Medical Geography: A Promising Field of Application for Geostatistics

The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. However, it presents several methodological challenges that arise from the fact that data is typically aggregated over irregular spatial supports and consists of a numerator and a denominator (e.g., population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: (1) estimation of the underlying disease risk, (2) detection of areas with significantly higher risk, and (3) analysis of relationships with putative risk factors. The analysis is illustrated by using age-adjusted cervix cancer mortality rates recorded from 1970 to 1994 of 118 counties in four Western USA states. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify how uncertainty of the spatial distribution of health outcomes translates into uncertainty of the location of clusters of high values or the correlation with covariates. Finally, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates; the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.     

Solution of Multiple——Point Statistics to Extracting Information from Remotely Sensed Imagery

Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information．Therefore,there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery．In the current article,a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification．The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese YeHow River delta on August 8,1999． The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier (MLC)     

Application of Fractal Models to Distinguish between Different Mineral Phases

Fractal modeling is demonstrated to be an effective and rapid tool to distinguish between mineral phases in rock samples. It supplements work that previously could be performed only by observing the interpenetrational or metasomatic phenomena between different minerals with the aid of mineralographic microscope. The Gejiu tin district in southwestern China was chosen as a study area for the recognition and characterization of the spatial distribution of two phases (Types I and II) of cassiterite. Vector patterns used for this study were extracted from digital photomicrographs and analyzed with the aid of MapGIS. Perimeter–area fractal dimension, cumulative number–area exponent, and shape index were determined in order to quantify geometrical irregularities and spatial cassiterite phase distribution characteristics. The results show that fractal dimensions based on area and perimeter are larger for crystals of Type I than for those of Type II. The mean shape index (SI) increases from 0.54 (Type I) to 0.64 (Type II), indicating an increase in regularity. The number–area exponent also increases from 0.88 to 1.15, indicating the smaller crystals of Type II. The cumulative number–shape index log–log plot shows two separate straight-line segments. One of these probably represents a background shape realized during the original process of natural crystallization, whereas the other likely represents anomalous shapes because of weathering or other superimposed processes. Two parallel lines can be constructed on the perimeter–area log–log plots. The upper line, with a larger intercept, represents crystals with lower SI. The lower line represents crystals with higher SI, indicating that the intercept provides a measure of the irregularity of grains. By combining the perimeter–area model with cumulative number–area plot and shape index, the two phases of cassiterite can be distinguished and characterized. One phase has fewer crystals of large size, and the other has smaller crystals. This difference can be explained by assuming that under higher-temperature conditions, the large cassiterite crystals formed earlier than the smaller crystals. Consequently, the large cassiterites underwent longer, high-intensive weathering than the small crystals so that their shapes became more irregular. The younger, more abundant small cassiterites retained their original regular shapes.     

Two-dimensional Conditional Simulations Based on the Wavelet Decomposition of Training Images

Scale dependency is a critical topic when modeling spatial phenomena of complex geological patterns that interact at different spatial scales. A two-dimensional conditional simulation based on wavelet decomposition is proposed for simulating geological patterns at different scales. The method utilizes the wavelet transform of a training image to decompose it into wavelet coefficients at different scales, and then quantifies their spatial dependence. Joint simulation of the wavelet coefficients is used together with available hard and or soft conditioning data. The conditionally co-simulated wavelet coefficients are back-transformed generating a realization of the attribute under study. Realizations generated using the proposed method reproduce the conditioning data, the wavelet coefficients and their spatial dependence. Two examples using geological images as training images elucidate the different aspects of the method, including hard and soft conditioning, the ability to reproduce some non-linear features and scale dependencies of the training images.     

Determination and Application of the Sharp Degradation Point of Concrete under Environmental Actions

INTRODUCTIONMany experi mental results have indicated thatthe deterioration process of concrete can be dividedinto two stages—the nondestructive stage (stage 1)and the destructive stage (stage 2) ,as shownin Fig.1 ( Wu, 2002 ; Rostam, 1993) . When concrete issubjected to environmental actions ,the inner energyit gains is al most balanced by the released energywhen crackingin stage 1 , while in stage 2 the innerenergy is greater than that released. There are fewremarkable degradation phe…     

Scattered Data Approximation on the Bisphere and Application to Texture Analysis

The paper deals with the approximation and optimal interpolation of functions defined on the bisphere \mathbb S²×\mathbb S²\mathbb {S}^{2}\times \mathbb {S}^{2} from scattered data. We demonstrate how the least square approximation to the function can be computed in a stable and efficient manner. The analysis of this problem is based on Marcinkiewicz–Zygmund inequalities for scattered data which we present here for the bisphere. The complementary problem of optimal interpolation is also solved by using well-localized kernels for our setting. Finally, we discuss the application of the developed methods to problems of texture analysis in material science.     

