A Numerical Modelling Method of Fractured Reservoirs with Embedded Meshes and Topological Fracture Projection Configurations

Projection-based embedded discrete fracture model(pEDFM)is an effective numerical model to handle the flow in fractured reservoirs,with high efficiency and strong generalization of flow models.However,this paper points out that pEDFM fails to handle flow barriers in most cases,and identifies the physical projection configuration of fractures is a key step in pEDFM.This paper presents and proves the equivalence theorem,which explains the geometric nature of physical projection configurations of fractures,that is,the projection configuration of a fracture being physical is equivalent to it being topologically homeomorphic to the fracture,by analyzing the essence of pEDFM.Physical projection configurations of fractures may be rigorously established based on this theorem,allowing pEDFM to obtain physical numerical results for many flow models,particularly those with flow barriers.Furthermore,a natural idea emerges of employing flow barriers to flexibly‘cut’the formation to quickly handle the flow problems in the formation with complex geological conditions,and several numerical examples are implemented to test this idea and application of the improved pEDFM.

Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

Infinitesimal dividing modeling method for dual suppliers inventory model with random lead times

As one of the basic inventory cost models, the （Q, τ）inventory cost model of dual suppliers with random procurement lead time is mostly formulated by using the concepts of ＂effective lead time＂ and ＂lead time demand＂, which may lead to an imprecise inventory cost. Through the real-time statistic of the inventory quantities, this paper considers the precise （Q, τ） inventory cost model of dual supplier procurement by using an infinitesimal dividing method. The traditional modeling method of the inventory cost for dual supplier procurement includes complex procedures. To reduce the complexity effectively, the presented method investigates the statistics properties in real-time of the inventory quantities with the application of the infinitesimal dividing method. It is proved that the optimal holding and shortage costs of dual supplier procurement are less than those of single supplier procurement respectively. With the assumption that both suppliers have the same distribution of lead times, the convexity of the cost function per unit time is proved. So the optimal solution can be easily obtained by applying the classical convex optimization methods. The numerical examples are given to verify the main conclusions.

Ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints

The reasonable prior information between the parameters in the adjustment processing can significantly improve the precision of the parameter solution. Based on the principle of equality constraints, we establish the mixed additive and multiplicative random error model with equality constraints and derive the weighted least squares iterative solution of the model. In addition, aiming at the ill-posed problem of the coefficient matrix, we also propose the ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints based on the principle of ridge estimation method and derive the U-curve method to determine the ridge parameter. The experimental results show that the weighted least squares iterative solution can obtain more reasonable parameter estimation and precision information than existing solutions, verifying the feasibility of applying the equality constraints to the mixed additive and multiplicative random error model. Furthermore, the ridge estimation iterative solution can obtain more accurate parameter estimation and precision information than the weighted least squares iterative solution.

A Data-Driven Car-Following Model Based on the Random Forest

The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re- presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em- ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car- following behavior with better performance under multiple performance indicators.

Bias and Mean Square Error of Reliability Estimators under the One and Two Random Effects Models: The Effect of Non-Normality

The coefficient of reliability is often estimated from a sample that includes few subjects. It is therefore expected that the precision of this estimate would be low. Measures of precision such as bias and variance depend heavily on the assumption of normality, which may not be tenable in practice. Expressions for the bias and variance of the reliability coefficient in the one and two way random effects models using the multivariate Taylor’s expansion have been obtained under the assumption of normality of the score (Atenafu et al. [1]). In the present paper we derive analytic expressions for the bias and variance, hence the mean square error when the measured responses are not normal under the one-way data layout. Similar expressions are derived in the case of the two-way data layout. We assess the effect of departure from normality on the sample size requirements and on the power of Wald’s test on specified hypotheses. We analyze two data sets, and draw comparisons with results obtained via the Bootstrap methods. It was found that the estimated bias and variance based on the bootstrap method are quite close to those obtained by the first order approximation using the Taylor’s expansion. This is an indication that for the given data sets the approximations are quite adequate.

