We used ground penetrating radar (GPR) data to help determine the spatial distribution and the subsurface geometry of clastic dikes at the Hanford Site. This information will help to improve the understanding of the hydrological role of these ubiquitous clastic dikes at the Hanford Site. We collected 100 MHz ground penetrating radar (GPR) 3-D surface reflection data at two sites, the 216-S-16 (S-16) Pond and the Army Loop Road sites, and 2-D reflection data along a 6.9 km linear transect (Traverse site) near the Army Loop Road site. The dikes are distinguished in the GPR data by a strongly attenuated zone, disruptions in the continuity of reflections, and diffractions where reflections are disrupted. In general, the data quality is better at the Army Loop Road and Traverse sites than at the S-16 Pond site, probably due to the presence of cobbles at the S-16 Pond site. A high-moisture, fine-grained unit probably causes the strong reflections at the Army Loop Road site and the Traverse survey site. The signal penetration varies between 5 and 12 m below the land surface.

Abstract Sole markings from the Upper Cambrian Booley Bay Formation at Booley Bay, Co. Wexford, Ireland, previously described as the Ediacaran-type fossil Ediacaria booleyi appear to possess a number of morphological characteristics which are not consistent with assignment to the genus Ediacaria , nor any other known Ediacaran taxon. An inorganic origin for the structures has been proposed by several workers; we tentatively consider them to be at least in part organic, and are currently working to evaluate this hypothesis critically. If organic, the organisms must have had a density similar to that of the sediment in which they were emplaced, and to survive the transport process, they must have had a rigid integument. Neither of these properties appears to be consistent with an interpretation as an Ediacaran-type organism. We suggest here that the Booley Bay specimens should be removed from the genus Ediacaria . If Ediacaria booleyi is not related to known Ediacaran organisms, this would remove a key aspect of the argument against a mass extinction at the Precambrian/Cambrian boundary.

Abstract Stochastic models have been used extensively to represent uncertainty in the spatial distribution of aquifer properties and its impact on prediction of groundwater flow and transport behavior. Because natural porous media are often strongly heterogeneous and exhibit complex spatial structure, it is not possible for any model to completely characterize the spatial distribution of aquifer properties. Several models have been proposed, each of which relies on specific assumptions regarding the character of natural spatial structure. In this study we have performed a direct comparison of the performance of a number of stochastic simulation methods, using a geologically realistic synthetic dataset as the basis for such a comparison. The methods are evaluated in a Monte Carlo sense through comparison of the predicted distributions of a variety of measures of flow and transport behavior. The results indicate that classical (second-order) stochastic models can be expected to provide biased and nonconservative predictions of many practical measures of flow and transport behavior and to underestimate the uncertainty in those predictions. These empirical results are supported by theoretical arguments based on statistical entropy. Models that preserve the spatial continuity of geologic facies to a greater degree provide better predictions of flow and transport behavior.

Abstract Because the reservoir properties of various rock types can differ considerably, it is best to simulate the distribution of reservoir properties in two steps: simulate the rock types, then simulate the reservoir properties conditional on the rock types; however, identifying the rock types themselves can be difficult, due to the interaction of depositional processes and diagenesis, and the variable amount and quality of data available from well to well. This chapter presents a methodology using cluster analysis for identifying petrophysical rock types in a training data set of cored wells. A case study from the Cretaceous Muddy Sandstone of the Powder River basin illustrates the methodology. After identifying the rock types, analyzing the core properties provided geologic information concerning each rock type. Discriminant function analysis extended the rock type classification to wells without core data. Sequential indicator simulation was used to model and simulate the spatial distribution of the rock types in three dimensions. An annealing method was used to postprocess the simulations so that they honor the rock type transition frequencies seen in the well data.