改进的贝叶斯迭代反演方法及其在白云岩致密储层识别的应用

由于低孔隙度和低渗透率的白云岩致密油储集层的纵波阻抗与其围岩差异非常小,利用叠后反演技术难以有效预测储层,而可以提取丰富弹性信息的叠前反演是解决此问题的有效手段,但是由于噪声等问题,叠前反演方程有较强的不适定性,笔者在贝叶斯框架下引入了改进的多变量柯西分布和改进的低频约束因子,重新推导了反演方程,获得了新的目标函数,有效地减少了反演的不适定性,从而提高了反演的稳定性,并结合迭代的思想来不断更新反演求解过程中的背景纵横波速度比值,从而增加了反演结果的精度。模型数据测试和实际资料应用都证明了该方法的稳定性和适用性。统计表明,利用提出的反演方法,目的层段内优质储层厚度预测吻合率高达89.75%。因此,此方法对类似硅质致密储层的勘探有重要的借鉴意义。     

A statistical approach to evaluating distance metrics and analog assignments for pollen records

The modern analog technique typically uses a distance metric to determine the dissimilarity between fossil and modern biological assemblages. Despite this quantitative approach, interpretation of distance metrics is usually qualitative and rules for selection of analogs tend to be ad hoc. We present a statistical tool, the receiver operating characteristic (ROC) curve, which provides a framework for identifying analogs from distance metrics. If modern assemblages are placed into groups (e.g., biomes), this method can (1) evaluate the ability of different distance metrics to distinguish among groups, (2) objectively identify thresholds of the distance metric for determining analogs, and (3) compute a likelihood ratio and a Bayesian probability that a modern group is an analog for an unknown (fossil) assemblage. Applied to a set of 1689 modern pollen assemblages from eastern North America classified into eight biomes, ROC analysis confirmed that the squared-chord distance (SCD) outperforms most other distance metrics. The optimal threshold increased when more dissimilar biomes were compared. The probability of an analog vs no-analog result (a likelihood ratio) increased sharply when SCD decreased below the optimal threshold, indicating a nonlinear relationship between SCD and the probability of analog. Probabilities of analog computed for a postglacial pollen record at Tannersville Bog (Pennsylvania, USA) identified transitions between biomes and periods of no analog.     

基于贝叶斯理论的逐次迭代非线性AVA反演方法

常规AVA三参数反演方法均基于横波速度与纵波速度之比γ为常数这一假设条件,且常被近似地取为0.5。然而在许多情况下γ并不为常数,而是在横向与纵向都渐变。若一概假定γ等于0.5,反演出的岩性参数势必要偏离真实值,因此有必要合理地选择γ。笔者基于贝叶斯理论,提出逐次迭代非线性AVA的反演方法。该方法把γ看成横向与纵向都渐变的反演初始背景,通过给定初始模型计算初始背景γ,并采用逐次迭代的策略求解该反演问题,解决了关于γ的选取问题以及由于引入变γ值而带来的非线性问题,提高了AVA三参数反演结果的精确度。     

VGI and crowdsourced data credibility analysis using spam email detection techniques

Volunteered geographic information (VGI) can be considered a subset of crowdsourced data (CSD) and its popularity has recently increased in a number of application areas. Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high. However, quality issues such as credibility, reliability and relevance are limiting many of the advantages of utilising CSD. Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens. VGI and CSD are also highly unstructured and the quality and metadata are often undocumented. In the 2011 Australian floods, the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards, evacuations, emergency services, road closures and property damage. This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems. The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90% using a forced classification methodology.     

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem

This paper deals with the problem of detecting and correcting cycle-slips in Global Navigation Satellite System (GNSS) phase data by exploiting the Bayesian theory. The method is here applied to undifferenced observations, because repairing cycle-slips already at this stage could be a useful pre-processing tool, especially for a network of permanent GNSS stations. If a dual frequency receiver is available, the cycle-slips can be easily detected by combining two phase observations or phase and range observations from a single satellite to a single receiver. These combinations, expressed in a distance unit form, are completely free from the geometry and depend only on the ionospheric effect, on the electronic biases and on the initial integer ambiguities; since these terms are expected to be smooth in time, at least in a short period, a cycle-slip in one or both the two carriers can be modelled as a discontinuity in a polynomial regression. The proposed method consists in applying the Bayesian theory to compute the marginal posterior distribution of the discontinuity epoch and to detect it as a maximum a posteriori (MAP) in a very accurate way. Concerning the cycle-slip correction, a couple of simultaneous integer slips in the two carriers is chosen by maximazing the conditional posterior distribution of the discontinuity amplitude given the detected epoch. Numerical experiments on simulated and real data show that the discontinuities with an amplitude 2 or 3 times larger than the noise standard deviation are successfully identified. This means that the Bayesian approach is able to detect and correct cycle-slips using undifferenced GNSS observations even if the slip occurs by one cycle. A comparison with the scientific software BERNESE 5.0 confirms the good performance of the proposed method, especially when data sampled at high frequency (e.g. every 1 s or every 5 s) are available.     