联合比估计法在敏感性问题调查中的应用

文章基于定性特征敏感性随机化模型——分层抽样下的Warner模型,运用联合比估计法对敏感性问题调查比例的估计量及估计误差进行了理论推导。通过效率比较得出:当各层样本量较大、比估计有效时,联合比估计法的精度优于分层估计法的精度。经调查实例验证,上述结论正确。

基于随机游走麻雀搜索算法的多特征结构尺寸熔融沉积成型工艺参数优化

在熔融沉积成型过程中,打印参数对成型样件精度有着重要影响.为了提高整体尺寸精度,采用随机游走的麻雀算法获得最优实验方案.首先,以熔融沉积成型的分层厚度、喷头温度、打印速度和填充率为实验变量设计4因素4水平的正交试验;然后,以样件不同特征结构尺寸的相对误差为优化对象,使用田口-灰色关联法对实验数据进行处理;最后,通过随机游走的麻雀算法计算最优参数方案.结果表明,相比常用的田口-灰色关联法,采用优化后工艺参数成型样件的综合尺寸精度提高了20%,灰色关联度提高了27%.

考虑气体的渗流应力耦合模型在黄壁庄水库中的应用

目前建立的多孔介质耦合模型通常认为气体是不可压缩的,无法描述气体的输运过程,无法反映气体对水体入渗和固体变形的影响。为了描述多孔介质中固液气三相的渗流应力耦合过程,基于多孔介质理论和有效应力原理,提出了一种耦合了改进守恒水平集(ICLS)方法并考虑气体可压缩性的固液气三相渗流应力耦合模型。将该方法应用于黄壁庄水库坝前冒泡现象的渗流应力耦合分析中,模拟了水位骤升后,固体变形、水体入渗和气体运移过程,及其耦合效应。计算结果表明,该方法能够简单直观的描述水气两相的运移过程及渗流应力耦合过程,且模拟结果与实际观测结果基本一致。

考虑水库泄水影响的逐网格汇流演算方法

为将网格新安江(Grid-XAJ)模型更好地应用于受水库泄水影响地区的洪水模拟及预报,以横排头流域为研究区域,基于水库真实地理位置,利用分段马斯京根(MK)法、逐网格MK法、逐网格Muskingum-Cunge-Todini(MCT)法对比分析水库泄水对模型汇流计算的影响。结果表明:3种方法对洪峰的模拟情况均较好,率定期与验证期全部洪水的平均合格率均为100%;逐网格MK法与分段MK法全部洪水的平均径流深合格率均为76.9%,小于逐网格MCT法(84.6%);与分段MK法相比,逐网格MCT法与逐网格MK法可以反映上游水库泄水对汇流过程中流量在流域中时空分布的影响;逐网格MCT法在逐网格MK法的基础上进一步考虑了下垫面河道的真实情况;总体而言,逐网格MCT法对于受水库泄水影响流域的洪水预报具有较强的适应性和可靠性。

基于随机森林模型的长江流域分区多源融合降水模拟方法研究

基于3种卫星降水产品,提出了一种基于随机森林模型的长江流域分区多源融合降水模拟算法(FCM-RF算法)。采用模糊C均值算法,结合地面观测站点资料对长江流域进行降水区域划分,引入降水比降刻画降水空间性,进一步通过普通克里金插值法优化融合结果,得到一套长江流域空间分辨率为0.25°×0.25°的多源融合降水产品,并对其进行了评估。结果表明:FCM-RF算法在长江流域具有良好的表现,可以有效提高原始卫星降水产品对于降水事件的捕捉能力,在验证站点模拟降水量与实测降水量的相关系数可达到0.76;FCM-RF算法在年际上具有相似变化特征,对于春秋季降水的敏感性较高,在夏季由于强降水影响表现欠佳,冬季由于雨量稀少、存在固态降水,呈现出误差小、相关系数较低的特点;FCM-RF算法在东南地区具有较强的降水捕捉能力,在青藏高原地区的准确性较低。

