镁铁质―超镁铁质岩全谱段遥感识别模型

镁铁质―超镁铁质岩的遥感岩性识别研究一直是遥感岩石学领域的热点之一,其岩石信息对于岩浆型铜镍硫化物矿床的预测十分重要。提出一种综合利用Landsat 8、ASTER和PALSAR-2数据的镁铁质―超镁铁质岩石全谱段遥感识别模型：通过对野外采集岩石样品及光谱库中相关岩石矿物的可见光―热红外谱域的光谱测试与特性分析,结合岩石表面微波散射特性,应用特征空间,贝叶斯线性判别分析、逐步偏最小二乘回归分析等方法,创建镁铁质―超镁铁质岩性指数,并利用提出的岩性指数进行镁铁质―超镁铁质岩的初步提取,再经过基于贝叶斯决策理论的融合处理得到最终的镁铁质―超镁铁质岩石信息。结果表明:该模型能够对镁铁质―超镁铁质岩进行精准定位,识别精度达到94%以上。此外,根据遥感岩性、构造特征的解译与区内已知铜镍硫化物矿床的成矿岩体比较,推断出位于赤石山―小长山―中坡山一带以及小青山附近岩体具有一定的成矿潜力。     

镁铁质―超镁铁质岩全谱段遥感识别模型

镁铁质―超镁铁质岩的遥感岩性识别研究一直是遥感岩石学领域的热点之一，其岩石信息对于岩浆型铜镍硫化物矿床的预测十分重要。提出一种综合利用Landsat 8、ASTER和PALSAR-2数据的镁铁质―超镁铁质岩石全谱段遥感识别模型：通过对野外采集岩石样品及光谱库中相关岩石矿物的可见光―热红外谱域的光谱测试与特性分析，结合岩石表面微波散射特性，应用特征空间，贝叶斯线性判别分析、逐步偏最小二乘回归分析等方法，创建镁铁质―超镁铁质岩性指数，并利用提出的岩性指数进行镁铁质―超镁铁质岩的初步提取，再经过基于贝叶斯决策理论的融合处理得到最终的镁铁质―超镁铁质岩石信息。结果表明:该模型能够对镁铁质―超镁铁质岩进行精准定位，识别精度达到94%以上。此外，根据遥感岩性、构造特征的解译与区内已知铜镍硫化物矿床的成矿岩体比较，推断出位于赤石山―小长山―中坡山一带以及小青山附近岩体具有一定的成矿潜力。     

高光谱遥感岩矿识别填图的技术流程与主要技术方法综述

自20世纪80年代以来,伴随着多种航空和航天成像光谱仪的研制成功和投入运行,一系列的图像光谱基本处理手段和实验室岩矿光谱分析技术方法,如最小噪声分量(MNF)变换-像元纯度指数(Pixel Purity Index,PPI)-N维可视化(N-Dimensional Visualization)、岩石类型系统光谱分类、多变量统计分析、矿物光谱特征确认的概率分析、MGM技术等相继发展起来,从而为形成成像光谱岩矿填图的完整技术流程打下了坚实的技术基础。分析了多种岩矿光谱分析的技术方法,同时指出,不同方法具有各自的优、缺点,针对不同应用目标需要不同的矿物识别与岩矿填图方案;混合方法的开发应用和从可见光到微波波段的融合应用在未来更为重要。     

Utilization of Hyperion data over Dongargarh,India, for mapping altered/weathered and clay minerals along with field spectral measurements

