the 14 bands of ASTER were adopted to identify the lithologic and hydrothermal alteration minerals, such as the calcite, kaolinite and OH bearing minerals and so on, in the East Kunlun region of China. The effect of different mapping methods presented in the past, including band ratio, relative band-depth (RBD) images, False Color Composite (FCC) and SFF et al, were compared and analyzed. Results indicate that the mineral index method is suit to the high and cold mountainous area and could get the ideal result relative to the others. This work study once more again shows that the multi-spectral remote sensing techniques have excellent potentials for metallic mineral prognostication.

Case-based reasoning and GIS approach to regional metallogenic prediction

Traditional qualitative methods and quantitative geostatistical methods are usually two kinds of ways for regional metallogenic prediction, the formers workloads are large and heavy, the latters algorithm models are complex. In order to propose a new, simple and more accurate method, this paper carried out research work on Case-Based Reasoning and GIS approach to regional metallogenic prediction. The metallogenic Case-Based Reasoning model including metallogenic case expression model and metallogenic case retrieval model was well discussed, the reasoning flow was given, experiment to Gold mine prediction was fully done. The experiment results show the prediction of metallogenic Case-Based Reasoning is effective.

Use of data assimilation technique for improveing the retrieval of leaf area index in time-series in alpine wetlands

Leaf area index (LAI) is one of the key vegetation indices for many biological and physical processes in plant canopies. In this study, an assimilation technique was used to simulate the LAIs varying in time series in an alpine wetland located in western China. The Terra MODIS 16 day composite surface reflectance products at 250 m resolution in 2010 with high quality were used. LAI was retrieved based on the ACRM canopy reflectance model and LUT algorithm. An experiential LOGISTIC model was fitted using the retrieved LAI, and the ensemble Kalman filter algorithm was introduced to assimilate the estimated LAI into the LOGISTIC model to update the model state.

A framework for uncertain spatial data mining and its application to environmental geochemistry quality assessment

A framework for uncertain spatial data mining was proposed. In which, the uncertainties of spatial data themselves and spatial data mining are emphatically dealt with, including uncertainty simulation by Monte Carlo method, spatial autocorrelation matrix based on uncertain spatial data, discretization based on neighborhood EM algorithms, and quality assessment of results. Meanwhile, the experiments of environmental geochemistry quality assessment with uncertain and spatial data mining have been performed using the environmental geochemistry data gotten from Dexing, Jiangxi province in China.