新疆双亚幔柱模式及其控矿机制

文章简述了新疆地区的地质理论研究成果、矿产地质特征,认为准噶尔、塔里木双亚幔柱是新疆金属矿产呈现"8"字型分布的主要控制因素;双亚幔拉主体形成时期分别为石炭纪和二叠纪。准噶尔亚幔柱斜向天山造山带,塔里木亚幔柱近垂直,前者作用强烈、短暂,后者温和而漫长。准噶尔、塔里木盆地周缘薄弱带与内部构造体制为地幔深源岩浆及后碰撞造山岩浆的上侵提供了通道。天山造山带形成于晚石炭世,它受双亚幔柱挤压隆起作用有限,成矿局部叠加;二叠纪以来,以相对南北向挤压为主,是能源矿产主要形成时期;阿尔金、昆仑等山脉在新近纪快速隆升。基于幔枝构造启示,建议金属矿产地质找矿勘查与石油、煤炭及铀矿地质勘查展开合作,在准噶尔、塔里木两盆地内进行金属矿产综合找矿。     

庆阳-合水地区长8油层组沉积物源分析

物源分析对砂岩储层的展布与分析预测具有重要的意义。通过对鄂尔多斯盆地西南缘庆阳-合水地区上三叠统延长组长8油层组的砂岩重矿物组合、轻矿物组合及砂岩岩屑组分的平面分布特征分析,判断了庆阳-合水地区的沉积物源方向。研究表明,庆阳-合水地区存在两大物源和古流沉积体系,即西南方向的庆阳沉积古流体系和南部方向的合水沉积古流体系;长82亚组沉积期以南部的沉积古流体系为主,而长81亚组沉积期以西南部的沉积古流体系为主;重矿物组合特征分析显示长81亚组沉积期合水区带混源现象明显。     

鄂尔多斯盆地富县大东沟区长8储层微观孔隙结构特征研究

储层的微观孔隙结构不仅决定着油气的运移、聚集,而且影响着油水分布、油气采收率。通过物性分析、扫描电镜、铸体薄片、高压压汞等技术对长8储层微观孔隙结构特征进行分析研究,结果表明孔隙类型主要有剩余粒间孔、粒间溶孔、粒内溶孔、晶间孔,其中以前2种为主要有效孔隙。孔喉组合以小孔微喉为主,小孔微细喉型次之。根据毛细管压力曲线形态特征,结合排驱压力、孔喉分选性、进汞饱和度、中值压力等参数,将研究区储层分为三类。     

Landsat 8数据地表温度反演算法对比

随着卫星遥感技术的发展,利用遥感反演地表温度的方法不断出现,如劈窗法、双角度法和单通道算法等。Landsat系列卫星的遥感数据是地表温度反演的重要数据之一。本文选择无锡周边区域为研究区,利用Landsat 8卫星遥感数据,对两种劈窗算法(Juan C.Jiménez-Muoz劈窗算法和Offer Rozenstein劈窗算法)和两种单窗算法(Juan C.Jiménez-Muoz单通道算法和覃志豪单窗算法)的地表温度反演精度进行了对比和敏感性分析。采用太湖16个浮标站的实测数据来验证了4种算法的反演精度。结果表明:两种劈窗算法的精度较高且较为接近,误差为0.7 K左右;覃志豪单窗算法和Juan C.Jiménez-Muoz单通道算法精度较低,误差分别为1.3 K和1.4 K左右。Juan C.Jiménez-Muoz劈窗算法对参数的敏感性最低,Juan C.Jiménez-Muoz单通道算法次之,覃志豪单窗算法和Offer Rozenstein劈窗算法敏感性相对最高。其中Juan C.Jiménez-Muoz单通道算法只适用于一定的水汽含量范围,有一定的局限性。     

资源一号02C与Landsat8影像融合方法对比分析

针对以往关于资源一号02C和Landsat8卫星影像数据融合的研究不足的问题,该文利用前者在空间分辨率上高于后者、后者具有前者所不具有的光谱信息这一特性,选取主成分变换法、比值变换法、色彩变换法、高通滤波法和超分辨率贝叶斯法5种融合方法,分别对两种数据本身及数据间进行融合,并利用定性与定量的方法对融合结果进行评价,得出:资源一号02C星全色波段与多光谱波段数据融合结果中高通滤波法与超分辨率贝叶斯法效果较好,Landsat8OLI全色波段与多光谱数据融合结果中高通滤波法效果最好,资源一号02C星全色波段与Landsat8OLI多光谱数据融合结果中高通滤波法效果最好。     

基于Landsat 8劈窗算法与混合光谱分解的城市热岛空间格局分析——以兰州市中心城区为例

在ENVI和GIS支持下,提出了基于Landsat 8遥感影像的地温反演劈窗算法,提取兰州市中心城区地表温度。利用FNEA和混合光谱分解法确定了兰州市中心城区的城市热岛中心、不透水面和植被盖度,分析了城市热岛空间分布格局以及地表温度与下垫面之间的关系。结果显示:基于Landsat 8数据地温反演的劈窗算法是可行的。兰州中心城区的高温区分布较集中,地表温度与植被呈较强的负相关,与不透水面呈不显著的正相关,与其他非光合物质呈正相关。     

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.     

Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data — where do the severely degraded areas occur?

