Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Parallel Technologies with Image Processing Using Inverse Filter

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

Reservoir history matching and inversion using an iterative ensemble Kalman filter with covariance localization

Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.

An improved particle filtering algorithm based on observation inversion optimal sampling

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.

Pre-stack AVO inversion with adaptive edge preserving smooth filter regularization based on Aki-Richard approximation

With the development of exploration of oil and gas resources,the requirements for seismic inversion results are getting more accurate.In particular,it is hoped that the distribution patterns of oil and gas reservoirs can be finely characterized,and the seismic inversion results can clearly characterize the location of stratigraphic boundaries and meet the needs of accurate geological description.Specifically,for pre-stack AVO inversion,it is required to be able to distinguish smaller geological targets in the depth or time domain,and clearly depict the vertical boundaries of the geological objects.In response to the above requirements,we introduce the preprocessing regularization of the adaptive edge-preserving smooth filter into the pre-stack AVO elastic parameter inversion to clearly invert the position of layer boundary and improve the accuracy of the inversion results.

A high resolution inversion method for fluid factor with dynamic dryrock V_(P)/V_(S) ratio squared

As an important indicator parameter of fluid identification,fluid factor has always been a concern for scholars.However,when predicting Russell fluid factor or effective pore-fluid bulk modulus,it is necessary to introduce a new rock skeleton parameter which is the dry-rock VP/VS ratio squared(DVRS).In the process of fluid factor calculation or inversion,the existing methods take this parameter as a static constant,which has been estimated in advance,and then apply it to the fluid factor calculation and inversion.The fluid identification analysis based on a portion of the Marmousi 2 model and numerical forward modeling test show that,taking the DVRS as a static constant will limit the identification ability of fluid factor and reduce the inversion accuracy.To solve the above problems,we proposed a new method to regard the DVRS as a dynamic variable varying with depth and lithology for the first time,then apply it to fluid factor calculation and inversion.Firstly,the exact Zoeppritz equations are rewritten into a new form containing the fluid factor and DVRS of upper and lower layers.Next,the new equations are applied to the four parameters simultaneous inversion based on the generalized nonlinear inversion(GNI)method.The testing results on a portion of the Marmousi 2 model and field data show that dynamic DVRS can significantly improve the fluid factor identification ability,effectively suppress illusion.Both synthetic and filed data tests also demonstrate that the GNI method based on Bayesian deterministic inversion(BDI)theory can successfully solve the above four parameter simultaneous inversion problem,and taking the dynamic DVRS as a target inversion parameter can effectively improve the inversion accuracy of fluid factor.All these results completely verified the feasibility and effectiveness of the proposed method.

An improved four-dimensional variation source term inversion model with observation error regularization

Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.

Coastal bathymetry inversion using SAR-based altimetric gravity data:A case study over the South Sandwich Island

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography.Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions.The Synthetic Aperture Radar(SAR)altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution.In this study,we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry,where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated.Furthermore,we study the effects on bathymetry modeling by using different scale factor calculation approaches,where a partition-wise scheme is implemented.The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SARbased altimetric gravity data improves local coastal bathymetry modeling,compared with the model calculated without SAR altimetry data by a magnitude of 3:55 m within 10 km of offshore areas.Moreover,by using the partition-wise scheme for scale factor calculation,the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method.These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

唐山市大气颗粒物和O_(3)多尺度变化及影响因素

采用KZ滤波法、多元逐步回归法和小波相干性分析法,从不同时间尺度探究了唐山市2015~2022年间PM_(2.5)、PM_(10)和O_(3)的演变特征,并有效区分和定量估算了污染源排放和气象因素对污染物浓度的贡献,揭示了气象因素对污染物不同尺度的影响,以及颗粒物和O_(3)之间的协同作用机制.结果表明:研究期间唐山市颗粒物PM_(2.5)和PM_(10)的浓度长期分量均呈现显著下降趋势,季节分量和短期分量均呈现不同程度的周期波动.O_(3)浓度长期分量变化幅度较小,其季节分量和短期分量均在每年5~7月之间有明显变化趋势.颗粒物PM_(2.5)和PM_(10)浓度的长期分量变化主要由源排放因素控制,且源排放贡献占90%以上,而O_(3)浓度的长期分量变化则由源排放和气象因素共同控制,且其贡献比例约为2:3.气象因素温度、相对湿度、地表垂直风速和降水量对PM_(2.5)主要表现为小时间尺度的正向作用和大时间尺度的负向作用.温度和短波辐射强度对O_(3)主要呈正向影响,而PM_(2.5)、PM_(10)和O_(3)之间存在小时间尺度的正向影响和大时间尺度的负向作用.

