基于静主元消元法的频率域波动方程正演(英文)

频率域波动方程正演是求解一个大型线性稀疏方程组问题,其受到计算效率和内存存储问题的限制。常规的高斯消元法不能满足大型数据的并行计算,本文提出基于静主元消元法(GESP)进行稀疏矩阵LU分解和多炮有限差分正演,该方法不仅提高了稳定性,更有利于单频点内LU分解的分布式并行计算。通过Marmousi模型模拟试验,单频波场和转化到时间域地震剖面的试验表明模拟精度和计算效率得到提高,节约并充分利用内存,为波形反演奠定基础。     

塔河油气田AT1区块凝析气藏三维地质建模研究

以随机函数理论为基础,采用相控-多参数协同的随机建模方法,建立塔河油气田AT1区块凝析气藏三维地质模型,实现气藏精细三维表征。首先,以钻井和岩芯资料为基础构建储层构造模型;然后,以小层界面为控制条件建立储层结构模型;接着,在沉积相、地质条件的约束下,采用序贯指示模拟法来建立砂体骨架模型;随后,在砂体骨架模型内进行优势相计算,形成最终有效砂体骨架模型;最后,以有效砂体骨架模型为约束,采用序贯高斯模拟法建立储层物性参数模型。结果表明:将物性参数变量与微相分布结合的序贯高斯模拟法建立孔隙度等物性参数的分布模型,以及采用地质分析类比、地质统计分析等方法优选最佳模型是有效的地质建模方法;所建地质模型精确细致地表征了塔河油气田AT1区块凝析气藏构造格架及储层、流体三维分布,反映了辫状水道复合连片,东北向展布,储层物性受相控较明显,非均质性较强。     

基于瞬时相位的BPSK信噪比估计算法

通过对加性高斯白噪声条件下正弦型信号瞬时相位分布情况进行研究,提出了一种新的BPSK（Binary Phase Shift Keying）信噪比估计方法．含噪信号瞬时相位的方差与信噪比之间存在近似线性的一一对应关系,以此为基础通过将含噪信号的估计方差与理论值相比较获得信噪比估计值．该方法直接对基带过采样信号进行处理,不需要事先进行符号定时与同步．使用计算机仿真测试了算法的估计性能,并与高阶累积量方法和子空间分解方法进行了比较,结果表明该算法具有更高的估计精度．     

Approximate up-scaling of geo-spatial variables applied to deep foundation design

We present a series of simple approximate methods for up-scaling the cumulative distribution function of spatially correlated variables by using an effective number n _e of independent variables. Methods are based on the property of distribution permanence of the gamma and inverse Gaussian distributions under averaging, bootstrap sampling and expansions about the normal and gamma distributions. A stochastic simulation study is used to validate each method, and simple parameters are defined to identify respective ranges of applicability. A practical example is presented where core sample rock strength data are up-scaled to shaft size for probabilistic (risk-based) deep foundation design. Supplemental material is available online.     

Equal-Area Projections of the Triaxial Ellipsoid: First Time Derivation and Implementation of Cylindrical and Azimuthal Projections for Small Solar System Bodies

Many small solar system bodies such as asteroids or small satellites have irregular shapes, often approximated by the reference surface of a triaxial ellipsoid. Map projections for the triaxial ellipsoid are needed to present the incoming data in the form of maps. In this paper the formulae of equal-area cylindrical and azimuthal projections of the triaxial ellipsoid were derived and practically implemented for the first time using as an example the asteroid 253 Mathilde. This paper is the final in a series of papers devoted to all main classes of projections of the triaxial ellipsoid. Before this, the authors obtained equidistant along meridians projection and Jacobi conformal projection for the triaxial ellipsoid.     

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR,hyperspectral imagery,and large inventory datasets

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.     

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r²: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r²: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.     

A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery

A fast endmember-extraction algorithm based on Gaussian Elimination Method (GEM) is proposed in this paper under the fact that a pixel is an endmember if it has the maximum value in any spectral band of a hyperspectral image when based on linear mixing model. Applying Gaussian elimination is much like performing a lower triangular matrix to transform the hyperspectral image. As more endmembers have been extracted, fewer bands are needed to be involved in the Gaussian elimination process, thus greatly reducing the computing time. The experimental results with both simulated and real hyperspectral images indicate that the method proposed here is much faster than the vertex component analysis (VCA) method, and can provide a similar performance with VCA.     

The Hurst phenomenon and fractional Gaussian noise made easy

Abstract

The Hurst phenomenon, which characterizes hydrological and other geophysical time series, is formulated and studied in an easy manner in terms of the variance and autocorrelation of a stochastic process on multiple temporal scales. In addition, a simple explanation of the Hurst phenomenon based on the fluctuation of a hydrological process upon different temporal scales is presented. The stochastic process that was devised to represent the Hurst phenomenon, i.e. the fractional Gaussian noise, is also studied on the same grounds. Based on its studied properties, three simple and fast methods to generate fractional Gaussian noise, or good approximations of it, are proposed.     

Mixture model based multivariate statistical analysis of multiply censored environmental data

Environmental data are commonly constrained by a detection limit (DL) because of the restriction of experimental apparatus. In particular due to the changes of experimental units or assay methods, the observed data are often cut off by more than one DL. Measurements below the DLs are typically replaced by an arbitrary value such as zeros, half of DLs, or DLs for convenience of analysis. However, this method is widely considered unreliable and prone to bias. In contrast, maximum likelihood estimation (MLE) method for censored data has been developed for better performance and statistical justification. However, the existing MLE methods seldom address the multivariate context of censored environmental data especially for water quality. This paper proposes using a mixture model to flexibly approximate the underlying distribution of the observed data due to its good approximation capability and generation mechanism. In particular, Gaussian mixture model (GMM) is mainly focused in this study. To cope with the censored data with multiple DLs, an expectation–maximization (EM) algorithm in a multivariate setting is developed. The proposed statistical analysis approach is verified from both the simulated data and real water quality data.