Reconstruction of Incomplete Data Sets or Images Using Direct Sampling

With increasingly sophisticated acquisition methods, the amount of data available for mapping physical parameters in the geosciences is becoming enormous. If the density of measurements is sufficient, significant non-parametric spatial statistics can be derived from the data. In this context, we propose to use and adapt the Direct Sampling multiple-points simulation method (DS) for the reconstruction of partially informed images. The advantage of the proposed method is that it can accommodate any data disposition and that it can indifferently deal with continuous and categorical variables. The spatial patterns found in the data are mimicked without model inference. Therefore, very few assumptions are required to define the spatial structure of the reconstructed fields, and very limited parameterization is needed to make the proposed approach extremely simple from a user perspective. The different examples shown in this paper give appealing results for the reconstruction of complex 3D geometries from relatively small data sets.     

Application of the EnKF and Localization to Automatic History Matching of Facies Distribution and Production Data

The performance of the ensemble Kalman filter (EnKF) for continuous updating of facies location and boundaries in a reservoir model based on production and facies data for a 3D synthetic problem is presented. The occurrence of the different facies types is treated as a random process and the initial distribution was obtained by truncating a bi-Gaussian random field. Because facies data are highly non-Gaussian, re-parameterization was necessary in order to use the EnKF algorithm for data assimilation; two Gaussian random fields are updated in lieu of the static facies parameters. The problem of history matching applied to facies is difficult due to (1) constraints to facies observations at wells are occasionally violated when productions data are assimilated; (2) excessive reduction of variance seems to be a bigger problem with facies than with Gaussian random permeability and porosity fields; and (3) the relationship between facies variables and data is so highly non-linear that the final facies field does not always honor early production data well. Consequently three issues are investigated in this work. Is it possible to iteratively enforce facies constraints when updates due to production data have caused them to be violated? Can localization of adjustments be used for facies to prevent collapse of the variance during the data-assimilation period? Is a forecast from the final state better than a forecast from time zero using the final parameter fields?To investigate these issues, a 3D reservoir simulation model is coupled with the EnKF technique for data assimilation. One approach to enforcing the facies constraint is continuous iteration on all available data, which may lead to inconsistent model states, incorrect weighting of the production data and incorrect adjustment of the state vector. A sequential EnKF where the dynamic and static data are assimilated sequentially is presented and this approach seems to have solved the highlighted problems above. When the ensemble size is small compared to the number of independent data, the localized adjustment of the state vector is a very important technique that may be used to mitigate loss of rank in the ensemble. Implementing a distance-based localization of the facies adjustment appears to mitigate the problem of variance deficiency in the ensembles by ensuring that sufficient variability in the ensemble is maintained throughout the data assimilation period. Finally, when data are assimilated without localization, the prediction results appear to be independent of the starting point. When localization is applied, it is better to predict from the start using the final parameter field rather than continue from the final state.     

Feasibility Analysis of the Use of Binary Genetic Algorithms as Importance Samplers Application to a 1-D DC Resistivity Inverse Problem

DC resistivity inverse problems are ill-posed. For the Vertical Electrical Sounding method the acceptable solutions lie in very narrow elongated-shape regions in the model space. To characterize this ensemble of solutions is a central question. In a Bayesian framework this issue is solved adopting as solution the so-called model posterior probability distribution. However, due to the nonlinearity of the problem, this distribution is not explicitly known, or it is difficult to calculate. Therefore, algorithms that efficiently sample the model space according to it (importance sampling) are very desirable. The main goal of this paper is to numerically explore the performance of binary genetic algorithms as posterior importance sampling strategies. Their behavior will be firstly analyzed using 2D synthetic posterior test functions bearing the relevant properties of the real geo-electrical inverse problem. The conclusions will be again checked through the histogram reconstruction of parameters in a synthetic VES case, and eventually, in a real, higher dimensional, sea-water coastal intrusion problem, by comparing the results with those obtained with a theoretically correct Metropolis-Hasting importance sampler (simulated annealing without cooling). Percentile curves are introduced as an effective tool for risk assessment. We show that binary genetic algorithms perform well under very general assumptions. When the roulette wheel is the selection method used, mutation is over 10%, and the algorithm does not incorporate elitism. The results do not depend on the values of the remaining tuning parameters. Finally, to improve the efficiency of the sampling strategy, we introduce a binary genetic algorithm with oriented search space. This is done with the help of linearization of the forward operator and singular value decomposition around the maximum posterior estimate. It is shown, also, that the logarithmic model parameterization is adequate for this task.     