The Tibetan Plateau in Western China is the world’s largest alpine landscape, sheltering a rich diversity of native flora and fauna. In the past few decades, the Tibetan Plateau was found to suffer from grassland degradation processes. Grassland degradation is assumed to not only endanger biodiversity but also to increase the risk for natural hazards in other parts of the country which are ecologically and hydrologically connected to the area. However, the mechanisms behind the degradation processes remain poorly understood due to scarce baseline data and insufficient scientific research.We argue that remote sensing data can help to better understand degradation processes and patterns by: (1) identifying the distribution of severely degraded areas and (2) comparing the patterns of key spatial attributes of the identified areas (altitude above sea level, aspect, slope, administrative districts) with existing theories on degradation drivers. Therefore, we applied four Landsat 8 images covering large portions of the three counties Jigzhi, Baima and Darlag in the Eastern Tibetan Plateau. The dates of the Landsat scenes were selected to cover differing phenological stages of the ecosystem. Reference data were collected with a remotely piloted aircraft and a standard consumer RGB camera. To exploit the phenological information in the Landsat data as well as deal with the problem of cloud cover in multiple images, we developed a straightforward PCA-based procedure to merge the Landsat scenes. The merged Landsat data served as input to a supervised support vector machine classification which was validated with an iterative bootstrap procedure and an additional independent validation set. The considered classes were “high-cover grassland”, “grassland (including several stages of grassland vitality)”, “(severely) degraded grassland”, “green shrubland”, “grey shrubland”, “urban areas” and “water bodies”. Kappa accuracies ranged between 0.84 and 0.93 in the iterative procedure, while the independent validation led to a kappa accuracy of 0.76. Mean producer’s and user’s accuracies for all classes were higher than 80%, and confusion mainly occurred between the two shrubland classes and between the three grassland classes.Analysis of the slope, aspect and altitude values of the vegetation classes revealed that the degraded areas mostly occurred at the higher altitudes of the study area (4300–4600 m), with no strong connection to any specific slope or aspect. High-cover grassland was mostly located on sunny slopes at lower altitudes (less than 4300 m), while shrubland preferred shady, relatively steep slopes across all altitudes. These observations proved to be stable across the examined counties, while the proportions of land-cover classes differed between the examined regions. Most counties showed 5–7% severely degraded land cover. Darlag, the county located at the edge of the permafrost zone, and featuring the highest average altitude and lowest annual temperature and precipitation, was found to suffer from larger areas of severe degradation (14%).Therefore, our findings support a strong connection between degradation patterns and climatic as well as altitudinal gradients, with an increased degradation risk for high altitude areas and areas in colder and drier climatic zones. This is relevant information for pastoral management to avoid further degradation of high altitude pastures.     

两次华南飑线卫星亮温特征与强对流天气分析

利用Himawari-8高时空分辨率红外亮温资料和ERA-Interim再分析资料,对比冷锋型和暖区型飑线个例("4·13"和"5·6")亮温特征与雷暴大风、地面强降水的关系,结果表明:(1)两次过程的云顶最低亮温、冷云区平均亮温差异小,但两次过程初生阶段云型不同,"4·13"与"5·6"相比,冷云顶面积较小、持续时间较短、移动速度较快;(2)"4·13"("5·6")的亮温梯度大值区主要位于冷云区东南侧(西南侧),与云团移动方向平行(垂直);两次过程雷暴大风与亮温梯度均具有较好的空间对应关系,亮温梯度增大超前于雷暴大风增强,可作为提前预警指标;(3)"4·13"地面强降水集中分布在低亮温区西侧,原因为风暴顶前移导致强降水与冷云区具有空间位置差异;"5·6"地面强降水则与云顶低亮温具有较好的对应关系。     

2017年“海棠”台风影响辽宁不同区域极端暴雨成因分析

利用常规观测、NCEP FNL、葵花8号卫星、GNSS反演大气可降水量、智能网格实况产品等资料,分析2017年“海棠”台风造成辽宁西部朝阳地区和东南部岫岩县的极端暴雨成因。结果表明：辽宁西部和东南半岛均出现区域性的极端特大暴雨,岫岩县小时雨强更大,最大雨强达到113 mm
83;h^-1,对流性降水特征明显。两个区域暴雨过程均受到热带、副热带、西风带系统共同作用,狭长型“海棠”台风沿着副热带高压西侧逐渐北上,并且与西风带短波槽相互作用,导致辽宁西部出现强降水,随后加强的涡旋系统后侧干冷空气与低空暖湿水汽输送带相互作用,导致岫岩县出现极端暴雨过程。热带台风“奥鹿”对副热带高压南落东退起到阻挡作用。两个区域均具有来自于南海的水汽通道,另外东南半岛也受到了“奥鹿”台风北侧水汽输送的影响。朝阳市和岫岩县大气可降水量值长时间接近65 mm和70 mm,异常指数最高达到3.0和2.5,表明此次暴雨水汽条件的极端性。辽宁西部降水期间动力不稳定更强,辐合层由地面伸展到500 hPa,而东南半岛降水期间上干下湿的水汽分布以及更强的冷暖空气交汇,有利于产生对流性降水。两个区域均受到多个中尺度云团的共同影响,朝阳地区初期降水由中γ尺度辐合线触发,后期台风在北上过程中与高空槽后部的干冷空气相互作用,形成的暖锋云系以及冷锋云系导致朝阳地区出现持续性强降水;加强的涡旋后部干空气侵入到暖湿水汽输送带中,配合岫岩县山区地面辐合线稳定不动,不断有积云触发并且直接影响岫岩县,导致岫岩县产生极端对流性暴雨。