基于GPS和水准数据的微粒群算法联合反演祁连山北缘断裂三维滑动速率     

刘杰 《大地测量与地球动力学》2019,39(9):906-909

将微粒群算法与位错理论模型相结合，采用中国地壳运动观测网络提供的青藏高原地区2001~2004年GPS测量数据和2000~2006年水准测量数据，通过常规定权和附有相对权比的方法对祁连山北缘断裂的三维滑动速率进行联合反演，并与蚁群算法反演结果进行对比。结果表明，微粒群算法收敛速度快、稳定性高，结合经典位错理论模型，是一种可以有效求解断层三维滑动速率反演问题的优化算法，在大地测量反演领域极具应用潜力。  相似文献   

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process     

《地学前缘(英文版)》2020,11(6):2207-2219

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

基于最大球算法定量表征土体空间孔隙网络     

解朝阳  张巍  姚东方  王坚  朱雨辰 《工程地质学报》2020,28(1):60-68

孔隙网络控制着土体渗流、排水固结与基质吸力等重要工程性质。本文介绍了多孔介质孔隙网络最大球建模基本原理与算法。以显微CT扫描振捣干法生成的南京粉砂试样为例,采用最大球算法建立了试样三维重构模型表征单元体(REV)的空间孔隙网络球棍模型,计算得到了样品REV尺度的孔隙网络参数,统计发现,孔隙半径、喉道半径、孔隙配位数、孔隙截面形状因子、喉道截面形状因子与喉道长度等孔隙参数均近似服从正态分布,孔隙体积近似服从衰减型指数分布。孔隙与喉道半径分别分布在100μm与65μm以内,两者数学期望分别为40. 0μm与18. 0μm;配位数分布在25以内,数学期望为5. 1;孔隙与喉道截面形状因子分别分布在0. 01~0. 04与0. 01~0. 05的区间内,两者数学期望分别为0. 019与0. 033;喉道长度分布在100~800μm以内,数学期望为292. 22μm。同时发现,样品中体积小于1. 5×10^7μm^3的小孔隙数量超过90%。本方法可应用于土体细观孔隙结构的定量表征。  相似文献   

基于CUDA的地震相干体并行算法   总被引：1，自引：0，他引：1  

张全 《地质与勘探》2020,56(1):147-153

相干体技术在地震勘探资料解释方面得到了广泛的应用,由于相干体技术处理的对象是三维地震数据体,所以算法运算时间较长。为了缩短解释周期,本文充分发挥GPU并行计算优势,对C3相干体算法进行并行化分析。从硬盘读取数据到GPU上计算相干值并写入硬盘的整个过程进行分析,剔除了冗余数据的读取,完成了C3相干体算法的并行化设计与实现。最后分别对串行算法与并行算法进行性能测试,结果表明本文设计的并行算法在保证精度的前提下达到了16倍左右的加速比,对加快地震资料解释具有重要意义。  相似文献   

考虑不确定性的地震灾害应急救援设备并行优化设计   总被引：1，自引：0，他引：1       下载免费PDF全文

刘航 《地震工程学报》2018,40(5):1118-1123

由于地震灾害的不确定性，使得应急救援设备运行速率及使用效率均受到影响，需要进行并行优化处理。对此，提出基于双向并行计算的地震灾害应急救援设备优化方法。以地震灾区灾情等级评估结果为基础，将地震等级及应急救援设备，设备及设备之间的关系进行标准化处理，转化为求解最优解问题；在考虑不确定性的情况下，通过通信时间与救援设备需求进行双向并行处理，优化地震灾害应急救援设备。实验结果表明，采用改进方法进行地震灾害应急救援设备并行优化，能够对地震灾害应急救援设备需求量进行准确预测，提高应急救援设备的运行速率，缩短通信时间，提高应急救援设备的使用效率，具有一定的优势。  相似文献   

基于分区间泛化Kriging近似模型的船舶局部结构形状优化     

张干锋  王德禹 《海洋工程》2018,36(6):46-57

为提高Kriging近似模型在船舶结构性能多维度响应预测方面的适用性,对常规Kriging近似模型进行分区间泛化改进:一是对设计样本点的各个维度(分量)进行划区,并在每一个分区间内采用最优拉丁超立方(OLhd)取样;二是引入比例系数w1组合高斯与指数型相关函数提高模型对数据的泛化能力,从而在每个划分的区间内建立泛化的Kriging近似模型。通过SCH测试函数,验证了构造的分区间泛化Kriging模型有效性。结合参数化建模和改进Kriging近似模型对某过渡肘板和舱口角隅边界进行形状优化,优化第一阶段由改进的近似模型通过多岛遗传算法得到全局初步的最优解,第二阶段在初步解的基础上缩小优化变量范围,由少量的FEM计算即可在小范围内搜寻到精确的最优设计变量。结果表明:分区间泛化Kriging近似模型在预测多维度响应时较常规Kriging模型预测精度更高;分阶段的形状优化流程在保证极小误差和缩小计算成本的情况下能够得到理想的应力分布和重量优化结果,有助于船舶结构的轻量化研究。  相似文献   

基于贝叶斯框架的各向异性页岩储层岩石物理反演技术   总被引：1，自引：0，他引：1       下载免费PDF全文

刘财  符伟  郭智奇  刘喜武  刘宇巍 《地球物理学报》2018,61(6):2589-2600

页岩岩石物理建模旨在建立页岩矿物组分、微观结构、流体填充与岩石弹性参数的关系.对四川盆地龙马溪组页岩进行岩石物理建模研究，针对页岩黏土含量高、层间微裂缝发育等特点，利用Backus平均理论描述页岩黏土矿物弹性参数，利用Chapman理论计算与水平微裂缝有关的VTI各向异性，并利用Bond变换考虑地层倾角的影响.提出以黏土矿物纵、横波速度和孔隙纵横比为拟合参数进行岩石物理反演的方法，并引入贝叶斯框架减小反演的多解性.由已知的黏土矿物纵、横波速度和孔隙纵横比作为先验信息，并以测井纵、横波速度作为约束条件建立反演的目标函数，同时利用粒子群算法进行最优化搜索.计算结果表明，基于先验约束和粒子群算法的反演方法能够较准确地反演黏土矿物的弹性参数、孔隙形态参数以及裂缝密度等参数.计算得到的黏土纵、横波速度较高，并且在一定范围内变化，这可能与龙马溪组页岩的黏土矿物组分中具有较高弹性模量的伊利石含量较高有关，同时也与黏土定向排列等微观物性特征有关.反演得到的裂缝密度与纵波各向异性参数ε呈明显的正相关，而与横波各向异性参数γ相关性较小.另外，页岩各向异性参数与黏土垂向的纵横波速度有较强的相关性.  相似文献   

基于F-范数的不确定性平差模型的解算方法     

鲁铁定  朱国红 《大地测量与地球动力学》2018,38(6):557-561

为提高基于F-范数的不确定性平差模型的解算效率，给出直接迭代算法进行参数估计。该算法无需SVD，解算过程简单且易于编程计算，同时给出迭代不收敛时的SVD-解方程算法。二元线性拟合及沉降观测AR模型的算例结果表明，这2种算法正确可行，与SVD-迭代算法具有等价性。当迭代收敛时，宜使用直接迭代算法，收敛速度更快，解算效率更高；当迭代不收敛时，可釆用SVD-解方程算法。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities     

Jayakrishnan Ajayakumar  Eric Shook 《International journal of geographical information science》2020,34(9):1683-1707

ABSTRACT

Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.  相似文献