Identification of regions enriched or depleted in radioelements through nondistributional analysis of aerial radiometric data

Throughout the aerial radiometric reconnaissance survey portion of the U.S. Department of Energy's National Uranium Resource Evaluation (NURE) program, the identification of outliers (anomalies) was an important approach to locating regions with radio-element concentrations that are either higher or lower than expected. The method introduced herein to locate such regions involves three steps: selection of a high (or low) threshold for the variate of interest; use of the sample percentile to identify all points of interest; and movement of a window over the selected data to locate significant clusters of observations. These steps, applied to aerial radiometric ²¹⁴Bi (equivalent uranium) data collected over the Copper Mountain area in Wyoming, resulted in the identification of areas enriched in that variate.     

Characteristic analysis—1981: Final program and a possible discovery

The latest or n ewest version of the char acteristic an alysis (NCHARAN)computer program offers the exploration geologist a wide variety of options for integrating regionalized multivariate data. The options include the selection of regional cells for characterizing deposit models, the selection of variables that constitute the models, and the choice of logical combinations of variables that best represent these models. Moreover, the program provides for the display of results which, in turn, makes possible review, reselection, and refinement of a model. Most important, the performance of the above-mentioned steps in an interactive computing mode can result in a timely and meaningful interpretation of the data available to the exploration geologist. The most recent application of characteristic analysis has resulted in the possible discovery of economic sulfide mineralization in the Grong area in central Norway. Exploration data for 27 geophysical, geological, and geochemical variables were used to construct a mineralized and a lithogeochemical model for an area that contained a known massive sulfide deposit. The models were applied to exploration data collected from the Gjersvik area in the Grong mining district and resulted in the identification of two localities of possible mineralization. Detailed field examination revealed the presence of a sulfide vein system and a partially inverted stratigraphic sequence indicating the possible presence of a massive sulfide deposit at depth.     

The present day formation of apatite in Mexican continental margin sediments

Results of pore water and sediment analyses from the western Mexican continental margin strongly suggest the present day formation of apatite. The interstitial water phosphate and fluoride profiles indicate chemical removal at a depth which corresponds to a large maximum in the phosphorus content of the sediments. Apatite is identified within this maximum via X-ray diffraction but is elsewhere undetectable in the core. Radioisotopic thorium, uranium, and radium data support the conclusion that this deposit is modern. The present day depositional environment is consistent with those reported by other workers for phosphorite formation with the exception that pore water magnesium is not depleted below its seawater value.     

Holocene vegetational history of the Kootenai River Valley,Montana

Pollen records in the Kootenai and Fisher River drainages in western Montana reveal a fivezone sequence of Holocene vegetation change. Deposition of Glacier Peak Ash-Layer G (ca. 10,540 ± 660 yr B.P.) in the lowermost sediments (clay intermixed with pebbles) at Tepee Lake gives a minimum date for the initiation of sedimentation. Initial vegetation on the newly deglaciated terrain was dominated by Pinus (probably white bark pine) with small amounts of Gramineae, Picea and Abies, reflecting a relatively cool, moist macroclimate. Two vegetation units appear to contribute to Pollen Zone II (ca. 11,000–7100 yr B.P.): arboreal communities with pines, along with Pseudotsuga or Larix, or both, and treeless vegetation dominated by Artemisia. Pollen Zone II represents an overall warmer macroclimate than occurred upon ice withdrawal. After ca. 7100 yr B.P. (Pollen Zone III) diploxylon pines became a major pollen contributor near both Tepee Lake and McKillop Creek Pond, indicating an expansion of xerophytic forest (P. contorta and P. ponderosa) along with an increase in the prominence of Pseudotsuga menziesii or Larix occidentalis, or both. Artemisia briefly expanded coverage near Tepee Lake concomitant with the Mazama ashfall ca. 6700 yr B.P. A short-term climatic trend with more available water began after ca. 4000 yr B.P. as Abies (probably A. grandis) along with Picea engelmannii became a more regular component of the forest surrounding both sites. Emergence of the modern macroclimate is indicated primarily with the first regular appearance of Tsuga heterophylla in the pollen record by ca. 2700 yr B.P., synchronous with the development of western hemlock forest within the same latitudes in northern Idaho and northeastern Washington.     

Coseismic and post-seismic signatures of the Sumatra 2004 December and 2005 March earthquakes in GRACE satellite gravity

The GRACE satellite mission has been measuring the Earth's gravity field and its temporal variations since 2002 April. Although these variations are mainly due to mass transfer within the geofluid envelops, they also result from mass displacements associated with phenomena including glacial isostatic adjustment and earthquakes. However, these last contributions are difficult to isolate because of the presence of noise and of geofluid signals, and because of GRACE's coarse spatial resolution (>400 km half-wavelength). In this paper, we show that a wavelet analysis on the sphere helps to retrieve earthquake signatures from GRACE geoid products. Using a wavelet analysis of GRACE geoids products, we show that the geoid variations caused by the 2004 December ( M _w= 9.2) and 2005 March ( M _w= 8.7) Sumatra earthquakes can be detected. At GRACE resolution, the 2004 December earthquake produced a strong coseismic decrease of the gravity field in the Andaman Sea, followed by relaxation in the area affected by both the Andaman 2004 and the Nias 2005 earthquakes. We find two characteristic timescales for the relaxation, with a fast variation occurring in the vicinity of the Central Andaman ridge. We discuss our coseismic observations in terms of density changes of crustal and upper-mantle rocks, and of the vertical displacements in the Andaman Sea. We interpret the post-seismic signal in terms of the viscoelastic response of the Earth's mantle. The transient component of the relaxation may indicate the presence of hot, viscous material beneath the active Central Andaman Basin.     

A crustal seismic velocity model for the UK, Ireland and surrounding seas

A regional model of the 3-D variation in seismic P -wave velocity structure in the crust of NW Europe has been compiled from wide-angle reflection/refraction profiles. Along each 2-D profile a velocity–depth function has been digitised at 5 km intervals. These 1-D velocity functions were mapped into three dimensions using ordinary kriging with weights determined to minimise the difference between digitised and interpolated values. An analysis of variograms of the digitised data suggested a radial isotropic weighting scheme was most appropriate. Horizontal dimensions of the model cells are optimised at 40 × 40 km and the vertical dimension at 1 km. The resulting model provides a higher resolution image of the 3-D variation in seismic velocity structure of the UK, Ireland and surrounding areas than existing models. The construction of the model through kriging allows the uncertainty in the velocity structure to be assessed. This uncertainty indicates the high density of data required to confidently interpolate the crustal velocity structure, and shows that for this region the velocity is poorly constrained for large areas away from the input data.     

Assessment of Exploration Bias in Data-Driven Predictive Models and the Estimation of Undiscovered Resources

The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits.