Stephen J. Fraser

Interpreting aerial gamma-ray surveys utilising geomorphological and weathering models

Abstract Applying weathering and geomorphological data can improve the interpretation of aerial gamma-ray surveys. Data from two potentially mineralised areas in eastern Australia are used to demonstrate the new approach. At Develin Creek, NW of Rockhampton in Queensland, Permian andesitic basalts host VMS-style Cu-Zn deposits. Topographic and SPOT data were used to generate a digital elevation model (DEM) over an area of approx. 125 km 2 . The Universal Soil Loss Equation (USLE) was then applied to model soil erosion and deposition. Surface estimates of the distribution of clay, iron-oxides and vegetation were derived from Landsat TM data. Multi-variate linear regression and analysis of residuals (i.e. the difference between predicted and observed maps for each radioelement) were then used to look for anomalies that may indicate near-surface mineralisation. This analysis indicated no surface indicators of undiscovered mineralisation, a finding in accord with extensive ground geophysics and mapping. At Barry, SW of Bathurst, NSW, Ordovician basalts potentially host porphyry-related gold deposits. These deposits are most probably seen in aerial gamma-ray survey data through elevated K in alteration haloes. A DEM was created over a study area of approx. 180 km 2 and the soil erosion/deposition characteristics modelled using the USLE. Laboratory analyses of rock and soil samples showed that weathering of the basalt (median radioelement contents of 1.4% K, 0.7 ppm eU, 1.6 ppm eTh) produced a soil with decreased K (0.8%) but increased eU (1.5 ppm) and eTh (7.0 ppm). These values were used along with the erosion model to predict the surface K distribution, which was then compared to the observed distribution using a linear regression model. Areas with high K residuals are considered prospective for Au mineralisation.

Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps

OBJECTIVE To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis. BACKGROUND Self-Organizing Maps (SOM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge. DESIGN/METHODS We used SOM to analyze T1, T2 and FLAIR acquisitions from two MRI machines in our service from 14 patients with brain tumors confirmed by biopsies--three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SOM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters. RESULTS Damaged tissue was separated from the normal tissue using the SOM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor--like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger. CONCLUSIONS Our results showed that SOM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors.

On the scope and management of pesticide pollution of Swedish groundwater resources: The Scanian example

Twenty-three south-Swedish public supply wells were studied to assess pesticide pollution of regional groundwater resources. Relations between pesticide occurrence, hydrogeology, and land use were analyzed using Kohonen’s Self-Organizing Maps approach. Pesticides are demonstrated to be substantially present in regional groundwater, with detections in 18 wells. Concentrations above the drinking water threshold are confirmed for nine wells. Observations indicate considerable urban influence, and lagged effects of past, less restricted use. Modern, oxic waters from shallow, unconfined, unconsolidated or fracture-type bedrock aquifers appear particularly vulnerable. Least affected waters appear primarily associated with deeper wells, anoxic conditions, and more confined sediment aquifers lacking urban influence. Comprehensive, standardized monitoring of pesticides in groundwater need to be implemented nationwide to enable sound assessments of pollution status and trends, and to develop sound groundwater management plans in accordance with the Water Framework Directive. Further, existing water protection areas and associated regulations need to be reassessed.

Reopening an Abandoned Underground Mine - 3D Digital Mine Inventory Model from Historical Data and Rapid Laser Scanning

The decision to reopen a mine abandoned for 30 years is going to be complex because a significant financial commitment is going to depend on a reevaluation of both the remaining mineral resource and the surviving mine infrastructure. Historical pre-digital records will need to be assessed and new data acquired both to prove up an ore reserve and to assess the status of existing mine development and infrastructure. We describe how historic mining data were collected and compiled on the mine infrastructure (tunnels, buildings) and how mineral resource data were prepared and transferred into a 3D digital mine inventory for the Otanmaki Mine in Finland. We also demonstrate the applicability of rapid handheld laser scanning for simultaneous localization and mapping to supplement the available mine plans. Point-cloud laser scans were collected in the hoisting tower and evacuation shelter.

Lithological classification of large‐scale 3D inversion of airborne electromagnetic, gravity gradiometry, and magnetic data: A case study from Reid‐Mahaffy, Ontario

Multi-sensor airborne platforms capable of measuring electromagnetic, gravity, and magnetic data are now being deployed for mineral exploration. The availability of such systems poses a significant challenge for the exploration geophysicist: How do you generate a common earth model which satisfies all data? We address this with a case study from the Reid-Mahaffy test site in Ontario to demonstrate how our multiple 3D inversions of MEGATEM II timedomain electromagnetic, FALCON gravity gradiometry, and TMI data can be analyzed by self-organizing maps (SOM) to produce 3D pseudo-lithological models that can be used for geological mapping and improved exploration targeting. Our analyses are shown to be in good agreement with the known geology of the Reid-Mahaffy area.

Self-organizing maps as a tool for segmentation of Magnetic Resonance Imaging (MRI) of relapsing-remitting multiple sclerosis

Multiple Sclerosis (MS) is the most prevalent demyelinating disease of the Central Nervous System, being the Relapsing-Remitting (RRMS) its most common subtype. We explored here the viability of use of Self Organizing Maps (SOM) to perform automatic segmentation of MS lesions apart from CNS normal tissue. SOM were able, in most cases, to successfully segment MRIs of patients with RRMS, with the correct separation of normal versus pathological tissue especially in supratentorial acquisitions, although it could not differentiate older from newer lesions.

Imputation of reactive silica and available alumina in bauxites by self-organizing maps

Geochemical analyses can provide multiple analytical variables. Accordingly, the generation of large geochemical databases enables imputation studies or analytical estimates of missing values or complex measuring. The processing of bauxite is a key step in the production of aluminum, in which the determination of Reactive Silica (RxSiO<inf>2</inf>) and Available Alumina (AvAl<inf>2</inf>O<inf>3</inf>) are very relevant. The traditional analytical method for achieving RxSiO<inf>2</inf> has limitations associated with poor repeatability and reproducibility of results. Based on the values from the unsupervised Self-Organizing Maps technique, this study aims to develop, systematically, the imputation of missing grades of the geochemical composition of bauxite samples of a database from three trial projects, for the variables: total Al<inf>2</inf>O<inf>3</inf>; total SiO<inf>2</inf>; total Fe<inf>2</inf>O<inf>3</inf>; and total TiO<inf>2</inf>. Each project was submitted to partial exclusion of AvAl<inf>2</inf>O<inf>3</inf> and RxSiO<inf>2</inf> values, in proportion of 20%, 30%, 40% and 50%, to investigate the SOM technique as imputation method for RxSiO<inf>2</inf> and AvAl<inf>2</inf>O<inf>3</inf>. By comparing the imputed values from the SOM analysis with the original values, SOM technique demonstrated to be an imputation tool capable of obtaining analytical results with up to 50% of missing data. Specifically, the best results demonstrate that AvAl<inf>2</inf>O<inf>3</inf> can be obtained by imputation with a higher correlation than RxSiO<inf>2</inf>, based on the parameters and variables involved in the study. Similarity in the nature of samples and an increase in the number of embedded analytical variables are factors that provided better imputation results.