基于梯度提升算法的岩性识别方法

传统的岩性识别方法如岩屑录井、钻井取心及测井资料解释等技术,对录井质量的依赖程度较高,识别精度与效率低,泛化能力差。随着计算机技术的迅速发展,将测井资料与计算机技术相结合开展岩性研究已成为岩性识别的有效手段。本文提出了一种基于梯度提升算法XGBoost和LightGBM的岩性识别方法。以苏里格气田苏东41-33区块下碳酸盐岩储层为例进行测试验证,采用该方法结合测井资料中的声波时差、自然伽马、光电吸收截面指数、密度、深侧向电阻率和补偿中子等6种参数进行岩性识别,并与KNN （K近邻分类器）、朴素贝叶斯和支持向量机等传统算法进行对比,结果表明,3种传统算法的岩性识别准确率分别为78.45%、74.43%和78.72%,基于梯度提升算法XGBoost和LightGBM的识别准确率分别达到了98.90%和98.72%,远高于传统算法。     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

气温变化对西峰黄土高原地温与梨树发育期的影响

用西北地区140站1961-2000年的气温资料和西峰黄土高原1971-2005年5,10,15,20cm地温和1984-2005年梨树发育期资料,分析了西北地区春季、夏季、秋季、冬季和年气温的变化事实,再用相关计算和典型年份对比,分析了地温的时间变化规律及其对梨树发育期的影响。结果表明,西北地区20世纪60年代冬季增温,其余降温,70年代均降温,80年代冬季增温,其余降温,90年代均增温,冬季的最明显。西峰10cm地温各季节呈持续升高的趋势,春季增温最明显为0.058℃/a,变幅也最大,冬季增温幅度次之为0.039℃/a,再是秋季为0.032℃/a,夏季增温幅度最小为0.029℃/a,上升趋势均通过0.05和0.01的信度检验。冬季、春季地温与梨树的各发育期均为负相关,即地温高,发育期早,地温低,发育期迟。冬季地温与梨树发育期相关最显著的是叶变始期和开花始期,相关系数为-0.41～-0.52,信度为0.05,春季地温与之相关最显著的是开花始期,相关系数为-0.68～-0.69,信度达0.001。春季地温对梨树发育期的影响具有明显的持续性和滞后性,冬季地温对梨树发育期影响有阶段性,春季地温对梨树发育期的影响比冬季的明显。     

中国北方磷矿矿床类型及成矿规律

通过对中国北方内生磷灰石、变质及沉积型磷矿成矿特征及典型矿床的研究,建立了各种类型磷矿床的成矿模式和找矿模型、确定了各类型矿床的时空分布规律以及矿床的共生规律,为开展磷矿的找矿工作具有重要参考价值。     

一种针对规则冲突的规则集语义比较算法

应用DFD(Diverse Firewall Design)方法进行防火墙规则集设计时,需使用规则集语义比较算法,判断多个规则集是否语义一致。规则冲突是导致规则集语义不一致的主要原因。因此,需要研究针对规则冲突的规则集语义比较算法。而现有算法时空复杂度较高,性能低下。文中提出了1种基于关键数据包选取映射的规则集语义比较算法(KPCM)。KPCM算法针对规则冲突选取关键数据包,并通过比较这些数据包在不同规则集中的处理动作,判断这些规则集是否语义一致。理论分析和测试表明,KPCM算法时空性能均优于现有算法。     

Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle

A randomized kinodynamic path planning algorithm based on the incremental sampling-based method is proposed here as the state-of-the-art in this field applicable in an autonomous underwater vehicle. Designing a feasible path for this vehicle from an initial position and velocity to a target position and velocity in three-dimensional spaces by considering the kinematic constraints such as obstacles avoidance and dynamic constraints such as hard bounds and non-holonomic characteristic of AUV are the main motivation of this research. For this purpose, a closed-loop rapidly-exploring random tree (CL-RRT) algorithm is presented. This CL-RRT consists of three tightly coupled components: a RRT algorithm, three fuzzy proportional-derivative controllers for heading and diving control and a six degree-of-freedom nonlinear AUV model. The branches of CL-RRT are expanded in the configuration space by considering the kinodynamic constraints of AUV. The feasibility of each branch and random offspring vertex in the CL-RRT is checked against the mentioned constraints of AUV. Next, if the planned branch is feasible by the AUV, then the control signals and related vertex are recorded through the path planner to design the final path. This proposed algorithm is implemented on a single board computer (SBC) through the xPC Target and then four test-cases are designed in 3D space. The results of the processor-in-the-loop tests are compared by the conventional RRT and indicate that the proposed CL-RRT not only in a rapid manner plans an initial path, but also the planned path is feasible by the AUV.     

锁块式水下采油树井口连接器性能分析

水下采油树井口连接器是连接采油树和井口的关键设备,对深海水下勘探开发及采油树的安装连接具有不可或缺的重要意义。以一种锁块式水下连接器为例,介绍其结构组成和工作原理,通过有限元建立连接器的三维模型,分析连接器在下放安装工况、连接锁紧工况、正常生产工况和钻完井工况下的连接性能,得出井口与采油树本体接触面作用力、VX钢圈接触应力和锁块受力情况随外部载荷的相互关系。结果表明,VX钢圈在整个过程中都能够保持良好密封,井口与采油树本体接触面未发生分离,弯矩对连接器连接性能影响很大,在额定弯矩作用时连接器部分锁块已发生弹塑性失效。     

一种海洋混合层深度的智能识别方法研究

文章提出了一种识别混合层深度的人工智能方法。该方法在温度(密度)与压强(或深度)间建立线性模型, 并且将其系数和方差做成一组表征廓线特征的统计量。初始时为模型设定一个主观的先验分布, 在一个自海表向下移动的窗口内通过贝叶斯链式法则和最小描述长度原理学习新数据, 得到系数均值的最大后验概率估计。用F-检验识别系数发生突变的位置, 以此确定混合层的存在性及其深度。通过2017年2月太平洋海域的地转海洋学实时观测阵(Array for Real-time Geostrophic Oceanography, ARGO)数据进行测试, 并且以质量因子(Quality Index, QI)值作为判断识别混合层深度结果准确性的依据, 发现该方法相比于梯度法、阈值法、混合法、相对变化法、最大角度法和最优线性插值法在识别结果上具备更大的QI值。表明该方法能够准确识别混合层深度。     

结合MST划分和RHMRF-FCM算法的高分辨率遥感图像分割

针对基于像素的HMRF-FCM算法抗噪性差以及对地物复杂边界分割精度低的问题,提出一种结合形状信息的静态MST区域划分和RHMRF-FCM算法的高分辨率遥感图像分割方法。该方法定义一种静态MST同质区域划分准则,借助MST能较好表达边界和形状信息、能较好抑制几何噪声的特点,解决地物复杂边界的表达和降低分割结果中几何噪声问题。首先,利用MST静态划分将图像域划分成若干个均质区域,假设每个均质区域内光谱测度服从独立同一的多元高斯分布。然后,在此基础上构建了区域隐马尔可夫随机场模型,以及建立基于信息熵和KL信息正则化项的模糊聚类目标函数。最后,采用偏微分方法对分割模型参数进行求解,从而得到全局最优分割结果。为验证本文方法,对WorldView-3高分遥感图像进行分割试验。定性、定量分析了尺度参数、光谱相似性参数和区域紧致度参数对最优分割结果的影响,并对比分析本文算法和eCognition软件中的多分辨率分割算法、分水岭算法。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.