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J.C. Davis

Publications and source records attributed to J.C. Davis.

16 recordsLinked to original sources
USGS2006

Two models for evaluating landslide hazards

Two alternative procedures for estimating landslide hazards were evaluated using data on topographic digital elevation models (DEMs) and bedrock lithologies in an area adjacent to the Missouri River in Atchison County, Kansas, USA. The two procedures are based on the likelihood ratio model but utilize different assumptions. The empirical likelihood ratio model is based on non-parametric empirical univariate frequency distribution functions under an assumption of conditional independence while the multivariate logistic discriminant model assumes that likelihood ratios can be expressed in terms of logistic functions. The relative hazards of occurrence of landslides were estimated by an empirical likelihood ratio model and by multivariate logistic discriminant analysis. Predictor variables consisted of grids containing topographic elevations, slope angles, and slope aspects calculated from a 30-m DEM. An integer grid of coded bedrock lithologies taken from digitized geologic maps was also used as a predictor variable. Both statistical models yield relative estimates in the form of the proportion of total map area predicted to already contain or to be the site of future landslides. The stabilities of estimates were checked by cross-validation of results from random subsamples, using each of the two procedures. Cell-by-cell comparisons of hazard maps made by the two models show that the two sets of estimates are virtually identical. This suggests that the empirical likelihood ratio and the logistic discriminant analysis models are robust with respect to the conditional independent assumption and the logistic function assumption, respectively, and that either model can be used successfully to evaluate landslide hazards. ?? 2006.

Computers & Geosciences↗
USGS2003

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

Engineering Geology↗
USGS2000

Assessing the Kansas water-level monitoring program: An example of the application of classical statistics to a geological problem

Geologists may feel that geological data are not amenable to statistical analysis, or at best require specialized approaches such as nonparametric statistics and geostatistics. However, there are many circumstances, particularly in systematic studies conducted for environmental or regulatory purposes, where traditional parametric statistical procedures can be beneficial. An example is the application of analysis of variance to data collected in an annual program of measuring groundwater levels in Kansas. Influences such as well conditions, operator effects, and use of the water can be assessed and wells that yield less reliable measurements can be identified. Such statistical studies have resulted in yearly improvements in the quality and reliability of the collected hydrologic data. Similar benefits may be achieved in other geological studies by the appropriate use of classical statistical tools.

The Compass: Earth Science Journal of Sigma Gamma ↗
USGS1999

Multivariate analyses of Erzgebirge granite and rhyolite composition: Implications for classification of granites and their genetic relations

High-precision major, minor and trace element analyses for 44 elements have been made of 329 Late Variscan granitic and rhyolitic rocks from the Erzgebirge metallogenic province of Germany. The intrusive histories of some of these granites are not completely understood and exposures of rock are not adequate to resolve relationships between what apparently are different plutons. Therefore, it is necessary to turn to chemical analyses to decipher the evolution of the plutons and their relationships. A new classification of Erzgebirge plutons into five major groups of granites, based on petrologic interpretations of geochemical and mineralogical relationships (low-F biotite granites; low-F two-mica granites; high-F, high-P2O5 Li-mica granites; high-F, low-P2O5 Li-mica granites; high-F, low-P2O5 biotite granites) was tested by multivariate techniques. Canonical analyses of major elements, minor elements, trace elements and ratio variables all distinguish the groups with differing amounts of success. Univariate ANOVA's, in combination with forward-stepwise and backward-elimination canonical analyses, were used to select ten variables which were most effective in distinguishing groups. In a biplot, groups form distinct clusters roughly arranged along a quadratic path. Within groups, individual plutons tend to be arranged in patterns possibly reflecting granitic evolution. Canonical functions were used to classify samples of rhyolites of unknown association into the five groups. Another canonical analysis was based on ten elements traditionally used in petrology and which were important in the new classification of granites. Their biplot pattern is similar to that from statistically chosen variables but less effective at distinguishing the five groups of granites. This study shows that multivariate statistical techniques can provide significant insight into problems of granitic petrogenesis and may be superior to conventional procedures for petrological interpretation.