GNSS/MEMS IMU车载组合导航中IMU比例因子误差的影响分析

从系统状态模型出发,分析比例因子误差对组合导航精度和计算量的影响,同时基于车载运动的特点分析比例因子误差的观测性,提出一种仅保留航向陀螺仪和水平加速度计比例因子误差的降维状态模型。实验结果表明,当比例因子误差大于6×10^(-3)时,增广比例因子误差有助于提高导航精度,但计算量增加约170%;降维模型能够达到高维模型的导航精度,与不增广比例因子误差相比,计算量仅增加约70%。

结合矩阵补全的宽度协同过滤推荐算法

协同过滤是推荐系统中最经典的方法之一,能够满足人们对个性化推荐任务的需求,但许多协同过滤算法在面对评分数据稀疏性问题时推荐效果不佳。为解决此问题,提出一种结合矩阵补全的宽度协同过滤推荐算法。先使用矩阵补全技术对用户项目评分矩阵进行补全,再利用补全后的矩阵对已评分的用户和项目分别寻找其近邻项,进而构造用户与项目的评分协同向量,最后使用宽度学习系统来构建用户项目与评分之间的复杂的非线性关系。在MovieLens和filmtrust数据集上对所提出算法的有效性进行检验。试验结果表明,与当前最先进的方法相比,该方法能够有效地缓解数据稀疏性问题,具有较低的计算复杂度,在一定程度上提升了推荐系统的性能。

基于SVD的复数UKF及电力系统对称分量估计

电力系统对称分量的检测对于电力系统安全稳定的运行具有很重要的意义。利用复数域无迹卡尔曼滤波算法,对三相电压系统的正负序分量及频率进行了估计。为了提高复数无迹卡尔曼滤波的参数估计精度及算法稳定性,引入最优自适应因子并对预测协方差矩阵进行SVD分解,提出了基于SVD的自适应CUKF算法。为消除零序分量,对三相电压分量进行αβ变换,定义了复数形式的状态变量,建立了非线性状态方程及观测方程,实现了正序、负序对称分量估计。通过与普通复数域无迹卡尔曼滤波算法对比,所提研究方法在估计精度及收敛速度等方面优于传统无迹卡尔曼滤波方法。

三维大地电磁测深阶段式自适应正则化反演

如何合理地确定正则化因子是地球物理正则化反演领域的研究热点。阶段式自适应算法可以充分发挥模型稳定器的作用,提高反演结果的稳定性,但是该算法仅在一维、二维大地电磁测深(Magnetotelluric,MT)反演中得以实现。目前,三维MT反演正在快速发展,基于此,本文将阶段式自适应正则化算法引入三维MT正则化反演,按照“阶段”而不是“迭代次数”自适应地调整正则化因子的取值,进而观察反演结果的变化。本文设计单块体和双块体模型试验,并特意设置了较大的迭代次数,进而观察反演结果随反演进程的变化情况。模型试验表明:阶段式自适应算法是适用于三维MT正则化反演的,该算法在反演的后期可以更好地保持解的稳定,故此,从解的稳定性这个角度去考量正则化因子的选择是一种值得探索的方向。

白光显微干涉测量曲面样品形貌误差的校正方法

白光显微干涉术在平面阶跃型结构的形貌测量中具有显著优势。但在测量斜率变化的曲面样品时,由于物镜数值孔径的限制,样品表面反射光随着斜率的增大而减弱,干涉信号对比度降低,导致形貌测量结果的误差增大。基于表面传递函数(surface transfer function, STF)计算得到的逆滤波器可用于校正曲面样品的形貌测量误差,但现有方法的逆滤波器增益受限,无法有效提升频谱中的高频信号,对最大可测量斜率的提升有限。针对该问题,提取由白光干涉仪特性参数计算获得的虚拟STF的模作为振幅增益函数,由干涉图傅里叶变换得到的实测STF的相位作为相位补偿函数,形成虚实融合型逆滤波器,据此实现白光干涉仪曲面形貌测量误差的校正。应用该方法校正微球的形貌测量结果,校正后最大可测量斜率从8.09°提升到21.20°,均方根误差从0.545 5μm降低至0.175 9μm,实现了提升曲面样品的最大可测量斜率和减小测量误差的目的,有效提升了仪器针对曲面样品的测量范围。

基于车载激光点云的城市道路标线提取方法

针对车载激光点云道路标线反射强度特性,提出了一种基于车载激光点云的城市道路标线提取方法。具体而言,首先提出一种联合布料模拟滤波和高差偏度平衡的地面滤波方法,利用偏度平衡滤波的自适应性,剔除布料滤波后残留的低矮植被的问题;随后利用基于法向量密度聚类以提取路面点云,并通过反距离加权插值将路面点云转为强度特征图;为了缓解标线提取出的锯齿状现象,引入快速引导滤波来平滑道路标线的边缘信息;最后采用最大熵阈值分割和形态学比值滤波对道路标线进行精化处理。实验表明,该方法能够有效地提取出道路标线点云,提取的平均召回率为80.98%,平均准确率为96.89%,平均综合评定指标为88.19%,能够利用道路标线点云强度信息较为完整地提取出道路标线点云。