基于反射率法正演模拟的含气储层反射系数频变规律研究

振幅随偏移距/入射角的变化(AVO/AVA)技术已在油气勘探中广泛应用,其随频率的变化特征对岩性研究、储层预测和油气检测具有重要的指示作用。频变特征不仅与含气储层自身吸收衰减产生的速度频散有关,还与地震波在储层顶底之间发生的多次反射有关。本研究基于反射率法建立层状介质的频变反射系数递推公式,利用Chapman模型引入频散速度,提出一种充分考虑含气储层本身吸收衰减与层状结构的AVO精细模拟方法。并在此基础上深入分析了含气储层的厚度、含气饱和度、孔隙度及渗透率与频变反射系数之间的关系,为采用频变规律开展含气储层识别提供了依据。

正演模拟在火山岩储层中的应用

火山岩储层作为油气勘探开发新领域,受到国内外学者的广泛关注。火山岩岩性、岩相复杂,横向变化大,地震波场数值模拟可以为地震资料的采集、处理和解释提供有效指导。本文在调研较为广泛文献的基础上,综述了火山岩正演模拟研究的现状,包括地质模型的建立、数值模拟方法的选择。研究认为:国内外现对于火山岩储层正演模拟没有一个普适的规律;火山岩地质建模技术尚未取得有效进展,建立火山岩岩相、火山机构地质模型进行波场模拟研究更有价值和意义;数值模拟时兼顾震源函数、边界条件可以改善地震波场信息。

基于高光谱技术的穿心莲药材中穿心莲内酯类成分检测研究

基于高光谱技术结合化学计量学,建立不同种质穿心莲药材中穿心莲内酯类成分含量的检测方法。采集穿心莲样品的高光谱信息,获得原始光谱数据(Raw Data)。采用一阶导数(D1)、二阶导数(D2)、SG平滑(SG)、乘性散射校正(MSC)对Raw Data预处理,结合偏最小二乘判别分析(PLS-DA)建立分类模型,结合偏最小二乘回归(PLSR)、反向传播神经网络(BPNN)、随机森林回归(RFR)建立回归模型。应用连续投影算法(SPA)简化模型。不同种质的穿心莲最佳分类模型为D1-PLS-DA。穿心莲内酯、新穿心莲内酯、去氧穿心莲内酯、脱水穿心莲内酯4种穿心莲内酯类化合物总含量的最佳回归模型分别为SG-PLSR、MSC-PLSR、Raw Data-SPA-BPNN、MSC-SPA-BPNN和Raw Data-PLSR。应用高光谱技术可实现穿心莲品质的快速准确检测。

四川省滑坡灾害危险性评价模型比较研究

为探讨四川省滑坡灾害分布状况及对滑坡灾害危险性评价建模效果进行比较,以四川省为研究区,分析地质灾害尤其是滑坡灾害的时空分布,运用信息量法、基于信息量的逻辑回归法、随机森林法3种方法,取坡度、植被覆盖率、地震烈度、断层密度及年累计降雨量5个因子建立危险性评价模型,结合ROC曲线和AUC值对模型性能进行精度比较。研究结果表明:(1)坡度因子对滑坡灾害的贡献最大,3种评价模型中坡度、地震烈度及降雨3个因子均有较大贡献。(2)滑坡灾害主要分布于地震带附近,在成都平原处灾害发生几率最小。(3)3种评价方法均适用于滑坡危险性区划工作,具有良好的建模效果。相对于其他两种模型,逻辑回归模型在实际情况下更加适合四川省滑坡灾害的危险性评价工作。