Computers & Geosciences↗
USGS1999

Subdivision of Holocene Baltic sea sediments by their physical properties [Gliederung holozaner ostseesedimente nach physikalischen Eigenschaften]

The Holocene sediment sequence of a core taken within the centre of the Eastern Gotland Basin was subdivided into 12 lithostratigraphic units based on MSCL-data (sound velocity, wet bulk density, magnetic susceptibility) using a multivariate classification method. The lower 6 units embrace the sediments until the Litorina transgression, and the upper 6 units subdivide the brackish-marine Litorina- and post-Litorina sediments. The upper lithostratigraphic units reflect a change of anoxic (laminated) and oxic (non-laminated) sediments. By application of a numerical stratigraphic correlation method the zonation was extended laterally onto contiguous sediment cores within the central basin. Consequently the change of anoxic and oxic sediments can be used for a general lithostratigraphic subdivision of sediments of the Gotland Basin. A quantitative criterion based on the sediment-physical lithofacies is added to existing subdivisions of the Holocene in the Baltic Sea.

Petermanns Geographische Mitteilungen↗
USGS1998

Singularity and Nonnormality in the Classification of Compositional Data

Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore-Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.

Mathematical Geology↗
USGS1997

Geochemical patterns in soils of the karst region, Croatia

Soil samples were collected at 420 locations in a 5-km grid pattern in the Istria and Gorski Kotar areas of Croatia, and on the Croatian islands of Cres, Rab and Krk, in order to relate geochemical variation in the soils to underlying differences in geology, bedrock lithology, soil type, environment and natural versus anthropogenic influences. Specific objectives included assessment of possible agricultural and industrial sources of contamination, especially from airborne effluent emitted by a local power plant. The study also tested the adequacy of a fixed-depth soil sampling procedure developed for meager karstic soils. Although 40 geochemical variables were analyzed, only 15 elements and 5 radionuclides are common to all the sample locations. These elements can be divided into three groups: (1) those of mostly anthropogenic origin — Pb, V, Cu and Cr; (2) those of mixed origin — radionuclides and Zn; and (3) those of mostly geogene origin — Ba, Sr, Ti, Al, Na, Ca, Mg, Fe, Mn, Ni and Co. Variation in Pb shows a strong correlation with the pattern of road traffic in Istria. The distributions of Ca, Na and Mg in the flysch basins of southern Istria and Slovenia are clearly distinguishable from the distributions of these elements in the surrounding carbonate terrains, a consequence of differences in bedrock permeability, type of drainage and pH. The spatial pattern of 137 Cs from the Chernobyl nuclear power plant accident reflects almost exclusively the precipitation in Istria during the days immediately after the explosion.

Journal of Geochemical Exploration↗
USGS1997

Spatial analysis of temperature (BHT/DST) data and consequences for heat-flow determination in sedimentary basins

Large numbers of bottom-hole temperatures (BHTs) and temperatures measured during drill-stem tests (DSTs) are available in areas explored for hydrocarbons, but their usefulness for estimating geothermal gradients and heat-flow density is limited. We investigated a large data set of BHT and DST measurements taken in boreholes in the American Midcontinent, a geologically uniform stable cratonic area, and propose an empirical correction for BHTs based on relationships between BHTs, DSTs, and thermal logs. This empirical correction is compared with similar approaches determined for other areas. The data were analyzed by multivariate statistics prior to the BHT correction to identify anomalous measurements and quantify external influences. Spatial patterns in temperature measurements for major stratigraphic units outline relations to regional structure. Comparision of temperature and structure trend-surface residuals reveals a relationship between temperature highs and local structure highs. The anticlines, developed by continuous but intermittent movement of basement fault blocks in the Late Paleozoic, are subtle features having closures of 10-30 m and contain relatively small hydrocarbon reservoirs. The temperature anomalies of the order of 5-7 ??C may reflect fluids moving upward along fractures and faults, rather than changes in thermal conductivity resulting from different pore fluids. ?? Springer-Verlag 1997.

