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61.
利用美国ASD公司的FieldSpec Pro FR地面波谱仪,选择5°视场角探头和135cm探头距测试地物高度,开展0°、5°、10°、15°、20°等五种不同地形坡度明、暗地物自然定标场地面波谱测试。通过对比研究不同地形坡度具代表性、能真实反映被测试目标平均自然性的各波谱测试点波谱曲线特征,得出0°、5°地形坡度获取的波谱曲线连续、平滑,信噪比高,没有混入大气水气吸收、仪器噪声等因素导致的剧烈波动、跳跃现象或尖锐锯齿状噪声,波谱特征明显,吸收峰最小值位置清晰可辨,各波谱测试点波谱曲线变化幅度小,整体反射率值变化小,Fe~(2+)、Fe~(3+)、Al-OH、Mg-OH、CO_3~(2-)等分子基团与离子波谱的诊断性波谱特征位置清晰可辨。因此0°和5°坡度地形条件获取的地面同步自然场光谱定标波谱曲线,可以满足航空高光谱遥感数据的大气校正、光谱重建和空、地回归分析等所需的地面波谱数据要求。 相似文献
62.
文中探讨了加权Logistic回归模型在宁芜盆地中段火山岩型铜矿预测中的应用。首先,结合研究区的成矿地质背景,提取地质体、构造、围岩蚀变三大类证据因子;其次,分析各证据因子与铜矿点之间的空间关系,认为姑山旋回、娘娘山旋回火山机构控制了本区火山岩型铜矿的空间分布,根据计算结果,选取与火山岩型铜矿密切相关的龙王山组、姑山组地层,姑山旋回粗面斑岩、娘娘山旋回二长斑岩、NW向构造1.5 km缓冲区、NE向构造1.3 km缓冲区、EW向构造4.5 km缓冲区、硅化、褐铁矿化、黄铜矿化等作为模型自变量;最后采用加权Logistic回归模型进行成矿概率计算,并结合成矿地质背景,圈定四个成矿远景区,分别为P1、P2、P3、P4,其中P1、P2、P3呈北东向展布,主要受娘娘山和姑山火山机构控制,P4为东西向分布,主要受龙王山火山机构控制,在这些预测区中,均存在已发现的铜矿体,说明预测可信度较高。 相似文献
63.
A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection 总被引:1,自引:0,他引:1
To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset. 相似文献
64.
为了进一步了解海岛岬湾海岸表层沉积物粒度的时空分布特征和形成机制,作者以福建东山岛乌礁湾为典型研究区,通过秋、春、夏3个不同季节海滩表层沉积物的采集和点位测量,综合应用激光法和筛析法进行粒度测定。结果表明,东山乌礁湾海滩表层沉积物以0.16~0.50 mm之间的中、细砂为主,并含有少量的粗砂和细砾,这与区域砂质沉积背景有关;湾内从南到北剖面沉积物粒径由粗砂到细砂逐渐变细,主要受剖面地形、局地物源、季节性风浪作用和近岸往复水动力的影响。时间变化上,各取样站位表现出沉积物粒径粗、细不同程度的多种变化趋势,以秋季为参考,整体上表现为由南到北粒级的变小、增大和稳定,这与东北、南南西季风影响下的浪、潮作用以及沿岸流系格局变化下的水动力环境有关。 相似文献
65.
高温刺激导致虾夷扇贝死亡因素的探究 总被引:2,自引:0,他引:2
本文探究了高温刺激导致一龄和二龄虾夷扇贝死亡的原因。在高温刺激以及恢复过程中,两种贝龄的扇贝存活率存在显著差异(P0.05),同时两者的谷丙转氨酶(glutamic-pyruvic transaminase,GPT)活性、p53蛋白含量、总抗氧化能力(total antioxidant capacity,TAOC)以及HSP70(heat shock protein 70)含量在高温刺激以及恢复过程中的变化均存在显著差异。而且这些酶的活性(TAOC活性除外)以及蛋白质含量(p53以及HSP70)均受到贝龄、处理时间以及两者综合作用的显著影响。经过Cox模型分析可知,虾夷扇贝受到高温刺激后的存活受到贝龄、GPT活性、TAOC活性、p53含量以及HSP70含量的显著影响。此外,高温刺激会影响扇贝的心跳频率以及心输出量,从而影响心脏对机体的供氧供血能力,影响扇贝对高温的适应能力。 相似文献
66.
