东天山海相火山岩型铁矿区域成矿规律研究

选择东天山阿齐山-雅满苏-沙泉子一带海相火山岩型铁矿区作为研究区,从区域成矿地质背景入手,首先分析古火山机体成矿构造聚敛场、海盆成矿构造聚敛场对海相火山岩型铁矿的控制作用,依次总结了沉积岩相建造、火山岩建造、侵入岩、变质作用对铁矿的控制作用,最后全面系统的总结出东天山地区海相火山岩型铁矿在时间和空间上成矿规律和演化过程,指明该类型铁矿在宏观场和微观场中的识别标志,对该区域海相火山岩型铁矿勘查工作提供一定技术指导.     

利用HOG-LBP自适应融合特征实现禁令交通标志检测

针对我国圆形禁令交通标志,提出一种快速、稳定的标志自动检测方法。首先,基于RGB空间对标志图像进行颜色分割,针对三通道差值的固定阈值分割方法对图像亮度的适应性较差的问题,提出基于基础通道选择的相对通道差值计算及阈值曲线拟合的方法进行自适应颜色分割,并针对标志与背景同色的情况,提出梯度过滤的方法将标志与同色背景分离;然后对分割图像进行边缘提取,基于标志椭圆特征,用最小二乘椭圆拟合后验偏差估计法来对边缘进行筛选,并提取最终标志边缘。实验表明,该方法能快速准确地对自然环境下不同亮度的圆形禁令标志自动检测,具有实时性和鲁棒性,在智能交通中具有良好的应用前景。     

绞窄性肠梗阻MDCT表现

目的：探讨急诊中绞窄性肠梗阻的多排螺旋CT（MDCT）的影像表现。方法：分析经手术证实的29例绞窄性肠梗阻（SIO）患者的临床资料及MDCT资料,经多方位重建和多窗位的技术处理,对绞窄性肠梗阻患者CT的肠管影像改变（肠腔的扩张、积液、肠壁厚度、密度的改变）,肠系膜的CT影像改变（肠系膜的模糊积液、旋转、门静脉的变化）,腹腔内的CT影像改变（腹水、气腹）及增强CT影像改变（肠壁的强化改变,肠系膜血管是否栓塞）深入研究。结果：29例患者中共检出肠腔扩张、积液26例,肠壁密度变化、增厚18例,肠壁、门静脉内积气2例,肠系膜的模糊、积液19例,缆绳征11例,漩涡征9例,鸟嘴征5例,腹腔积液12例,肠系膜血管栓塞3例及肠壁不强化5例。结论：多排螺旋CT识别绞窄性肠梗阻的MDCT征象细节对提高绞窄性肠梗阻的诊断及其鉴别诊断有较好的应用价值。     

用Euler反褶积方法反演台湾海峡磁异常

用Ｅｕｌｅｒ反褶积方法反演台湾海峡磁异常,选取的窗口不能大,而决定取舍反褶积解的误差限又不能小,这势必对解的质量有较大影响．为此,不仅用了不同延深的单体模型,而且进行了多体模型实验．结果表明,复杂分布的磁性体,用Ｅｕｌｅｒ反褶积方法确定磁性体的轮廓可能是困难的,但可确定磁性体的水平位置和深度,从而降低了对资料精度和计算参数选取的要求．在磁异常比较复杂的地区．     

黑山铜镍矿重磁电异常解释

通过钻孔与地面物性测定,查明含矿岩体和硅质板岩都具有较高的极化率和相对较低的电阻率,是引起电性异常的主要因素。利用重磁异常推断岩体的空间分布形态为一向下延伸长度大于走向长度的岩柱。由重磁异常圈定的岩体范围内电性异常为矿体引起,其外为岩性异常,这是区分矿与非矿异常排除干扰的最佳标志。     

一种地下瞬变电磁法场值曲线的解释方法

均匀大地表面上大回线源在地下形成的瞬变电磁场解析计算式很复杂,无法用常规方法定义其视电阻率,而场值曲线本身分辨率低,进行分层解释困难。对大回线源在地下形成的瞬变电磁场场值,用由第一层介质充满半无限大地时的场值进行归一化的方法进行解释,经理论计算表明,与地面装置条件下的电阻率曲线进行解释有异曲同工之妙     

第二次土地调查中一些问题的探讨

介绍了平面坐标转换的数学模型及转换控制点筛选基本方法,遥感影像解译方法;讨论了外业调绘和矢量化以及跨带图幅的拼接及对面积的影响。     

第二次农村土地调查技术与实践

第二次土地调查工作,包括农村土地调查和城镇土地调查两部分,是一项统一由政府组织实施的重大任务。结合生产实践阐述了基于遥感影像建立影像解译标志,并在GIS软件上解译影像信息,采用内外业相结合方法,全面掌握城镇、村庄以及独立工矿区内部工业用地、基础设施用地、住宅用地以及农村宅基地等行业用地、农用地、未利用地的数量和分布,并建立土地利用现状数据库的过程。     

Seismic facies analysis through musical attributes

Seismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called sonification, mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.     

An automated cross‐validation method to assess seismic time‐to‐depth conversion accuracy: a case study on the Cooper and Eromanga basins,Australia

Selecting a seismic time‐to‐depth conversion method can be a subjective choice that is made by geophysicists, and is particularly difficult if the accuracy of these methods is unknown. This study presents an automated statistical approach for assessing seismic time‐to‐depth conversion accuracy by integrating the cross‐validation method with four commonly used seismic time‐to‐depth conversion methods. To showcase this automated approach, we use a regional dataset from the Cooper and Eromanga basins, Australia, consisting of 13 three‐dimensional (3D) seismic surveys, 73 two‐way‐time surface grids and 729 wells. Approximately 10,000 error values (predicted depth vs. measured well depth) and associated variables were calculated. The average velocity method was the most accurate overall (7.6 m mean error); however, the most accurate method and the expected error changed by several metres depending on the combination and value of the most significant variables. Cluster analysis tested the significance of the associated variables to find that the seismic survey location (potentially related to local geology (i.e. sedimentology, structural geology, cementation, pore pressure, etc.), processing workflow, or seismic vintage), formation (potentially associated with reduced signal‐to‐noise with increasing depth or the changes in lithology), distance to the nearest well control, and the spatial location of the predicted well relative to the existing well data envelope had the largest impact on accuracy. Importantly, the effect of these significant variables on accuracy were found to be more important than choosing between the four methods, highlighting the importance of better understanding seismic time‐to‐depth conversions, which can be achieved by applying this automated cross‐validation method.