Comparison of structural and least-squares lines for estimating geologic relations

Two different goals in fitting straight lines to data are to estimate a true linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating true straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal—predicting the dependent variable—OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples.     

Timing of the late-glacial climate reversal in the Southern Hemisphere using high-resolution radiocarbon chronology for Kaipo bog, New Zealand

The pattern of climate change in the Southern Hemisphere during the Younger Dryas (YD) chronozone provides essential constraint on mechanisms of abrupt climate change only if accurate, high-precision chronologies are obtained. A climate reversal reported previously at Kaipo bog, New Zealand, had been dated between 13,600 and 12,600 cal yr B.P. and appeared to asynchronously overlap the YD chron, but the chronology, based on conventionally radiocarbon-dated bulk sediment samples, left the precise timing questionable. We report a new high-resolution AMS ¹⁴C chronology for the Kaipo record that confirms the original chronology and provides further evidence for a mid-latitude Southern Ocean cooling event dated between 13,800 and 12,400 cal yr B.P. (2σ range), roughly equivalent to the Antarctic Cold Reversal.     

航天INSAR复影像对的自动快速匹配

针对航天INSAR主、辅影像数据的特点，提出了加大匹配窗口、加大锚点间隔的匹配策略，以提高匹配的可靠性和速度。在匹配过程中，综合利用轨道参数和强度影像金字塔进行粗匹配，并对强度影像采用相关系数与最小二乘相结合的双向匹配方案，实现了自动、快速、高可靠性和高精度的航天INSAR复影像匹配。最后总结了航天INSAR复影像匹配的特点。     

伴随模式同化系统在修正模式地形中的应用

任何模式都只是实际大气的一种近似模拟，存在误差。鉴于传统的四维资料伴随模式同化系统都是假设模式完全正确仅对初始场进行修正。将伴随模式同化系统应用于修正模式地形误差，通过对不同初始地形的修正试验表明，MM5伴随模式同化系统能很好地对地形误差进行修正，能够反演出与初始气象要素场分辨率相匹配、与模式更协调的地形场，为伴随模式同化系统的广泛应用提供了一种新的思路。     

航空影像分割的最小二乘支持向量机方法

将最小支持向量机LS-SVM用于航空影像的分割,讨论了不同核函数对分割结果的影响和稀疏化处理对决策函数的影响。试验表明了LS-SVM方法用于航空影像分割的可行性。     

Investigating the propagation mechanism of unmodelled systematic errors on coordinate time series estimated using least squares

The propagation of unmodelled systematic errors into coordinate time series computed using least squares is investigated, to improve the understanding of unexplained signals and apparent noise in geodetic (especially GPS) coordinate time series. Such coordinate time series are invariably based on a functional model linearised using only zero and first-order terms of a (Taylor) series expansion about the approximate coordinates of the unknown point. The effect of such truncation errors is investigated through the derivation of a generalised systematic error model for the simple case of range observations from a single known reference point to a point which is assumed to be at rest by the least squares model but is in fact in motion. The systematic error function for a one pseudo-satellite two-dimensional case, designed to be as simple but as analogous to GPS positioning as possible, is quantified. It is shown that the combination of a moving reference point and unmodelled periodic displacement at the unknown point of interest, due to ocean tide loading, for example, results in an output coordinate time series containing many periodic terms when only zero and first-order expansion terms are used in the linearisation of the functional model. The amplitude, phase and period of these terms is dependent on the input amplitude, the locations of the unknown point and reference point, and the period of the reference point's motion. The dominant output signals that arise due to truncation errors match those found in coordinate time series obtained from both simulated data and real three-dimensional GPS data.     

EFG法在土体固结中的应用

EFGM作为一种新的数值计算方法，具有只需节点信息而无段单元的特性，故在解固结方程方面有很大的优势。在计算分析中，此法容易构造固结方程的EFGM刚度矩阵和处理不同边界条件。对单面排水等条件的计算结果表明，EFGM在解决固结变形问题上，精度较高，处理边界准确。     

Method of Data Reduction and Uncertainty Estimation for Platinum-Group Element Data Using Inductively Coupled Plasma-Mass Spectrometry

A data reduction method is described for determining platinum-group element (PGE) abundances by inductively coupled plasma-mass spectrometry (ICP-MS) using external calibration or the method of standard addition. Gravimetric measurement of volumes, the analysis of reference materials and the use of procedural blanks were all used to minimise systematic errors. Internal standards were used to correct for instrument drift. A linear least squares regression model was used to calculate concentrations from drift-corrected counts per second (cps). Furthermore, mathematical manipulations also contribute to the uncertainty estimates of a procedure. Typical uncertainty estimate calculations for ICP-MS data manipulations involve: (1) Carrying standard deviations from the raw cps through the data reduction or (2) calculating a standard deviation from multiple final concentration calculations. It is demonstrated that method 2 may underestimate the uncertainty estimate of the calculated data. Methods 1 and 2 do not typically include an uncertainty estimate component from a regression model. As such models contribute to the uncertainty estimates affecting the calculated data, an uncertainty estimate component from the regression must be included in any final error calculations. Confidence intervals are used to account for uncertainty estimates from the regression model. These confidence intervals are simpler to calculate than uncertainty estimates from method 1, for example. The data reduction and uncertainty estimation method described here addresses problems of reporting PGE data from an article in the literature and addresses both precision and accuracy. The method can be applied to any analytical technique where drift corrections or regression models are used.