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G.C. Ohlmacher

Publications and source records attributed to G.C. Ohlmacher.

5 recordsLinked to original sources
USGS2007

Plan curvature and landslide probability in regions dominated by earth flows and earth slides

Damaging landslides in the Appalachian Plateau and scattered regions within the Midcontinent of North America highlight the need for landslide-hazard mapping and a better understanding of the geomorphic development of landslide terrains. The Plateau and Midcontinent have the necessary ingredients for landslides including sufficient relief, steep slope gradients, Pennsylvanian and Permian cyclothems that weather into fine-grained soils containing considerable clay, and adequate precipitation. One commonly used parameter in landslide-hazard analysis that is in need of further investigation is plan curvature. Plan curvature is the curvature of the hillside in a horizontal plane or the curvature of the contours on a topographic map. Hillsides can be subdivided into regions of concave outward plan curvature called hollows, convex outward plan curvature called noses, and straight contours called planar regions. Statistical analysis of plan-curvature and landslide datasets indicate that hillsides with planar plan curvature have the highest probability for landslides in regions dominated by earth flows and earth slides in clayey soils (CH and CL). The probability of landslides decreases as the hillsides become more concave or convex. Hollows have a slightly higher probability for landslides than noses. In hollows landslide material converges into the narrow region at the base of the slope. The convergence combined with the cohesive nature of fine-grained soils creates a buttressing effect that slows soil movement and increases the stability of the hillside within the hollow. Statistical approaches that attempt to determine landslide hazard need to account for the complex relationship between plan curvature, type of landslide, and landslide susceptibility. ?? 2007 Elsevier B.V. All rights reserved.

Engineering Geology↗
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↗
USGS2005

Kinematics, mechanics, and potential earthquake hazards for faults in Pottawatomie County, Kansas, USA

Many stable continental regions have subregions with poorly defined earthquake hazards. Analysis of minor structures (folds and faults) in these subregions can improve our understanding of the tectonics and earthquake hazards. Detailed structural mapping in Pottawatomie County has revealed a suite consisting of two uplifted blocks aligned along a northeast trend and surrounded by faults. The first uplift is located southwest of the second. The northwest and southeast sides of these uplifts are bounded by northeast-trending right-lateral faults. To the east, both uplifts are bounded by north-trending reverse faults, and the first uplift is bounded by a north-trending high-angle fault to the west. The structural suite occurs above a basement fault that is part of a series of north-northeast-trending faults that delineate the Humboldt Fault Zone of eastern Kansas, an integral part of the Midcontinent Rift System. The favored kinematic model is a contractional stepover (push-up) between echelon strike-slip faults. Mechanical modeling using the boundary element method supports the interpretation of the uplifts as contractional stepovers and indicates that an approximately east-northeast maximum compressive stress trajectory is responsible for the formation of the structural suite. This stress trajectory suggests potential activity during the Laramide Orogeny, which agrees with the age of kimberlite emplacement in adjacent Riley County. The current stress field in Kansas has a N85??W maximum compressive stress trajectory that could potentially produce earthquakes along the basement faults. Several epicenters of seismic events (<M2.0) are located within 10 km of the structural suite. One epicenter is coincident with the northwest boundary of the uplift. This structural suite, a contractional stepover between echelon northeast-trending right-lateral faults, is similar to that mapped in the New Madrid Seismic Zone, and both areas currently feature roughly east-west maximum compressive stress trajectory. Based on these similarities, the faults in Pottawatomie County have the potential for seismicity. The results demonstrate that mechanical analysis of minor structural features can improve our knowledge of local earthquake hazards. ?? 2004 Elsevier B.V. All rights reserved.

Tectonophysics↗
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↗