Application of electrical resistivity tomography to evaluate the variation in moisture content of waste during 2 months of degradation

Moisture content is considered to play an important role in the degradation of municipal solid waste (MSW). The present study was carried out to test the use of electrical resistivity tomography (ERT) to estimate the daily variations in the volumetric moisture content (VMC) during waste degradation. The degradation of MSW in a laboratory-scale reactor was monitored daily by ERT and time domain reflectometry (TDR) over a period of 55 days after the waste was landfilled. The bulk electrical resistivity, normalised to a standard temperature of 25 °C, was compared to the VMC obtained by TDR. The relationship between the bulk electrical resistivity and the VMC was not obvious in the upper unit, because there was a bias in the positions of the ERT and TDR measurements due to the settling of the waste. However, the bulk electrical resistivity depended on the VMC in the lower unit of the reactor. According to a power equation fitted in the lower unit, the 2-D distributions of the VMC are presented. Variations in the VMC reflected the processes of waste degradation and leachate transportation. Furthermore, the volume of water stored in the lower unit of the reactor was calculated and found to be consistent with that estimated from the gravimetric moisture content during waste sampling. The data showed that ERT could be used to estimate the variations in moisture content in the initial period following the landfilling of waste.     

Stability of Kerogen Classification with Regard to Image Segmentation

This paper investigates the stability of an automatic system for classifying kerogen material from images of sieved rock samples. The system comprises four stages: image acquisition, background removal, segmentation, and classification of the segmented kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation parameter d, called “overlap”, touching pieces of kerogen may be split differently. The aim of this study is to establish how robust the classification result is to variations of the segmentation parameter. There are two issues that pose difficulties in carrying out an experiment. First, even a trained professional may be uncertain when distinguishing between isolated pieces of inertinite and vitrinite, extracted from transmitted-light microscope images. Second, because manual labelling of large amount of data for training the system is an arduous task, we acquired the true labels (ground truth) only for the pieces obtained at overlap d=0.5. To construct ground truth for various values of d we propose here label-inheritance trees. With thus estimated ground truth, an experiment was carried out to evaluate the robustness of the system to changes in the segmentation through varying the overlap value d. The average system accuracy across values of d spanning the range from 0 to 1 was 86.5%, which is only slightly lower than the accuracy of the system at the design value of d=0.5 (89.07%).     

Compressional behavior of omphacite to 47 GPa

Omphacite is an important mineral component of eclogite. Single-crystal synchrotron X-ray diffraction data on natural (Ca, Na) (Mg, Fe, Al)Si₂O₆ omphacite have been collected at the Advanced Photon Source beamlines 13-BM-C and 13-ID-D up to 47 GPa at ambient temperature. Unit cell parameter and crystal structure refinements were carried out to constrain the isothermal equation of state and compression mechanism. The third-order Birch–Murnaghan equation of state (BM3) fit of all data gives V ₀ = 423.9(3) Å³, K _T0 = 116(2) GPa and K _T0′ = 4.3(2). These elastic parameters are consistent with the general trend of the diopside–jadeite join. The eight-coordinated polyhedra (M2 and M21) are the most compressible and contribute to majority of the unit cell compression, while the SiO₄ tetrahedra (Si1 and Si2) behave as rigid structural units and are the most incompressible. Axial compressibilities are determined by fitting linearized BM3 equation of state to pressure dependences of unit cell parameters. Throughout the investigated pressure range, the b-axis is more compressible than the c-axis. The axial compressibility of the a-axis is the largest among the three axes at 0 GPa, yet it quickly drops to the smallest at pressures above 5 GPa, which is explained by the rotation of the stiffest major compression axis toward the a-axis with the increase in pressure.     

