抗差估计的初值选择

对L1 范估计的平差值和LS估计的平差值作为抗差估计的初值进行了比较 ,指出在 ρ函数是严凸函数时 ,二者无明显的区别。因为LS估计简单且易于计算 ,因此建议用LS估计的平差值作为抗差估计的初值。     

STUDY ON GIS FOR YIELD ESTIMATION BY REMOTE SENSING IN JILIN MAIZE BELT

ＳＴＵＤＹ　ＯＮ　ＧＩＳ　ＦＯＲ　ＹＩＥＬＤ　ＥＳＴＩＭＡＴＩＯＮ　ＢＹ　ＲＥＭＯＴＥ　ＳＥＮＳＩＮＧ　ＩＮ　ＪＩＬＩＮ　ＭＡＩＺＥ　ＢＥＬＴＳＴＵＤＹＯＮＧＩＳＦＯＲＹＩＥＬＤＥＳＴＩＭＡＴＩＯＮＢＹＲＥＭＯＴＥＳＥＮＳＩＮＧＩＮＪＩＬＩＮＭＡＩＺ...     

REMOTE SENSING FOREST BIOMASS: AN EVALUATION USING HIGH RESOLUTION REMOTE SENSOR DATA AND LOBLOLLY PINE PLOTS

Eight-year-old Loblolly pine (Pinus taeda) plots located in South Carolina were evaluated using high resolution aircraft multispectral scanner data to determine if biomass could be accurately measured. Sixteen pine plots located on both sandy and clay soils were treated with 0, 180, or 360 kg. (approximately 0, 400, or 800 lb.) of nitrogen per plot. Indices of biomass from remote sensing data were significantly correlated with in situ biomass measurements made in each plot. A ratio of infrared (.9–1.1 μm) and red (.65–.70 μm) channels yielded the best correlation. The indices were not sensitive to differences in soil type (sandy or clay).     

On least squares estimation of generalized covariance functions

Least squares estimation (LSE) is theoretically related to quadratic unbiased estimation of variance components. It is argued that these methods of estimation of variance components essentially generalize LSE though they are not formally equivalent.     

变形分析的Bayes估计与检验

由于观测条件的变化，观测值方差将产生随机波动。本文根据Ｂａｙｅｓ估计理论，推导了线性模型待估参数的分布函数及观测值方差估计公式，并将其用于位移检验。计算结果表明，在同样的置信度下，本方法更符合实际。文中还对参数估值的统计性质进行了讨论。     

利用高光谱数据进行植被生化成分反演方法研究

高光谱数据包含着丰富光谱信息,能够定量地分析物质成分^[1]。由高光谱数据,可以运用多元逐步线性回归方法反演植被生化组分含量,从而达到监测植被生长状况的目的^[2]。本文具体介绍了多元逐步线性回归方法,及其在氮、木质素和纤维素含量估算中的应用。     

Sediment transport in analogue flume models compared with real‐world sedimentary systems: a new look at scaling evolution of sedimentary systems in a flume

Evolution of sedimentary systems at large temporal and spatial scales cannot be scaled down to laboratory dimensions by conventional hydraulic Froude scaling. Therefore, many researchers question the validity of experiments aiming to simulate this evolution. Yet, it has been shown that laboratory experiments yield stratigraphic responses to allocyclic forcing that are remarkably similar to those in real‐world prototypes, hinting at scale independency with strong dependence on boundary conditions but weak dependence on the actual sediment transport dynamics. This paper addresses the dilemma by contrasting sediment transport rules that apply in the laboratory with those that apply in real‐world geological systems. It is demonstrated that the generation of two‐dimensional stratigraphy in a flume can be simulated numerically by the non‐linear diffusion equation. Sediment transport theory is used to demonstrate that only suspension‐dominated meandering rivers should be simulated with linear diffusion. With increasing grain‐size (coarse sand to gravel) and shallowness of river systems, the prediction of long‐term transport must be simulated by non‐linear, slope‐dependent diffusion to allow for increasing transport rates and thus change in stratigraphic style. To point out these differences in stratigraphic style, three stages in infill of accommodation have been defined here: (i) a start‐up stage, when the system is prograding to base level (e.g. the shelf edge) with no sediment flux beyond the base‐level point; (ii) a fill‐up stage, when the system is further aggrading while progressively more sediment is bypassing base level with the progression of the infill; and (iii) a keep‐up stage, when more than 90% of the input is bypassing the base level and less than 10% is used for filling the accommodation. By plotting the rate of change in flux for various degrees of non‐linearity (varying the exponent in the diffusion equation) it was found that the error between model and real‐world prototype is largest for the suspension‐dominated prototypes, although never more than 30% and only at the beginning of the fill‐up stage. The error reduces to only 10% for the non‐linear sandy‐gravelly and gravelly systems. These results are very encouraging and open up ways to calibrate numerical models of sedimentary system evolution by such experiments.     

Combined mine tremors source location and error evaluation in the Lubin Copper Mine (Poland)

A modified method of mine tremors location used in Lubin Copper Mine is presented in the paper. In mines where an intensive exploration is carried out a high accuracy source location technique is usually required. The effect of the flatness of the geophones array, complex geological structure of the rock mass and intense exploitation make the location results ambiguous in such mines. In the present paper an effective method of source location and location's error evaluations are presented, combining data from two different arrays of geophones. The first consists of uniaxial geophones spaced in the whole mine area. The second is installed in one of the mining panels and consists of triaxial geophones. The usage of the data obtained from triaxial geophones allows to increase the hypocenter vertical coordinate precision. The presented two-step location procedure combines standard location methods: P-waves directions and P-waves arrival times. Using computer simulations the efficiency of the created algorithm was tested. The designed algorithm is fully non-linear and was tested on the multilayered rock mass model of the Lubin Copper Mine, showing a computational better efficiency than the traditional P-wave arrival times location algorithm. In this paper we present the complete procedure that effectively solves the non-linear location problems, i.e. the mine tremor location and measurement of the error propagation.     

基于群桩基础沉降估算确定压缩层厚度的探讨

在现行规范中,由于桩基沉降估算模式的不同,压缩层厚度的确定方法也不完全相同。对这些方法进行归纳、总结,并通过具体的工程实例,对由不同估算模式确定的压缩层厚度及沉降估算结果进行探讨与分析,认为对于工程场地地质资料掌握较为翔实的情况下,压缩层厚度可采用变形比法确定,否则,宜根据应力比法确定。     

Prediction of local scour around circular piles under waves using a novel artificial intelligence approach

Abstract

The scour phenomena around vertical piles in oceans and under waves may influence the structure stability. Therefore, accurately predicting the scour depth is an important task in the design of piles. Empirical approaches often do not provide the required accuracy compared with data mining methods for modeling such complex processes. The main objective of this study is to develop three data-driven methods, locally weighted linear regression (LWLR), support vector machine (SVR), and multivariate linear regression (MLR) to predict the scour depth around vertical piles due to waves in a sand bed. It is the first effort to develop the LWLR to predict scour depth around vertical piles. The models simulate the scour depth mainly based on Shields parameter, pile Reynolds number, grain Reynolds number, Keulegan–Carpenter number, and sediment number. 111 laboratory datasets, derived from several experimental studies, were used for the modeling. The results indicated that the LWLR provided highly accurate predictions of the scour depths around piles (R?=?0.939 and RMSE = 0.075). Overall, this study demonstrated that the LWLR can be used as a valuable tool to predict the wave-induced scour around piles.