Improved-GRACE����������������Ż��о�

???????????????????ü????????????????GPS????????λ????????????????????????????????????????????????????????????????Improved??GRACE?????????????????????????????????????????????1????300?????????350 km???????????????????????3.993??10 ^-1 m??????300 km??250 km???????????????????8.770????77.145????????400 km??450 km??????????????????8.718????75.307????2??????50 km???????????????????????3.993??10^-1 m??????110 km??220 km??????????????????1.259????1.395????3????????????Improved??GRACE????????????????????????????????350 km??50 km?????     

�Dz���λ����Ԥ�����TurboEdit�㷨����Ľ�

??TurboEdit????3???????????и????????????????????????Ρ????γ??????????????????????????????????????3??????????????????????????????????????????????????????????????????????????????Geometry??Free??G??F??????????????????????????????????G??F???ο???ж??δ???????????С?????????????????????????????????????????????????????????????????????????Ч?????     

��Ͽ�����������������

????Parker-Oldenburg????????????????????????????????????????????С??μ????????????????????????????????????????????????????????????μ????????B1??B2??B3???????????????????￡?????????????B2??仯???????????????4 km????????????о????е????????9 km????     

����Satoģ���о��Ž�-����������е�Qcֵ����

????Sato???????????????????????????????о?????-???????????β??Qc???仯?????????Qc???????仯????Qc(f)=19.63??4.81f ^{1.183 8??0.094 5}?? ??2005??12??3??ML3.9????????????????????ε????Q0???????仯?????????????????????????????????к?????ε????Q0???????仯?????????????????????????????? ?????????п?????ζ????????????????н???????????Ρ?     

�봨�������б����Ա��ε���Դ�ֲ����������Ѹ������о�

?????HYPODD???????????????о????λ????????????CAP?????????????????????????????5??????????????????????????ε??????????????????????????????????????????????е??????????????????????????????????????????????????????????????????-??????????????????????????????????Σ????????о??и??μ????λ????5.5?????????????????????????????????????λ???λ??7??5.5???????????????4????????????????3?????????????????????????-??????????-????4?????????????????????     

��-�����ٲ���ģʽ�й���߶ȵ��Ż�ѡȡ

???????????????????????????????120??GRACE???????????????????????1????????????????100 km??????????????10??????????????????????????????GRACE????????????????2????????????????????????????500 km?????????20?????????????????0.221?????Ч??????50???80???100???120?????????9.621????92.857????418.957????1 895.369????3??????????????????????????????????????????JPL??????GRACE?????????120???????500 km????????????????澫???17.316 cm????????450 km??400 km??350 km?????????????????1.566????4.502????10.871????4?? ???????????????????й?????????350 ~ 400 km??????120?????????????????     

����Ms6.6�����InSAR�۲⼰�ϲ�λ����

????3??Envisat?????????,?????2008??10??6?????????Ms6.6??????????????α?,????????????4????????????δ?????????????α?,?????????????????20 km??20 km??Χ??????0.3 m????????Ρ??α??????????????????????????????????????t???????????λ???????????????????????????λ?????????????????????????????????????????????λ???3 m, ????λ??90.374??E, 29.745??N????Mw6.35??????????????189?????60????????????????′???????ε?μ?????????????12 km,??11 km,???9.5 km???????????????????????????????????????????????й??     

����GPS�����о���γ�Ȼ�����д��Ļ��

????GPS????о????γ???????д???????????????????????????????????????????????????????????????????????????????????????????????????????????13.7??0.6??5.8??0.5??7.1??1.0??12.7??0.2??25.4??0.5 mm/a???????????Σ?????????????????????????????????????29.2??1.8??14.1??1.0 mm/a?????????????????????????????????????????18.5??1.0??15.2??0.6 mm/a?????????????????????????????10.7??1.1 mm/a????γ????д????????????????????????л??????     