Hyperion data acquired over Dongargarh area, Chattisgarh (India), in December 2006 have been analysed to identify dominant mineral types present in the area, with special emphasis on mapping the altered/weathered and clay minerals present in the rocks and soils. Various advanced spectral processes such as reflectance calibration of the Hyperion data, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) have been used for comparison/mapping in conjunction with spectra of rocks and soils that have been collected in the field using Analytical Spectral Devices's FieldSpec instrument. In this study, 40 shortwave infrared channels ranging from 2.0 to 2.4 μm were analysed mainly to identify and map the major altered/weathered and clay minerals by studying the absorption bands around the 2.2 and 2.3 μm wavelength regions. The absorption characteristics were the results of O–H stretching in the lattices of various hydrous minerals, in particular, clay minerals, constituting altered/weathered rocks and soils. SAM and SFF techniques implemented in Spectral Analyst were applied to identify the minerals present in the scene. A score of 0–1 was generated for both SAM and SFF, where a value of 1 indicated a perfect match showing the exact mineral type. Endmember spectra were matched with those of the minerals as available in the United States Geological Survey Spectral Library. Four minerals, oligoclase, rectorite, kaolinite and desert varnish, have been identified in the studied area. The SAM classifier was then applied to produce a mineral map over a subset of the Hyperion scene. The dominant lithology of the area included Dongargarh granite, Bijli rhyolite and Pitepani volcanics of Palaeo-Proterozoic age. Feldspar is one of the most dominant mineral constituents of all the above-mentioned rocks, which is highly susceptible to chemical weathering and produces various types of clay minerals. Oligoclase (a feldspar) was found in these areas where mostly rock outcrops were encountered. Kaolinite was also found mainly near exposed rocks, as it was formed due to the weathering of feldspar. Rectorite is the other clay mineral type that is observed mostly in the southern part of the studied area, where Bijli rhyolite dominates the lithology. However, the most predominant mineral type coating observed in this study is desert varnish, which is nothing but an assemblage of very fine clay minerals and forms a thin veneer on rock/soil surfaces, rendering a dark appearance to the latter. Thus, from this study, it could be inferred that Hyperion data can be well utilized to identify and map altered/weathered and clay minerals based on the study of the shape, size and position of spectral absorption features, which were otherwise absent in the signatures of the broadband sensors.     

新疆柯坪地区沉积岩光谱特征分析

从新疆柯坪地区的沉积岩样品分析入手，阐述岩石样品的光谱测量方法，分析其光谱特征，探讨沉积岩的矿物成分，岩石表面结构、颜色、风化及环境等因素对其光谱特征的影响。     

岩矿薄片偏光序列图像的矿物颗粒分割方法

岩矿薄片图像中矿物颗粒的提取是岩矿薄片粒度分析、成分识别的基础,为了进一步提升矿物颗粒提取的准确性,提出一种新的矿物颗粒分割提取方法.该方法以相同视域、不同角度下的正交偏光序列图像为基础,对序列图像进行融合后,利用熵率超像素算法提取矿物颗粒目标.为了减少矿物颗粒的过分割现象,采用快速区域合并算法合并具有相似性的区域,最后,依据矿物颗粒在正交偏光下的变化规律对分割区域筛选,再次进行颗粒目标融合实现颗粒自动分割.该方法提取岩矿薄片图像中的矿物颗粒,可以获得较好的效果.     

基于背景多层次分离的遥感矿化蚀变信息提取模型及应用实例

基于背景多层次分离的遥感矿化蚀变信息提取模型,以地物的波谱特征和遥感技术的物理机制为基础,以"矿化蚀变岩波谱特征分析—视反射率图像计算—"背景"多层次分离—蚀变信息增强—蚀变信息提取"为技术流程,能快速、准确地提取矿化蚀变信息。以新疆焉耆县哈都虎拉山一带为研究区,以最新的Landsat8-OLI多光谱数据为数据源,将该模型用于提取研究区的黄钾铁矾蚀变岩石信息,共提取出80余处黄钾铁矾蚀变岩石出露点,其中有两处黄钾铁矾蚀变岩石集中分布区,为研究区的找矿工作提供了重要线索。     

Application of CASI/SASI and fieldspec4 hyperspectral data in exploration of the Baiyanghe uranium deposit,Hebukesaier, Xinjiang,NW China