Bayesian probabilistic damage detection of a reinforced‐concrete bridge column

A Bayesian probabilistic approach for damage detection has been proposed for the continuous monitoring of civil structures (Sohn H, Law KH. Bayesian probabilistic approach for structure damage detection. Earthquake Engineering and Structural Dynamics 1997; 26 :1259–1281). This paper describes the application of the Bayesian approach to predict the location of plastic hinge deformation using the experimental data obtained from the vibration tests of a reinforced‐concrete bridge column. The column was statically pushed incrementally with lateral displacements until a plastic hinge is fully formed at the bottom portion of the column. Vibration tests were performed at different damage stages. The proposed damage detection method was able to locate the damaged region using a simplified analytical model and the modal parameters estimated from the vibration tests, although (1) only the first bending and first torsional modes were estimated from the experimental test data, (2) the locations where the accelerations were measured did not coincide with the degrees of freedom of the analytical model, and (3) there existed discrepancies between the undamaged test structure and the analytical model. The Bayesian framework was able to systematically update the damage probabilities when new test data became available. Better diagnosis was obtained by employing multiple data sets than just by using each test data set separately. Copyright © 2000 John Wiley & Sons, Ltd.     

Modeling of environmental influence in structural health assessment for reinforced concrete buildings

One branch of structural health monitoring (SHM) utilizes dynamic response measurements to assess the structural integrity of civil infrastructures. In particular,modal frequency is a widely adopted indicator for structural damage since its square is proportional to structural stiffness. However,it has been demonstrated in various SHM projects that this indicator is substantially affected by fluctuating environmental conditions. In order to provide reliable and consistent information on the health status of the monitored structures,it is necessary to develop a method to filter this interference. This study attempts to model and quantify the environmental influence on the modal frequencies of reinforced concrete buildings. Daily structural response measurements of a twenty-two story reinforced concrete building were collected and analyzed over a one-year period. The Bayesian spectral density approach was utilized to identify the modal frequencies of this building and it was clearly seen that the temperature and humidity fluctuation induced notable variations. A mathematical model was developed to quantify the environmental effects and model complexity was taken into consideration. Based on a Timoshenko beam model,the full model class was constructed and other reduced-order model class candidates were obtained. Then,the Bayesian modal class selection approach was employed to select the one with the most suitable complexity. The proposed model successfully characterizes the environmental influence on the modal frequencies. Furthermore,the estimated uncertainty of the model parameters allows for assessment of the reliability of the prediction. This study not only improves the understanding about the monitored structure,but also establishes a systematic approach for reliable health assessment of reinforced concrete buildings.     

基于BPSO-NBayes的雷暴释用预报技术研究

提出了一种新的雷暴预报法,即二进制粒子群-朴素贝叶斯分类器(Binary Particle Swarm Optimization-Naive Bayesian Classifiers,BPSO-NBayes)方法,以福州、连城、宁波3站为例,对使用T511数值预报产品站点的雷暴释用预报技术进行研究。利用2010—2014年T511数值预报产品和单站观测资料,使用BPSO-NBayes方法,建立了0~72 h雷暴预报模型,并与Fisher判别准则和Bayes判别准则进行比较。预报结果表明,BPSO-NBayes模型临界成功指数都在0.29以上,平均值达到0.33以上,是3种方法中最好的,空报率都在0.59以下,漏报率在0.60以下,而且变化幅度很小。BPSO-NBayes模型明显优于Fisher判别准则和Bayes判别准则,具有良好的稳定性和预报能力。     

基于支持向量机的雷暴潜势预报初探

根据2008—2010年夏季邵阳地区的NCEP全球再分析资料(分辨率为1°×1°)和闪电定位资料,利用支持向量机(SVM)分类方法建立该地区雷暴潜势预报模型,并用测试样本检验了该模型的预报能力,同时与Logistic回归模型和Bayes判别法的预报效果进行了比较。结果表明,SVM模型的预报准确率为86.21%,虚警率为15.25%,漏报率为13.79%。对比三种模型的TSS技术评分,发现使用SVM方法建立的模型对邵阳地区雷暴预报的效果最好,评分值为0.79。因此,SVM方法所建立的模型可以为邵阳地区6 h的雷暴潜势预报提供一定的参考价值。