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51.
杜斌 《地质与勘探》2021,57(4):879-894
松诺铜矿区位于香格里拉格咱斑岩铜矿带红山-普朗铜多金属成矿亚带中段,矿区剥蚀程度较低,具有寻找斑岩型铜矿的潜力。本次蚀变矿物填图工作采用近红外光谱矿物分析技术,识别出绿泥石、绿帘石、高岭石、云母类、蒙脱石类及伊利石等6类主要蚀变矿物,蚀变类型为青磐岩化、绢英岩化;结合物探、化探异常分布,圈定3处找矿靶区,经对KHT2、KHT3钻孔验证,深部均圈定多层铜矿化体。结果表明,近红外光谱矿物分析技术在斑岩型铜多金属矿床勘查评价中可以较好地划分热液矿化蚀变带,进一步明确斑岩型矿化-蚀变中心,为探矿工程部署提供依据。  相似文献   
52.
多年冻土工程地质制图的阶梯发展   总被引:2,自引:1,他引:1  
张中琼  林芳  吴青柏  张泽  张鹏 《冰川冻土》2021,43(1):176-183
多年冻土工程地质制图是区域和历史冻土学研究的基础内容之一。多年冻土工程地质制图是根据需要和制图原则,在特定时空尺度分析多年冻土特征,以及各种自然、工程、环境要素共同影响下的多年冻土属性的量化描述。多年冻土工程地质制图所解决的科学问题从多年冻土的分布、规律和格局、冻土功能,认识不断提高。制图的内容从研究多年冻土的基本要素,特征要素,到功能要素,制图的实用性不断增强。制图方法从基本地理和工程地质方法逐步发展到系统论方法。总之,多年冻土工程地质制图表现出时间尺度上阶段性和空间坐标上的台阶性,在认识的深度和广度上均不断深化。为多年冻土区基础科学研究、工程建设、寒区资源环境等方面的工作提供基础数据和技术支持。  相似文献   
53.
高光谱遥感以其超高的光谱维数据优势,使对地物的精细识别和区分能力较传统多光谱遥感数据有质的提升。以HyM ap高光谱数据和高分五号高光谱数据为数据源,选择中国西部基岩区区域开展了高光谱遥感岩性-构造解译工作。通过图像增强处理后,对研究区地层单元、岩体/脉、构造等进行了遥感地质解译。对比已有的地质调查结果,发现高光谱遥感数据相较多光谱/高分数据对岩性-构造信息的展布情况显示得更加清晰和直观,同时其对不同岩性段、不同岩相带,以及细小构造等区分能力突出,表现出明显的技术优势。研究认为,高光谱遥感可为基岩区区域地质填图提供更加客观、真实的地质体、构造展布情况,能提高地质调查填图的效率和质量。  相似文献   
54.
黑龙江省黑河市340高地金矿点大地构造上位于兴安地块东北缘,多宝山Cu-Au-Mo-W-Fe成矿亚带内,其地貌为浅覆盖区,找矿较为困难。本文采用浅钻技术取样,对研究区进行基岩填图,圈定出以硅化为中心、青磐岩化为外围的蚀变矿化带。对基岩样品进行岩石地球化学分析,通过主成分分析,确定有6个主成分特征值大于1,其中第二主成分与金元素关系密切,是研究区有效的找矿标志。高精度磁测结果显示区内构造以NNW向为主,且其线性低值异常或与蚀变引起的退磁有关。对区内的地物化异常进行工程查证,发现金矿点1处,圈定预测靶区1处。  相似文献   
55.
This study demonstrates the use of ASTER data for the mapping of gypsum deposits and associated geological formations that occurred in the Thumrait region of southern Oman. The measurement of spectra over samples of gypsum in the 1,300–2,500 nm wavelength using a PIMA spectrometer showed the presence of distinct absorptions at 1400–1600, 1750, 1940, 2,100, and 2,400 nm characteristics to O? H stretching, H2O combinations, and S? O bending overtones and stretching, respectively. Studying the unique spectral absorption characters of gypsum samples, we developed a false color composite (FCC) and an image by decorrelation stretch using the spectral bands 7, 3, and 2 of ASTER. The results FCC showed the regions of gypsum occurrences, and the decorrelated image discriminated the gypsum occurrences from other geological formations of the area. The study of surface mineralogy of the region using the VNIR‐SWIR bands by the spectral angle mapper method showed the presence of sulfate, carbonate, and clay minerals of the geological formations in the study area. We compared the results of ASTER with the results obtained using spectral bands 12, 8, and 4 of Sentinel‐2A processed by the same methods. The study showed that the spectral bands of ASTER can be used for mapping the gypsum and associated geological formations.  相似文献   
56.
甘肃北山地区岩浆岩及变质岩出露广泛,植被稀少,地势平缓,是无人机遥感开展地质填图试验的理想目标区。为解决传统地质填图方法受地形、环境限制和投入高及工作周期长等问题,选取北山长流水地区20 km2化探重点工作区为目标区,利用大疆精灵4专业版无人机采集图像,并采用Photoscan软件合成高分辨率正射影像以及三维模型,建立解译标志,对目标区域进行地质解译,获得比前人1:1万地质图更加精细的地质图。该方法较传统填图方法能够解译出地质体形态、岩脉产状、微小断裂等更详细的地质内容,并能提供划分岩脉期次的证据。  相似文献   
57.
Soil thickness, intended as depth to bedrock, is a key input parameter for many environmental models. Nevertheless, it is often difficult to obtain a reliable spatially exhaustive soil thickness map in wide-area applications, and existing prediction models have been extensively applied only to test sites with shallow soil depths. This study addresses this limitation by showing the results of an application to a section of Wanzhou County (Three Gorges Reservoir Area, China), where soil thickness varies from 0 to ~40 m. Two different approaches were used to derive soil thickness maps: a modified version of the geomorphologically indexed soil thickness (GIST) model, purposely customized to better account for the peculiar setting of the test site, and a regression performed with a machine learning algorithm, i.e., the random forest, combined with the geomorphological parameters of GIST (GIST-RF). Additionally, the errors of the two models were quantified, and validation with geophysical data was carried out. The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area: the mean absolute error was 10.68 m with the root-mean-square error (RMSE) of 12.61 m, and the frequency distribution residuals showed a tendency toward underestimation. In contrast, GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m. The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.  相似文献   
58.
Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m2 were modelled from 938 forest stand patches (MPS = 6868.20 m2) and 43,742 tree stand patches (MPS = 140.79 m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.  相似文献   
59.
As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.  相似文献   
60.
较全面、系统地介绍了Tan DEM-X/Terra SAR-X双站SAR科学计划,重点涉及其科学目标、Tan DEM-X卫星参数、轨道结构以及干涉数据获取模式等相关内容,并讨论了双站SAR成像、极化In SAR和数字波束成形等干涉测量新技术及其研究进展。这些双站SAR新技术的实现将大大地推动SAR干涉测量在全球地形测绘、冰川学、海洋学及地质学等领域中的应用。  相似文献   
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