Hyperspectral technology can quickly identify hydrothermal altered minerals and can locate the possible hydrothermal/mineralization centres through the variations of the spectral characteristic parameters of some specific altered minerals. In this study, airborne sensors Compact Airborne Spectrographic Imager (CASI)/ Shortwave infrared Airborne Spectrographic Imager (SASI) and visible-shortwave infrared spectrometer FieldSpec4 hyperspectral data were used to study the alteration characteristics to locate the possible hydrothermal/mineralization centres for the Baiyanghe uranium deposit, Xinjiang, NW China. The results showed that the hydrothermal altered minerals mainly included white mica (it is a general term for aqueous silicoaluminate mineral group, and mainly includes muscovite and illite in the text) (Al-rich, Al-median, and Al-poor white mica), montmorillonite, kaolinite, chlorite, carbonate, and ferric iron (Fe₂O_3, and the ferric iron alteration usually presents red, mainly hematite in the text); and that the alteration had obvious zoning characteristics. The variations in the absorption-peak wavelength of white mica at 2200 nm, SWIR-IC (short-wavelength infrared illite crystallinity, a dimensionless quantity, and it is defined as the absorption-peak depth of illite at 2200 nm divided by the absorption-peak depth of illite at 1900 nm on a hull quotient spectrum) of white mica, and acid–base properties revealed that the possible hydrothermal/mineralization centres were located in the northern contact zone between the Yangzhuang rock body and the northern volcanic rocks of the Devonian and in the acidic alteration zone to north of the deposit about 3.0–4.0 km, where there appeared a certain number of uranium mineralization occurrences. These results pointed to the existence of multiple hydrothermal activities and could provide references for periphery uranium exploration and geological origin of this uranium area.     

Reflectance spectra of minerals and their discrimination using Thematic Mapper,IRS and SPOT multi-spectral data

Abstract

Minerals and rocks show varying spectral reflectances under different spectral ranges of the electromagnetic spectrum and such spectral responses vary because of colour, texture, crystal structure, specific gravity and other physical and optical properties. Hence in order to optimise the spectral ranges which may be used in the recognition of the minerals and the rocks, spectral reflectance measurements were carried out for 29 minerals under the visible and near-infrared ranges of the electromagnetic spectrum covered by the TM bands 1, 2, 3 and 4, IRS bands 1, 2, 3 and 4 and SPOT bands 1, 2 and 3. The analysis of the data identifies the optimum spectral bands for distinguishing the different minerals and the mineral aggregates/rocks     

Mutual complement between statistical and neural network approaches for rock magnetism data analysis

Interpretation of magnetic phenomena in rock magnetism requires a good understanding in relationship between magnetic susceptibility and magnetic minerals, particularly magnetite, contained in rocks. Previous studies emphasized on describing such a correlation using a sole expression through statistical analysis. The resultant correlations are generally useful only in qualitative interpretation, but too coarse to simulate quantitative solutions. In this paper, we combine the correlation analysis with neural network techniques to not only identify the correlations between susceptibility and magnetite in rocks but also simulate accurate susceptibilities with respect to the magnetite contents provided. Our study has demonstrated that multilayer perceptron models are capable of producing accurate mappings between susceptibility and magnetite in rocks. However, correlation analysis provides qualitative interpretation for rock magnetism data in identifying the patterns of magnetic behaviours of the rocks. In quantitative simulation, if the required accuracy is not restricted, a general MLP model with existence of noises in training data is the first choice because it does not require statistical data pre-processing for establishing the NN model. If the simulation is to provide solutions as accurate as possible, the MLP model must be trained by noise-filtered datasets. The noise filtering is based on the preliminary correlation analysis. Therefore, these two approaches are mutually complementary, rather than competitive to each other.     

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.     

An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents

The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of rocks.     

Integrating visible,near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data

Hyperspectral and thermal infrared (TIR) multispectral remote sensing have great potential for surface geological mapping. This paper investigates the potential impact of combining these data on the comparative accuracy of different classification methods. A series of simulated datasets based on the characteristics of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER) sensors was created from surface reflectance and emissivity data derived from library spectra of 16 common minerals and rocks occurring in Cuprite, Nevada. System noise, illumination effects, the presence of vegetation, and spectral mixing were added to create the simulated data. Five commonly used classification algorithms, minimum distance, maximum likelihood classification, binary encoding, spectral angle mapper (SAM) and spectral feature fitting (SFF), were applied to all datasets. All the classification methods, excluding binary encoding, achieved nominal to significant improvement in overall accuracy when applied to the combined datasets in comparison to using only the AVIRIS dataset. Furthermore, certain classification methods of the combined datasets show a marked increase in individual rock or mineral class accuracies. Limestone, silicified and muscovite, for instance, show an improvement of almost 30% or greater in either producer's or user's accuracy using the combined datasets with SAM. SFF provides a great improvement in accuracy for limestone, quartz and muscovite. In terms of overall comparative accuracy for the individual and the combined datasets, maximum likelihood classification shows the best performance. For the simulated AVIRIS data, SFF was generally superior to SAM, although the accuracy of SAM applied to the combined datasets was slightly better than that of SFF. SAM applied to the combined datasets increases classification accuracy for some minerals and rocks which do not exhibit distinct absorption feature in the TIR region, while for SFF, only the accuracy of minerals and rocks with characteristic absorption features in the TIR region is improved.     