Conference Paper↗
USGS1995

Adjusting stream-sediment geochemical maps in the Austrian Bohemian Massif by analysis of variance

The Austrian portion of the Bohemian Massif is a Precambrian terrane composed mostly of highly metamorphosed rocks intruded by a series of granitoids that are petrographically similar. Rocks are exposed poorly and the subtle variations in rock type are difficult to map in the field. A detailed geochemical survey of stream sediments in this region has been conducted and included as part of the Geochemischer Atlas der Republik O??sterreich, and the variations in stream sediment composition may help refine the geological interpretation. In an earlier study, multivariate analysis of variance (MANOVA) was applied to the stream-sediment data in order to minimize unwanted sampling variation and emphasize relationships between stream sediments and rock types in sample catchment areas. The estimated coefficients were used successfully to correct for the sampling effects throughout most of the region, but also introduced an overcorrection in some areas that seems to result from consistent but subtle differences in composition of specific rock types. By expanding the model to include an additional factor reflecting the presence of a major tectonic unit, the Rohrbach block, the overcorrection is removed. This iterative process simultaneously refines both the geochemical map by removing extraneous variation and the geological map by suggesting a more detailed classification of rock types. ?? 1995 International Association for Mathematical Geology.

Mathematical Geology↗
USGS1993

Prediction of hydrocarbons in sedimentary basins

To estimate the undiscovered hydrocarbon potential of sedimentary basins, quantitative play assessments specific for each location in a region may be obtained using geostatistical methods combined with the theory of classification of geological objects, a methodology referred to as regionalization. The technique relies on process modeling and measured borehole data as well as probabilistic methods to exploit the relationship between geology (the "predictor") and known hydrocarbon productivity (the "target") to define prospective stratigraphic intervals within a basin. It is demonstrated in case studies from the oil-producing region of the western Kansas Pennsylvanian Shelf and the gas-bearing Rotliegend sediments of the Northeast German Basin. ?? 1993 International Association for Mathematical Geology.

Mathematical Geology↗
USGS1993

Boundary assessment under uncertainty: A case study

Estimating certain attributes within a geological body whose exact boundary is not known presents problems because of the lack of information. Estimation may result in values that are inadmissible from a geological point of view, especially with attributes which necessarily must be zero outside the boundary, such as the thickness of the oil column outside a reservoir. A simple but effective way to define the boundary is to use indicator kriging in two steps, the first for the purpose of extrapolating control points outside the body, the second to obtain a weighting function which expresses the uncertainty attached to estimations obtained in the boundary region. ?? 1993 International Association for Mathematical Geology.

Mathematical Geology↗
USGS1993

Volumetric calculations in an oil field: The basis method

The basis method for estimating oil reserves in place is compared to a traditional procedure that uses ordinary kriging. In the basis method, auxiliary variables that sum to the net thickness of pay are estimated by cokriging. In theory, the procedure should be more powerful because it makes full use of the cross-correlation between variables and forces the original variables to honor interval constraints. However, at least in our case study, the practical advantages of cokriging for estimating oil in place are marginal. ?? 1993.

Computers & Geosciences↗
USGS1993

A fortran program for Monte Carlo simulation of oil-field discovery sequences

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios. ?? 1993.

Computers & Geosciences↗
USGS1977

Estimation of the probability of success in petroleum exploration

A probabilistic model for oil exploration can be developed by assessing the conditional relationship between perceived geologic variables and the subsequent discovery of petroleum. Such a model includes two probabilistic components, the first reflecting the association between a geologic condition (structural closure, for example) and the occurrence of oil, and the second reflecting the uncertainty associated with the estimation of geologic variables in areas of limited control. Estimates of the conditional relationship between geologic variables and subsequent production can be found by analyzing the exploration history of a "training area" judged to be geologically similar to the exploration area. The geologic variables are assessed over the training area using an historical subset of the available data, whose density corresponds to the present control density in the exploration area. The success or failure of wells drilled in the training area subsequent to the time corresponding to the historical subset provides empirical estimates of the probability of success conditional upon geology. Uncertainty in perception of geological conditions may be estimated from the distribution of errors made in geologic assessment using the historical subset of control wells. These errors may be expressed as a linear function of distance from available control. Alternatively, the uncertainty may be found by calculating the semivariogram of the geologic variables used in the analysis: the two procedures will yield approximately equivalent results. The empirical probability functions may then be transferred to the exploration area and used to estimate the likelihood of success of specific exploration plays. These estimates will reflect both the conditional relationship between the geological variables used to guide exploration and the uncertainty resulting from lack of control. The technique is illustrated with case histories from the mid-Continent area of the U.S.A. ?? 1977 Plenum Publishing Corp.

Journal of the International Association for Mathe↗