基于Seed-PCG法的列车-轨道-地基土三维随机振动GPU并行计算方法

为了解决列车-轨道-地基土三维有限元模型随机多样本计算效率低的问题,本文提出了一种基于Seed-PCG法的高效并行计算方法。基于有限元法和虚拟激励法建立轨道不平顺激励下的三维列车-轨道-地基土耦合随机振动分析模型;针对车致地基土随机振动分析产生的多右端项线性方程组求解问题,采用Seed-PCG方法进行求解。通过PCG方法求解种子系统得到的Krylov子空间进行投影,以改进其余线性方程组的初始解和对应的初始残量,有效提高了PCG法的收敛速度,最后,在MATLABCUDA混合平台上开发了并行计算程序。数值算例表明:相同计算平台下的该方法相比多点同步算法获得了104.2倍的加速;相比PCG法逐个求解方案减少了18%的迭代次数,获得了1.21倍的加速。

基于随机森林模型的近岸海域水质污染预测预警方法研究

常规的近岸海域水质污染预测预警方法以水环境监测为主,受到温度、湿度、浊度等水质参数的影响,污染预测预警不准确将影响近岸水域水质保护。设计了基于随机森林模型的近岸海域水质污染预测预警方法,采集并预处理近岸海域水质数据,基于随机森林模型预测近岸海域水质污染物的空间布局,将水质污染物数量作为随机森林树的数量,再将污染预警等级作为树的深度,通过交叉验证的方式优化随机森林模型参数,满足水质污染预测需求,将近岸海域水质污染物浓度预测值作为预警阈值,划分出近岸海域水质污染预警等级,确保预警效果。通过对比实验,验证了该方法的预测预警效果更加准确,能够应用于实际生活中。

数字经济对乡村振兴发展的影响及机制研究

近年来,数字经济的蓬勃发展对乡村振兴发展的影响极大。文章选取2011—2022年中国30个省份的面板数据,采用熵权法和固定效应模型,探究数字经济对乡村振兴发展的影响机制和程度,并创新性地使用随机森林模型,深入考察影响乡村振兴发展的数字经济各项指标的重要性。研究发现:(1)数字经济发展对乡村振兴工作具有显著的促进作用,影响效应为0.4100。(2)数字经济对林业大省乡村振兴的促进作用较其他地区更为显著,影响系数为0.5700,说明数字经济在林业经济的应用已取得了显著成效。(3)数字经济影响乡村振兴最重要的因素是企业拥有网站数和信息从业人数,重要性分别为13.62和11.61,农村电商有望成为乡村振兴的新增长极。(4)数字经济对乡村振兴的促进作用有明显的区域异质性,影响系数分别为东部地区0.6450、西部地区0.2560、东北部地区0.2350、中部地区0.1970。文章聚焦于数字经济各个维度指标的重要性比较以及林业经济大省和其他省份的异质性分析,深化了相关研究内容,弥补了现有文献对林业经济大省数字经济研究的不足。基于此提出政策启示:乡村振兴工作中应利用好数字经济这一推手,重点推动农村电子信息制造业和农村电商,推进林业大省和其他地区乡村振兴工作低成本、高质量发展。

基于SSA-RF的SAR图像变化检测方法

为了提高合成孔径雷达(Synthetic Aperture Radar, SAR)图像变化检测精度,提出一种基于麻雀搜索算法优化随机森林(Sparrow Search Algorithm-Random Forest, SSA-RF)的SAR图像变化检测方法。该方法首先通过对数比值法得到两时相SAR图像的差异图,然后在差异图预处理后进行层次模糊C均值(Fuzzy C-means, FCM)聚类预检测,生成有效标签。最后,利用标签训练随机森林二次检测模型,并采用麻雀搜索算法对随机森林进行参数优化构建性能稳定的分类器,得到差异图变化类与不变化类的分类结果,从而实现SAR图像的变化检测。实验结果表明,该方法较好地保留了变化区域信息,有效提高了SAR图像的变化检测精度。