相山铀矿田蚀变遥感异常及地质意义

蚀变遥感异常是矿产资源综合评价与定位预测的一个新的独立参数, 应用ETM+数据在基岩裸露区进行含水矿物的蚀变遥感异常提取的技术日趋成熟, 但在植被覆盖区的提取效果一直在探索之中。通过对植被发育的相山铀矿田ETM+数据主成分分析, 基本消除了植被的影响, 获得了良好的含水矿物蚀变遥感异常信息。其中分布在相山矿田西北部碎斑熔岩中的或北部西段变质岩中的蚀变遥感异常是热液水云母化引起的, 具有重要的找矿意义。     

Modeling hardness of Nb-microalloyed steels using fuzzy logic

The paper presents some results of the research connected with the development of new approach based on the fuzzy logic of predicting the Vickers microhardness of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size, and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 114 different experimental data were gathered from the literature. The data used in the fuzzy logic model are arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size, and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the fuzzy logic systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels.     

Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data

We applied the Modified Spectral Angle Mapper (MSAM) and continuum-removal methods to reflectance data obtained in the shortwave infrared regions by the airborne hyperspectral sensor, HyMap, to delineate distribution of the minerals related to hydrothermal alteration and pegmatite. The combination of these two methods was applied to remotely sensed hyperspectral data for the first time in this study. The effectiveness of this method, ‘continuum-removal MSAM’, was confirmed by comparing our mineral index maps with existing mineral maps of the Cuprite region, Nevada, USA. We then produced mineral index maps of southern Namibia using newly observed hyperspectral data. The existence of many pegmatite- and porphyry-type deposits is reported in this study area, where detailed mineralogical mapping based on remote-sensing data has not previously been conducted. Our mineral index maps are consistent with our field survey results, and also with X-ray diffraction analyses and spectral measurements of rock samples. In this study, the continuum-removal MSAM method successfully discriminated hydrothermal alteration minerals such as kaolinite and pyrophyllite, and pegmatite-related minerals such as high-aluminium muscovite and lepidolite in southern Namibia for the first time. The main advantages of our classification method are simplicity and high-precision identification of alteration minerals.     

Concept and model experiments on automatic shoveling of rocks from the rock piles

This paper is concerned with the robotics of the shovel dozer. One methodology to distinguish between the rock pile and the huge rock was proposed in this study. The concept of this method is based on the difference of the time history of the force acting on the lift arms of the shovel dozer model. By using this model, two kinds of experiment on shoveling the rocks were carried out with changing the RPM of the motor. It was confirmed that stable shoveling was conducted.     

Passive microwave radiometry in the Gobi-desert region

This paper summarizes preliminary results of remote sensing test with an airborne microwave radiometer, in the Gobi-desert region. The relationship between microwave signature and complex dielectric constant for various rocks and minerals is analyzed. The possibilities of exploring for water and mineral resources in this arid region by means of passive microwave radiometry are discussed.     

ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.     

Numerical Simulation of Injection Molding Cooling Process Based on 3D Surface Model

The design of the cooling system of injection molds directly affects both productivity and the quality of the final part. Using the cooling process CAE system to instruct the mold design, the efficiency and quality of design can be improved greatly. At the same time, it is helpful to confirm the cooling system structure and optimize the process conditions. In this paper, the 3D surface model of mold cavity is used to replace the middle-plane model in the simulation by Boundary Element Metho…