COMPASSα�൥�㶨λ���ȷ���

??????COMPASS???????????COMPASSα?????????????????????????????4GEO+5IGSO????????COMPASSα????λ???????????????COMPASSα???·??Ч???????????0??3 m???仯???侫???0.3??0.7 m??????????????·??Ч??????????С??GEO??????????·??Ч??????????????С??IGSO?????COMPASSα????λ?????RMS????N??E??U?????????3.33??3.45??8.84 m??     

�����ݶ��½�����������Mw7.1����ͬ�𻬶��ֲ�

????InSAR??????ENVISAT ASAR?????????????2008??3??21??????7.1??????????α?????????????????????????????????????????????????????????????????????????????????????????????????????????????λ??????0~14 km???????????λ???ж?F2??2.05 km?????5.47 m??????F1????????????????????????????ж?F2?????F3-N/F3??S??????????????????????????????????????????????ж?????Σ????λ???????С?????????????Mo=5.16??10 ¹⁹ N??m?????????7.07????     

求解制图区域的地图图幅编号的算法研究

基于地理格网理论,从点、线、面三个方面,提出了归原法、斜率分段-同异侧判别法和投影反算-图廓内外判别法,这些算法可准确映射各种投影后制图区域所对应的基础地理信息数据范围,实现了制图范围内地理信息数据所属地图图幅编号的可视化查询。     

基于离散粒子群算法的2种新型水体提取方法的对比与验证

随着遥感技术在水体提取与监测方面的广泛应用,更多的研究者致力于提高遥感水体提取的精度。离散粒子群算法在遥感图像分类研究中获得了较高的精度和更稳健的分类效果,已经被应用到遥感水体提取领域,但其在水体提取中的适用性和精度还有待对比与验证。本文采用最新提出的2种基于离散粒子群算法的水体提取方法,即光谱匹配耦合离散粒子群算法（SMDPSO）与最大熵耦合离散粒子群算法（MEDPSO）,基于Landsat8_OLI遥感影像,分别选择了有冰雪、有云、有山体阴影和有建筑物的4种环境复杂,常规方法提取精度较低的区域进行水体提取,并与2种常用的水体指数法（NDWI、MNDWI）进行了对比与验证。结果表明：① SMDPSO和MEDPSO方法在4个实验区都能快速地寻找出最佳的水体分布,具有一定的通用性;NDWI和MNDWI方法对有冰雪、有云、有山体阴影和有建筑物影响的区域表现出水体信息的错分现象,提取精度较低;② SMDPSO方法能够识别细小河流和离散水体,水体提取精度较高,但在有冰雪、云、山体阴影和建筑物的复杂环境下提取精度较低、误判率高;MEDPSO方法不仅可以识别细小水体,而且也解决了其他3种方法在提取过程中无法抑制背景信息干扰的问题,在4个实验区的总体精度均在97.8%以上,高于其他3种方法;③ 将离散粒子群算法引入到水体提取方法之中,可增强方法的区域整体性,也可提高其水体提取的精度和自动化程度;④ 运用最大熵模型等机器学习方法,可以结合光谱、形状和纹理等影像信息以及地形信息来进行水体识别,使得水体信息提取精度更高。本文的研究可为离散粒子群算法的推广以及遥感水体提取方法的选择提供参考。     

Edge detection of potential field data based on image processing methods

The conventional methods of edge detection can roughly delineate edge position of geological bodies, but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this paper, three image processing methods, Canny, LoG and Sobel operators are briefly introduced, and ap-plied to edge detection to determine the edge of geological bodies.Furthermore, model data is built to analyze the edge detection ability of this image processing methods, and compare with conventional methods.Combined with gravity anomaly of Sichuan basin and magnetic anomaly of Zhurihe area, the detection effect of image pro-cessing methods is further verified in real data.The results show that image processing methods can be applied to effectively identify the edge of geological bodies.Moreover, when both positive and negative anomalies exist and noise is abundant, fake edge can be avoided and edge division is clearer, and satisfactory results of edge detection are obtained.     

С��ȥ���������۷����ĶԱ��о�

???С??????????о???????С????????????????????????Ч???????????????????????????5??С???????????????????????????????     

