Detecting thermal anomalies within the Molasse Basin, southern Germany     

Wolfram Rühaak  Volker Rath  Christoph Clauser 《Hydrogeology Journal》2010,18(8):1897-1915

The groundwater flow regime at great depth within the Molasse Basin (SW Germany) was studied. Data relevant for a flow model at 600–1,600 m depth are sparse in the western part of the basin. However, temperature measurements are available covering much of the area at a wide range of depths. Therefore, a thermal 3D steady-state model was set up with the aim of comparing modeled with observed subsurface temperatures. Stratigraphic information from many boreholes was also available, but only a few values of rock thermal conductivity and heat-production rate could be obtained. Some strong thermal residual anomalies were identified with respect to the purely conductive model, especially along fault zones, and within stratigraphic layers with high hydraulic conductivity. These anomalies can be explained by various advective heat-transport mechanisms, yet most explanations can be eliminated. The most plausible constellation explaining the major positive thermal anomalies of 10 Kelvin and more is a fault zone of E–W strike, intersected by an aquifer with flow parallel to the fault zone. This concept was investigated by using a simplified type model. In spite of some shortcomings, the method presented here can be used to identify temperature anomalies, and to identify possible explanations.  相似文献   

Predicting Threshold Exceedance by Local Block Means in Soil Pollution Surveys     

Christoph Hofer  Andreas Papritz 《Mathematical Geosciences》2010,42(6):631-656

Soil contamination by heavy metals and organic pollutants around industrial premises is a problem in many countries around the world. Delineating zones where pollutants exceed tolerable levels is a necessity for successfully mitigating related health risks. Predictions of pollutants are usually required for blocks because remediation or regulatory decisions are imposed for entire parcels. Parcel areas typically exceed the observation support, but are smaller than the survey domain. Mapping soil pollution therefore involves a local change of support. The goal of this work is to find a simple, robust, and precise method for predicting block means (linear predictions) and threshold exceedance by block means (nonlinear predictions) from data observed at points that show a spatial trend. By simulations, we compared the performance of universal block kriging (UK), Gaussian conditional simulations (CS), constrained (CK), and covariance-matching constrained kriging (CMCK), for linear and nonlinear local change of support prediction problems. We considered Gaussian and positively skewed spatial processes with a nonstationary mean function and various scenarios for the autocorrelated error. The linear predictions were assessed by bias and mean square prediction error and the nonlinear predictions by bias and Peirce skill scores.  相似文献