An evaluation of the health status of the Lavaca Bay, Texas ecosystem using Crassostrea virginica as the sentinel species

Locational risks for compromised ecosystem health for the eastern oysters (Crassostrea virginica) harvested from Lavaca Bay, Texas were estimated. Flow cytometric evaluation of variations in DNA content and the lysosomal destabilization assay were used for evaluation of genotoxicity and stress, respectively. Bayesian geo-statistical methods were utilized to estimate and evaluate spatial effects. For models with spatial risks, continuous surface maps of predicted parameter values were created to evaluate risk location. Lysosomal destabilization assay results were spatially oriented whereas flow cytometry results were fit best with the random effects model. While not spatially oriented, the highest levels of variations in DNA content were also present near industrial facilities. Locational risks of increased biomarkers of genotoxicity and stress in the eastern oyster (Crassostrea virginica) were increased with proximity to industrial facilities     

Secondary phase validation—Phase classification by polarization

A long-standing problem in operational seismology is that of reliable focal depth estimation. Standard analyst practice is to pick and identify a ‘phase’ in the P-coda. This picking will always produce a depth estimate but without any validation it cannot be trusted. In this article we ‘hunt’ for standard depth phases like pP, sP and/or PmP but unlike the analyst we use Bayes statistics for classifying the probability that polarization characteristics of pickings belong to one of the mentioned depth phases given preliminary epicenter information. In this regard we describe a general-purpose PC implementation of the Bayesian methodology that can deal with complex nonlinear models in a flexible way. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical-programming environment. An analytic signal representation is used with the imaginary part being the Hilbert transform of the signal itself. The pickings are in terms of a plot of posterior probabilities as a function of time for pP, Sp or PmP being within the presumed azimuth and incident angle sectors for given preliminary epicenter locations. We have tested this novel focal depth estimation procedure on explosion and earthquake recordings from Cossack Ranger II stations in Karelia, NW Russia, and with encouraging results. For example, pickings deviating more than 5° off ‘true’ azimuth are rejected while Pn-incident angle estimate exhibit considerable scatter. A comprehensive test of our approach is not quite easy as recordings from so-called Ground Truth events are elusive.     

Bayesian power-law regression with a location parameter,with applications for construction of discharge rating curves

This paper presents a Bayesian approach for fitting the standard power-law rating curve model to a set of stage-discharge measurements. Methods for eliciting both regional and at-site prior information, and issues concerning the determination of prior forms, are discussed. An efficient MCMC algorithm for the specific problem is derived. The appropriateness of the proposed method is demonstrated by applying the model to both simulated and real-life data. However, some problems came to light in the applications, and these are discussed.     

Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region.     

Bayesian methods for estimating multi-segment discharge rating curves

This study explores Bayesian methods for handling compound stage–discharge relationships, a problem which arises in many natural rivers. It is assumed: (1) the stage–discharge relationship in each rating curve segment is a power-law with a location parameter, or zero-plane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties in the interpolated area, i.e. within the range of available stage–discharge measurements. A second model is constructed in an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order to obtain a more efficient simulation scheme.     

弹性地基条件下狭窄煤柱岩爆的突变理论分析

将未采煤层视为弹性地基条件下,运用maple9.5符号运算软件解得岩梁的挠曲线方程。根据能量守恒原理得到的功、能增量平衡关系式,求得岩梁–煤柱系统作准静态形变时的平衡方程--折迭突变模型平衡方程;用折迭突变总势能函数所作的稳定性判别表明,岩爆前兆阶段岩梁–煤柱系统处于不稳定平衡状态;煤柱岩爆的条件是Cook刚度准则,该准则可以动态地用煤柱形变增大时所需外界的能量输入率趋于0来表示;计算了煤柱岩爆地震能释放量,阐明煤柱岩爆机制是由于岩梁弹性能释放量超过峰后软化煤柱形变所耗的能量。通过岩梁弹性势能变化率曲线和煤柱耗能曲线,以几何形式描述了所得研究结果。计算表明,未开采煤层刚度变化对岩梁的等效刚度影响微弱。工作面开挖和采空区增大,将显著减小岩梁刚度Ke,对煤柱岩爆有重要影响。     

新疆阿克苏东,西部雹云和非雹云雷达临近指标的统计特征值

根据阿克苏东部沙雅７１１雷达１９９４、１９９５年雷达资料以及西部农一师八团ＸＤＲ－Ｘ型７１１数字化雷达１９９６、１９９７年回波资料,利用多因子雹云判别方程,给出了东西部地区冰雹云及非冰雹云临近指标的统计特征值。     

Statistics for Twenty-first Century Astrometry

H. K. Eichhorn had a lively interest in statistics during his entire scientific career, and made a number of significant contributions to the statistical treatment of astrometric problems. In the past decade, a strong movement has taken place for the reintroduction of Bayesian methods of statistics into astronomy, driven by new understandings of the power of these methods as well as by the adoption of computationally-intensive simulation methods to the practical solution of Bayesian problems. In this paper I will discuss how Bayesian methods may be applied to the statistical discussion of astrometric data, with special reference to several problems that were of interest to Eichhorn.     

贝叶斯统计方法在海浪方向谱研究中的应用

较详细介绍了贝叶斯统计方法在海浪方向谱估计中的应用,指出先验分布的不同对估计结果没有影响,由于均匀分布与最大熵原则相对应,使其具有某种特殊性.数值模拟表明贝叶斯方法对双峰方向分布估计的有效性,将其应用于黑海实测资料,证明双峰方向分布的出现依赖于估计方法分辨力的大小,而用最大似然法得到的双峰方向分布值得怀疑.最后给出出现双峰方向分布的一种可能物理解释.