松辽盆地十屋断陷储集层类型研究

本文主要利用毛细管压力曲线、电镜及薄片等资料,采用数理统计及综合评价方法探讨了研究区的储集层类型,将本区的储集层划分为:孔隙型、裂缝孔隙型等类型。在同一地区,储集层类型随深度而变化,故对含油气远景评价采取的方法也要相应改变,浅部应采用常规方法,深部应采用非常规方法。     

优化反演方法及数值反演试验初步研究

基于典型力学模型。以研究区域的岩性参数和研究块体的边界力作为反演参数进行数值模拟，研究了单纯形法，模式搜索法，鲍威尔法，变量轮换法，混合罚函数法，复合形法等6种最优化反演方法的反演效果和可靠性。结果显示6种方法都有较好的表现。在恰当的条件下，6种方法反演所得参数都达到或接近参数的理论值，迭代收敛图，位移拟合曲线和残差统计分布图直观地反映出6种方法的效果和可靠性，但对单纯形法和复合形法应给予较高的迭代收敛精度。     

真实和有效叶面积指数及聚集指数的尺度效应

尺度效应是地球科学和定量遥感中的重要研究课题,目前的许多研究大多集中在估算尺度效应带来的误差,而对一些关键的植被结构参数是否存在尺度效应及其尺度转换方法尚存在诸多不同见解。本文针对真实和有效叶面积指数（Leaf Area Index, LAI和Effective LAI, LAI_e）以及聚集指数（Clumping Index, CI）3个植被关键结构参数,从基本概念和获取方法上分析参数的尺度效应及其尺度转换方法。从定义上看,LAI并不存在尺度效应,而LAI_e和CI则存在尺度效应,其中CI的尺度效应由LAIe引入（CI=LAI_e/LAI）。在野外实测中,LAI破坏测量法没有尺度效应,但由孔隙率模型获取3个参数的方法均具有尺度效应。异速生长方程和遥感反演方法的尺度效应取决于方法本身的线性或非线性特征。目前全球主要的LAI、LAI_e和CI遥感产品都基于非线性模型获取,其反演过程具有尺度效应。像元尺度的LAI本身并不具有尺度效应,而像元尺度的LAI_e和CI虽然具有尺度效应,但在实践中常常被忽略。因此,实际工作中应注意区分参数概念本身、野外测量、遥感反演方法以及遥感产品等所展示的不同尺度效应。     

综合物探方法在地热勘查中的应用

物探方法在金属、石油、煤炭等地质勘探中应用广泛，并占有十分重要的位置，但在地热勘探方面应用的较少，主要表现为采用的方法少和新技术应用的少。该文试图通过较系统的电、磁、重力、遥感、测井、地震等物探方法在地热资源勘查方面的应用及实例探讨，以期引起有关部门对地热物探工作的重视，使物探手段更好地为地热资源开发利用服务。     

综合物探方法在地热勘查中的应用

物探方法在金属、石油、煤炭等地质勘探中应用广泛,并占有十分重要的位置,但在地热勘探方面应用的较少,主要表现为采用的方法少和新技术应用的少。该文试图通过较系统的电、磁、重力、遥感、测井、地震等物探方法在地热资源勘查方面的应用及实例探讨,以期引起有关部门对地热物探工作的重视,使物探手段更好地为地热资源开发利用服务。     

STUDY ON THE SUBSIDING LAND EXTRACTION FROM LANDSAT TM IMAGE SUPPORTED BY GIS AND DOMAIN KNOWLEDGE

The subsiding land can be extracted from Remote Sensing image based on its spectral and spatial fea-tures,The features of subsiding land caused by mining,especially its RS information features and relative knowledge are proposed Three methods can be used to extract subsiding land from RS image ,The first is to categorize the region into different blocks (or layers) according to their features and apply corresponding strategies for each block,the second is to identify the changeable region based on GIS firstly and then to classify those regions.and the third is to post-process the classified image by traditional methods or ANN (Artificial Neural Network) methods based on domain knowledge and GIS.Two direct extraction methods are intrduced also.One is the extraction based on the water accumulating property of subsiding land ,and the other is based on the dynamic change of land cover in subsiding land.