The polarimetric nature of HD 108

V -band polarimetric observations of HD 108 were obtained during 1994 August and September. This is the first time that the temporal polarimetric variability of this star has been investigated. Its percentage polarization and position angle vary randomly, and no evidence of its proposed binary nature is detected. The random variability is consistent with that seen for previously observed Wolf–Rayet stars and OB supergiants. Nightly variations do show some systematic behaviour consistent with the blob ejection model of Underhill & Fehey (1984). From the data it is estimated that the mass-loss rate resulting from blobs is ∼1-10⁻⁷ M⊙ yr⁻¹. The total mass-loss rate of the star is estimated to be ∼1-10⁻⁵ M⊙ yr⁻¹. The stellar rotation rate is estimated to be V _rot∼400 km s⁻¹ with an inclination of i <20c. The data are also interpreted in terms of perturbations occurring in an equatorial disc plus bipolar jets viewed equatorially (Underhill 1994). It is found that the perturbation mass-loss rate is about ∼3-10⁻⁷ M⊙ yr⁻¹, giving a total mass-loss rate for the star of 13-10⁻⁵ M⊙ yr⁻¹. Both models are consistent with HD 108 being an OB supergiant or of the class Ofpe/WR9.     

Calculation of the Inverse of the Covariance

In reservoir characterization, the covariance is often used to describe the spatial correlation and variation in rock properties or the uncertainty in rock properties. The inverse of the covariance, on the other hand, is seldom discussed in geostatistics. In this paper, I show that the inverse is required for simulation and estimation of Gaussian random fields, and that it can be identified with the differential operator in regularized inverse theory. Unfortunately, because the covariance matrix for parameters in reservoir models can be extremely large, calculation of the inverse can be a problem. In this paper, I discuss four methods of calculating the inverse of the covariance, two of which are analytical, and two of which are purely numerical. By taking advantage of the assumed stationarity of the covariance, none of the methods require inversion of the full covariance matrix.     

Growth rates of manganese nodules in Oneida Lake,New York

²²⁶Ra is used to document the growth histories of six manganese nodules from Oneida Lake, New York. Detailed sectioning and analysis reveal that there are discontinuous gradients in²²⁶Ra content in these samples. These gradients result from periods of rapid growth (>1 mm/100 years) separated by periods of no growth of erosion. Although the²²⁶Ra “age” of the nodules approximates the age of Oneida Lake, the nodules are not sediment-covered because they occur only in areas of the lake where fine-grained sediments are not accumulating.     

A method for studying iron silicate liquids under reducing conditions with negligible iron loss

In studies of iron silicate liquids under reducing conditions at 1 atm pressure, iron losses from the melt can be minimized by suspending the liquid as a drop from a short segment of fine Pt wire. For a basaltic composition at 1275°C and oxygen fugacities appropriate to terrestrial magmas, iron losses from the melt are less than 0.5 wt.% of the amount present for run times less than about 20 hr.     

Triaxial induction tool with electrode sleeve: FD modeling in 3D geometries

We have developed a finite-difference (FD) method to model the response of a multiple-spacing triaxial array induction tool with a multiple-electrode type sleeve. The FD software was verified versus two other independent modeling methods. The model response was checked for the FD grid refinement, while attempting to minimize run time. Many details of the electrode-sleeve geometry were studied using this method that led to the final electrode-sleeve design. The electrode sleeve reduces the response of the transverse couplings to eccentering in a conductive borehole. Quantitative agreement was found between the measurements and calculations. Using the FD software we study the tool response to various effects of 3D geometry.     

建筑物立面分类与DIM点云滤波

针对目前基于近景摄影测量方法构建建筑物立面模型过程中因密集影像匹配（DIM）点云噪声所引起的建筑物立面TIN网格模型畸变问题,本文借鉴机器学习中样本学习的思想,对建筑物立面进行了分类并对DIM点云提出了相应的滤波方法,以达到去除DIM点云噪声和改善其TIN网格模型畸变的目的。其中,针对平面结构立面,采取先对点云样本进行学习计算构建数学立面模型所需参数,再对该立面模型设定阈值并对其点云进行滤波处理的方法;针对曲面结构立面,则结合DIM点云特性先将点云样本分类标记归为立面点与非立面点,再进行样本特征值学习,使用Logistic回归算法迭代计算求解最佳回归系数,从而构建滤波分类器的方法对立面点云进行滤波处理。试验结果表明,本文滤波处理方法能将立面DIM点云噪声有效识别并去除,而且使用该方法处理后所得点云构建的建筑物立面TIN网格模型精细化程度得到有效提高,模型质量得到明显改善。     

Controls on biochemical oxygen demand in the upper Klamath River,Oregon

A series of 30-day biochemical oxygen demand (BOD) experiments were conducted on water column samples from a reach of the upper Klamath River that experiences hypoxia and anoxia in summer. Samples were incubated with added nitrification inhibitor to measure carbonaceous BOD (CBOD), untreated to measure total BOD, which included demand from nitrogenous BOD (NBOD), and coarse-filtered to examine the effect of removing large particulate matter. All BOD data were fit well with a two-group model, so named because it considered contributions from both labile and refractory pools of carbon: BOD_t = a₁(1 ? e^? a₀t) + a₂t. Site-average labile first-order decay rates a₀ ranged from 0.15 to 0.22/day for CBOD and 0.11 to 0.29/day for BOD. Site-average values of refractory zero-order decay rates a₂ ranged from 0.13 to 0.25 mg/L/day for CBOD and 0.01 to 0.45 mg/L/day for BOD; the zero-order CBOD decay rate increased from early- to mid-summer. Values of ultimate CBOD for the labile component a₁ ranged from 5.5 to 28.8 mg/L for CBOD, and 7.6 to 30.8 mg/L for BOD. Two upstream sites had higher CBOD compared to those downstream. Maximum measured total BOD₅ and BOD₃₀ during the study were 26.5 and 55.4 mg/L; minimums were 4.2 and 13.6 mg/L. For most samples, the oxygen demand from the three components considered here were: labile CBOD > NBOD > refractory CBOD, though the relative importance of refractory CBOD to oxygen demand increased over time. Coarse-filtering reduced CBOD for samples with high particulate carbon and high biovolumes of Aphanizomenon flos-aquae. There was a strong positive correlation between BOD, CBOD, and the labile component of CBOD to particulate C and N, with weaker positive correlation to field pH, field dissolved oxygen, and total N. The refractory component of CBOD was not correlated to particulate matter, instead showing weak but statistically significant correlation to dissolved organic carbon, UV absorbance at 254 nm, and total N. Particulate organic matter, especially the alga A.flos-aquae, is an important component of oxygen demand in this reach of the Klamath River, though refractory dissolved organic matter would continue to exert an oxygen demand over longer time periods and as water travels downstream.     

Ensemble filter methods with perturbed observations applied to nonlinear problems

The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g., water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble methods when applied to nonlinear problems.