滤波器弹性的深度神经网络通道剪枝压缩方法

深度神经网络(deep neural network,DNN)在各个领域获得了巨大成功,由于其需要高额的计算和储存成本,难以直接将它们部署到资源受限的移动设备端。针对这个问题,对网络中的全局滤波器重要性评估进行了研究,提出滤波器弹性的通道剪枝压缩方法以轻量化神经网络的规模。该方法先设置层间局部动态阈值改进L1正则化(L1 lasso)稀疏训练中剪枝过度的不足;然后将其输出乘以通道缩放因子替换普通的卷积层模块,利用滤波器的弹性大小定义全局滤波器的重要性,其数值由泰勒公式估计得出并排序,同时设计新的滤波器迭代剪枝框架,以平衡剪枝性能和剪枝速度的矛盾;最后利用改进的L1正则化训练和全局滤波器重要性程度进行复合通道剪枝。在CIFAR-10上使用所提方法对VGG-16进行实验,减少了80.2%的浮点运算次数(FLOPs)和97.0%的参数量,而没有明显的准确性损失,表明了方法的有效性,能大规模地压缩神经网络,可部署于资源受限的终端设备。

剪切增稠液微胶囊的制备与性能研究

本研究以壳聚糖材料为壁材,剪切增稠液(STF)为芯材,通过单凝聚法制备了STF微胶囊(STF MCs)。通过单因素实验研究,确定了微胶囊的制备工艺参数:司盘80(Span 80)与吐温80(Tween 80)作为分散剂,复配比例为3∶1;乳化剂用量为11%,核壳比为2∶1,搅拌速度为600 r/min,反应温度为60℃;体系的油水比为1∶2。在此条件下制得的STF MCs呈较为规整的球形,粒径分布较为均匀,且主要集中在3μm左右。结果表明,碳纳米管的引入有效改善了STF的流变性能。掺杂CNTs的STF体系具有更小临界剪切速率,更快的黏度突变,并且峰值黏度增加近一倍;芯材和微胶囊乳液的红外光谱大部分都一致,表明壳聚糖成功吸附在芯材液滴表面,实现了对STF的包封。壁材对芯材STF起到了保护作用,提升了芯材STF的热稳定性。

基于相对离群因子的标签噪声过滤方法

分类任务中含有类别型标签噪声是传统数据挖掘中的常见问题,目前还缺少针对性方法来专门检测类别型标签噪声.离群点检测技术能用于噪声的识别与过滤,但由于离群点与类别型标签噪声并不具有一致性,使得离群点检测算法无法精确检测分类数据集中的标签噪声.针对这些问题,提出一种基于离群点检测技术、适用于过滤类别型标签噪声的方法--基于相对离群因子(Relative outlier factor,ROF)的集成过滤方法(Label noise ensemble filtering method based on rel-ative outlier factor,EROF).首先,通过相对离群因子对样本进行噪声概率估计;然后,再迭代联合多种离群点检测算法,实现集成过滤.实验结果表明,该方法在大多数含有标签噪声的数据集上,都能保持优秀的噪声识别能力,并显著提升各种分类模型的泛化能力.

一种状态约束下的抗差自适应联邦滤波算法

充分利用先验约束信息可以提高多传感器组合导航的定位精度和可靠性。将状态约束下的卡尔曼滤波扩展到传统联邦滤波中,提出一种状态约束下的联邦滤波算法。当子传感器出现异常时,在状态约束下的联邦滤波基础上,采用Huber方法调整子滤波器观测噪声矩阵,同时在信息分配阶段引入自适应信息分配因子,实时调整子滤波器融合权重,得到一种状态约束下的抗差自适应联邦滤波算法,以进一步减少不准确的子滤波器估计对融合结果的影响。将该方法应用在捷联惯导、全球导航卫星系统和里程计的多传感器组合导航系统中。仿真实验表明,状态约束下的联邦滤波估计精度优于传统联邦滤波,状态约束下的抗差自适应联邦滤波能够进一步提高观测异常下的导航定位精度和可靠性。

改进的自适应扩展卡尔曼滤波雷达目标跟踪算法

卡尔曼滤波是雷达目标跟踪场景最常用的目标状态跟踪估计算法,但针对非线性运动模型和噪声模型适配失配后,其滤波算法跟踪精度会出现下降。针对这些问题,提出一种机动目标场景下改进自适应扩展卡尔曼滤波的雷达目标跟踪算法,通过目标位置偏差范围来修正预测的位置信息,使用BP神经网络算法来自适应进行扩展卡尔曼滤波(extended kalman filter,EKF)算法预测信息结果的修正;根据噪声影响情况,提出基于实际情况可调的更新因子,用于进行修正后的EKF预测位置信息、测量信息和修正后的BP-EKF预测信息值的权重处理,基于优化模型,自适应选择最优的位置预测信息。仿真分析表明,所提出的算法在目标跟踪的滤波精度和稳定度都得到提升。