全面分析研究区玄武岩潜水含水层水文地质条件的基础上,对稳定流和非稳定理论确定潜水含水层渗透系数的优缺点进行了对比,认为非稳定井流公式确定玄武岩潜水含水层渗透系数是合理的。Aquifer Test软件基于Neuman井流理论配线法求参原理,运用计算机完成了自动求参,避免了人工配线的不足。该文根据吉林靖宇煤矿研究区玄武岩潜水含水层非稳定流抽水试验,分别用Aquifer Test软件和稳定井流公式进行了求参,并对比分析得出:玄武岩含水层弹性释水作用影响时间较长,利用Neuman潜水井流公式求参可靠,且更逼近现实;用Aquifer Test软件实现了自动求参,具有操作简单规范、可比性好的优点。 相似文献
67.
Wei Dang Jin-Chuan Zhang Xuan Tang Xiao-Liang Wei Zhong-Ming Li Cheng-Hu Wang Qian Chen Chong Liu 《地学前缘(英文版)》2018,9(2):559-575
Measuring gas content is an essential step in estimating the commerciality of gas reserves. In this study,eight shale core samples from the Mouye-1 well were measured using a homemade patented gas desorption apparatus to determine their gas contents. Due to the air contamination that is introduced into the desorption canister, a mathematical method was devised to correct the gas quantity and quality.Compared to the chemical compositions of desorbed gas, the chemical compositions of residual gas are somewhat different. In residual gas, carbon dioxide and nitrogen record a slight increase, and propane is first observed. This phenomenon may be related to the exposure time during the transportation of shale samples from the drilling site to the laboratory, as well as the differences in the mass, size and adsorptivity of different gas molecules. In addition to a series of conventional methods, including the USBM direct method and the Amoco Curve Fit(ACF) method, which were used here for lost gas content estimation, a Modified Curve Fit(MCF) method, based on the 'bidisperse' diffusion model, was established to estimate lost gas content. By fitting the ACF and MCF models to gas desorption data, we determined that the MCF method could reasonably describe the gas desorption data over the entire time period, whereas the ACF method failed. The failure of the ACF method to describe the gas desorption process may be related to its restrictive assumption of a single pore size within shale samples. In comparison to the indirect method, this study demonstrates that none of the three methods studied in this investigation(USBM, ACF and MCF) could individually estimate the lost gas contents of all shale samples and that the proportion of free gas relative to total gas has a significant effect on the estimation accuracy of the selected method. When the ratio of free gas to total gas is lower than 45%, the USBM method is the best for estimating the lost gas content, whereas when the ratio ranges from 45% to 75% or is more than 75%, the ACF and MCF methods, are the best options respectively. 相似文献
68.
Subrata Mondal Sujit Mandal 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2018,12(1):29-44
The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making. 相似文献
69.
70.
四川省青川县滑坡灾害群发,点多面广,区域滑坡灾害预警是有效防灾减灾的重要手段,预警模型是成功预警的核心。由于研究区滑坡诱发机理复杂、调查监测大数据及分析方法不足等原因,传统区域地质灾害预警模型存在预警精度有限、精细化不足等问题。文章在青川县地质灾害调查监测和降水监测成果集成整理与数据清洗基础上,构建了青川县区域滑坡灾害训练样本集,样本集包括地质环境、降雨等27个输入特征属性和1个输出特征属性,涵盖了青川县近9年(2010—2018年)全部样本,数量达1 826个(其中,正样本613个,负样本1 213个)。基于逻辑回归算法,对样本集进行5折交叉验证学习训练,采用贝叶斯优化算法进行模型优化,采用精确度、ROC曲线和AUC值等指标校验模型准确度和模型泛化能力。其中,ROC曲线也称为“受试者工作特征”曲线;AUC值表示ROC曲线下的面积。校验结果显示,基于逻辑回归算法的模型训练结果准确率和泛化能力均较好(准确率94.3%,AUC为0.980)。开展区域滑坡实际预警时,按训练样本特征属性格式,输入研究区各预警单元27个特征属性,调用预先学习训练好的模型,输出滑坡灾害发生概率,根据输出概率分段确定滑坡灾害预警等级。当输出概率P≥40%且P<60%时,发布黄色预警;当输出概率P≥60%且P<80%时,发布橙色预警;当输出概率P≥80%时,发布红色预警。 相似文献