不同刚度植物杆群对规则波传播及紊动特性影响研究

以不同刚度硅胶圆杆群为概化植物模型,测定其抗弯弹性模量,通过波浪水槽实验,研究规则波在不同刚度植物杆群内的流速分布、紊动特征及不同刚度杆群的消浪效果。实验结果表明,当波浪通过柔性杆群时,受其摆动的影响,流速周期变化从单峰型逐渐转变成双峰型,杆群刚度越小形成的二次波峰越明显;不同刚度杆群内水体紊动强度变化显示,杆群刚度越大,造成杆群内水体的紊动强度越大;随着杆群抗弯弹性模量的增大,其消浪系数也增大,消浪系数的增长与材料的抗弯弹性模量值非线性关系,而是在某一弹性模量范围内,对消浪系数的影响较为敏感。     

煤渣改良土的抗冻能力及损伤特性研究

为研究煤渣改良土作为季节冻土区路基填料的抗冻能力,以不同煤渣掺量及不同养护龄期下的煤渣改良土为研究对象,利用GDS三轴试验系统开展了不同冻融次数下改良土的三轴压缩试验,获得了最佳的煤渣掺量及养护时间数据,提出冻融及加载综合影响下改良土的总损伤变量,并据此建立了损伤本构关系。结果表明：改良土的抗冻能力随着煤渣掺入量的增大出现先增强后减弱的变化趋势,随龄期的增长而逐渐增强。冻融循环对改良土物理力学性质的影响主要集中在前5次循环过程,超过5次后影响逐渐减弱。冻融加载总损伤变量能够较好地反映冻融及加载过程中改良土性质的劣化,据此建立的损伤本构关系具有一定的精度,可以为季节冻土区煤渣改良土路基工程提供参考。     

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

摄动理论在腐蚀管线随机地震反应分析中的应用

埋地管线的腐蚀情况十分复杂,具有随机性的特点,从而导致其在地震激励下的反应必然具有随机性。本文在管线腐蚀离散状态模型和弹性地基梁原理的基础上,利用随机摄动理论推导给出了腐蚀管线在地震激励下位移和应力的解析表达式,并进行了均值和标准差的计算。实例分析中将该方法计算结果与Monte Carlo模拟方法计算结果进行了对比。结果表明,利用随机摄动方法可以快速、精确地求得腐蚀管线的随机地震反应。     

折射层析成像法测试隧道围岩松动圈

通过应用折射层析成像法在乌鞘岭特长隧道中的测试,说明该方法对于横向速度变化不均一,过渡带速度渐变的地质情况具有分辨率高、图像直观、易于解释,适合对隧道围岩松动圈的测试.     

The symmetry energy and incompressibility constrained by the observations of glitching pulsars

We investigate the masses of glitching pulsars in order to constrain their equation of state(EOS). The observations of glitches(sudden jumps in rotational frequency) may provide information on the interior physics of neutron stars. With the assumption that glitches are triggered by superfluid neutrons, the masses of glitching neutron stars can be estimated using observations of maximum glitches.Together with the observations of thermal emission from glitching pulsars Vela and J1709–4429, the slope of symmetry energy and incompressibility of nuclear matter at saturation density can be constrained.The slope of symmetry energy L should be larger than 67 MeV while the lower limit of incompressibility for symmetric nuclear matter K_0 is 215 MeV. We also obtain a relationship between L and K_0:6.173 MeV + 0.283 K_0≤ L ≤ 7.729 MeV + 0.291 K_0. The restricted EOSs are consistent with the observations of 2-solar-mass neutron stars and gravitational waves from a binary neutron star inspiral.     

超参数对GRU-CNN混合深度学习弹性阻抗反演影响研究

CNN-GRU混合深度学习反演弹性阻抗取得了较好的反演效果.但是,基于深度学习的叠前反演参数众多,包括内部深度学习网络可学习参数和外部超参数等,目前超参数选取对网络性能及计算速度影响尚缺乏系统性研究,这直接影响到了该方法的进一步推广应用.因此,本文在混合深度学习反演弹性阻抗基础上,探讨学习率、Ep-och、batch_...     

各向异性介质中的弹性阻抗及其反演

地震反演已成为油藏描述中的重要组成部分.绝大多数的常规地震反演是叠后地震数据体上进行,很少考虑各向异性存在的情况.随着勘探开发的发展,地震各向异性和叠前地震波阻抗反演引起了人们极大关注.本文在各向同性介质中弹性阻抗研究基础上,推导出了各向异性介质中的弹性阻抗方程,提出了地震各向异性介质中用弹性阻抗进行储层参数描述的技术路线和框架,并对反演过程中存在的问题进行了有益探讨.     

弹性波阻抗在时移地震中的应用分析

常规声波波阻抗由于缺乏横波的信息,使得它对于流体的变化很不敏感,而弹性波阻抗反演,除了与纵波速度,密度有关之外,还与横波速度、入射角度有关,由于考虑了AVO(Amplitude Variation with Offset)效应,使得对流体和岩性的预测能力增强.本文将弹性波阻抗反演应用于时移地震中,采用AVA(Amplitude Variation with Angle)约束稀疏脉冲反演来进行弹性波阻抗反演,得到纵横波阻抗等弹性参数,通过交会图分析,将弹性参数转换为油藏参数,如预测含水饱和度的变化,通过本方法可增强对储层流体的预测,提高储层管理能力.