Application of the BME approach to soil texture mapping

In order to derive accurate space/time maps of soil properties, soil scientists need tools that combine the usually scarce hard data sets with the more easily accessible soft data sets. In the field of modern geostatistics, the Bayesian maximum entropy (BME) approach provides new and powerful means for incorporating various forms of physical knowledge (including hard and soft data, soil classification charts, land cover data from satellite pictures, and digital elevation models) into the space/time mapping process. BME produces the complete probability distribution at each estimation point, thus allowing the calculation of elaborate statistics (even when the distribution is not Gaussian). It also offers a more rigorous and systematic method than kriging for integrating uncertain information into space/time mapping. In this work, BME is used to estimate the three textural fractions involved in a texture map. The first case study focuses on the estimation of the clay fraction, whereas the second one considers the three textural fractions (sand, silt and clay) simultaneously. The BME maps obtained are informative (important soil characteristics are identified, natural variations are well reproduced, etc.). Furthermore, in both case studies, the estimates obtained by BME were more accurate than the simple kriging (SK) estimates, thus offering a better picture of soil reality. In the multivariate case, classification error rate analysis in terms of BME performs considerably better than in terms of kriging. Analysis in terms of BME can offer valuable information to be used in sampling design, in optimizing the hard to soft data ratio, etc.     

Bayesian spectral density approach for modal updating using ambient data

The problem of identification of the modal parameters of a structural model using measured ambient response time histories is addressed. A Bayesian spectral density approach (BSDA) for modal updating is presented which uses the statistical properties of a spectral density estimator to obtain not only the optimal values of the updated modal parameters but also their associated uncertainties by calculating the posterior joint probability distribution of these parameters. Calculation of the uncertainties of the identified modal parameters is very important if one plans to proceed with the updating of a theoretical finite element model based on modal estimates. It is found that the updated PDF of the modal parameters can be well approximated by a Gaussian distribution centred at the optimal parameters at which the posterior PDF is maximized. Examples using simulated data are presented to illustrate the proposed method. Copyright © 2001 John Wiley & Sons, Ltd.     

Probabilistic approach for modal identification using non‐stationary noisy response measurements only

This paper addresses the problem of identification of the modal parameters for a structural system using measured non‐stationary response time histories only. A Bayesian time‐domain approach is presented which is based on an approximation of the probability distribution of the response to a non‐stationary stochastic excitation. It allows one to obtain not only the most probable values of the updated modal parameters and stochastic excitation parameters but also their associated uncertainties using only one set of response data. It is found that the updated probability distribution can be well approximated by a Gaussian distribution centred at the most probable values of the parameters. Examples using simulated data are presented to illustrate the proposed method. Copyright © 2002 John Wiley & Sons, Ltd.     

Study on the method for comprehensive discrimination of small earthquakes

IntroductionThe present-day digital seismic observation technique provides us a large amount of accurate seismic records of high-precision, wide-band, and large dynamic range and conditions for higher-solution and higher-speed discrimination of earthquakes. But the problem we face is how to use the modern theory and method for digital signal processing in fast and precise extraction of the characteristic parameters from the large amount of digital seismic records. A basis or the important prob…     

多级模糊综合评判在油气资源评价中的应用

基于模糊综合评判理论，建立了一级评判、多级评判的模型，讨论了模糊评判的反问题及其应用，用VC^ 5.0语言在Windows95/98平台上开发了相应的程序，给出了一级评判和多级评判模型在储层评价方面的应用实例，结果表明，多级模糊综合评判不但比传统的判别分析更符合客观实际，同时，通过反问题的求解，可重构相应的因素集和评价集，有助于成因机理预测。     

Statistical Analysis of the Completeness of a Seismic Catalogue

Among the numerous issues that the study of seismic events presents, theincompleteness of catalogues is certainly one of the most important. It is also one that only the contribution of many and different skills canprovide with a valid solution. In this paper the search for the complete part of a catalogue is expressed in terms of identification of the changepoint in a hierarchical Bayesian model. Stochastic simulation methods, recently presented in the literature, have enabled us to overcome the computational issues that previously made this approach prohibitive. We have applied the method on data, drawn from the Italian NT4.1.1 catalogue,related to some seismogenetic zones of ZS.4 zonation within which we assumespatial incompleteness to be homogeneous. The results obtained are given inthe concluding sections of the paper.     

油气化探综合异常评价方法及效果

油气化探综合异常评价是油气化探测量中最为关键的一环,它关系着一个地区的含油气性评价和远景预测。由于多种原因,过去人们对诸多的化探综合异常仅限于定性评价,而缺乏量的概含。为此,作者应用综合评价指数和模糊数学等多种方法,通过对甲、乙、丙三个典型试验区的研究,证明了该方法的可行性和有效性,取得了良好的地质效果。     

Evaluation of risk concepts in partial duration series

Conventional design practice aims at obtaining optimal estimates of floods with specified exceedance probabilities. Such estimates are, however, known on the average to be exceeded more frequently than expected. Alternatively, methods focusing on the expected exceedance probability can be used. Two different methods are considered here; the first is based on the sample distribution of true exceedance probabilities. The second is a Bayesian analogue using the likelihood function and a noninformative prior to describe the variability of exceedance probabilities. Appropriate analytical solutions are presented in both cases using the partial duration series approach.