The Application of Computerized Pattern Recognition to the Prediction of Mineral Resources

The technique of mineral prediction by pattern recognition has been developed through the applicationof computerized pattern recognition to geological exploration. The principles and computing method of thistechnique as well as some characteristics of its application in geological exploration are expounded in thispaper. Some of the study results gained by the authors in this aspect are also given. which include classifica-tion of oil-field waters. evaluation of gossans of main ore deposits in China, prediction of ore resources inthe Dachang Sn-polymetallic field. and appraisal of Pb and Sn anomalies and prediction of mineral re-sources in southern Hunan. Some of the prediction results have been proved correct.     

Application of the geochemical prospecting to improve oilfield structure models

Background： Complicated structure of surveyed area has minor efficiency of seismic surveys-the main method for petroleum and gas prospecting and interpretation of subsurface structure of the Earth. This paper gives an example that in certain cases geochemical surveys can be used as additional tool to establish suitable geological model of the trap. This paper presents geochemical studies in the southeastern part of European territory of Russia-Nagumanovskoe oilfield. Complicated geological structure causes difficulties to create suitable geological model of the trap itself. There are two models of geological structure for the Nagumanovskoe oilfield： Variant A： Researchers assume the presence of gas condensate accumulation and perspective for exploration traps. Variant B： Researchers assume presence of 2 local structures. Method and results： Geochemical data came from over than 2000 shallow probes （2 meters, 6.6 feet） of soil gas samples collected every 200 meters over more than 7 different long profiles. The adsorbed soil gases were analyzed for hydrocarbones. All samples were decomposed into geochemical populations. According to geochemical data the Nagumanovskoe oilfield was well defined on all geochemical profiles. Examination and comparison of hydrocarbon ratio plots and magnitude graphs for each of the two models indicate that the more anomalous magnitude sites agree with the disposition of structures according to model B. The geochemical oil/gas data exhibit clearly defined compositional sub-populations which match the composition of the underlying reservoirs, difference in standard deviation of hydrocarbon values at background and Cretaceous structure （model B） leads to the deduction that variant B is more suitable geological structure model of the Nagumanovskoe oilfield.     

Application of genetic algorithm technique to inverse modeling of tide–aquifer interaction

Modeling of tide–aquifer interaction plays a vital role in the management of coastal aquifer systems. A novel and robust methodology is presented in this paper for estimating aquifer parameters of coastal aquifers from tide–aquifer interaction data using tide–aquifer interaction model and genetic algorithm (GA). Two stand-alone computer programs were developed to optimize hydraulic diffusivities of unconfined and confined coastal aquifers at multiple sites using GA technique and tide–aquifer interaction model and considering two approaches (‘lumped tidal component approach’ and ‘multi-tidal component approach’). Five sets of real-world tide–aquifer interaction data at two sites of an unconfined aquifer and one set of tide–aquifer interaction data at three sites of a confined aquifer were used to demonstrate the efficacy of the methodology. The analysis of the GA-based inverse modeling results indicated that the ‘multi-tidal component approach’ yields more accurate and reliable hydraulic diffusivities for the unconfined aquifer (RMSE = 0.0129–0.0521 m, NSE = 0.70–0.97, and d1 = 0.91–0.99) as well as for the confined aquifer (RMSE = 0.0204–0.0545 m, NSE = 0.95–0.97, and d1 = 0.99) compared with the ‘lumped tidal component approach’. A comparative evaluation of data-size revealed that the short-duration datasets of the unconfined aquifer provide more reliable estimate of hydraulic diffusivity than the long-duration datasets. Further, it was found that the spring and neap tidal data yield unreasonable values of hydraulic diffusivity with considerably high values of RMSE and very low values of r ², NSE, and d1, thereby suggesting that spring and neap tidal data are not suitable for aquifer parameter estimation. Overall, it is concluded that the GA-based tide–aquifer interaction model following ‘multi-tidal component approach’ is the most efficient tool for estimating aquifer parameters of unconfined and confined aquifers from tide–aquifer interaction data. The developed methodology is also applicable to other coastal basins of the world irrespective of hydrogeological settings.     

Experimental Study on Application of Shaped Charge to Rock Materials Cutting

The mechanic affection on the blast holewalls is simply analyzed and cracking propaga-tion caused by shaped charge is explained inthis paper. In the rock materials cutting, pri-