George Leblanc

Denoising of aeromagnetic data via the wavelet transform

Noise has traditionally been suppressed or eliminated in aeromagnetic data sets by the use of Fourier analysis filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they produce undesirable effects when denoising features of moderate to large amplitude and spatial extent. In this study, a new wavelet analysis procedure is presented that substantially reduces the contribution from high-frequency random noise and noise that is user defined. Applications to both synthetic data and aeromagnetic data from southern Alberta, Canada, show that the wavelet method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data than other methods.

An automatic network-extraction algorithm applied to magnetic survey data for the identification and extraction of geologic lineaments

Lineament analysis is commonly undertaken by interpreting a wide range of geoscientific data to delineate geologic structures. These structures include faults, fractures, dykes, and lithological contacts, which provide information for geologic mapping and mineral and energy exploration. We offer a simple automatic lineament analysis method that combines the principles of peak-identification algorithms typically used in geophysical data interpretation and a GIS drainage “network-extraction” algorithm commonly applied to a topographic surface. We apply this network-extraction process to a magnetic surface (grid) rather than a topographic one. The GIS approach calculates the curvature of a surface to determine whether a specific coordinate is at a minimum (trough). A simple quadratic surface is computed for a moving 3 × 3 window to determine if the local surface has the form of a dipping plane (or a trough). Continuity of troughs between adjacent kernels defines lineaments that typically correspond to stream...

Wavelet Analysis Approach to De-Noising of Magnetic Data

Summary A new de-noising technique using Wavelet Analysis to remove unwanted signal (noise) from an aeromagnetic data set is compared to the conventional techniques of Fourier analysis, 4 th difference and Naudy filtering. The new Wavelet Analysis approach of thresholding coefficients of the Wavelet Transform on wavelet levels where noise is considered present produces a superior de-noised product. Noise which is composed of 1/m integer multiples of the significant features in the data observati ons (i.e. the three point spike), are easier to remove. This method can be extended to other geophysical procedures such as miner al exploration magnetic bore-hole logging where noise often constitutes a large portion of the overall signal.

Spectral Reflectance of Polar Bear and Other Large Arctic Mammal Pelts; Potential Applications to Remote Sensing Surveys

Spectral reflectance within the 350–2500 nm range was measured for 17 pelts of arctic mammals (polar bear, caribou, muskox, and ringed, harp and bearded seals) in relation to snow. Reflectance of all pelts was very low at the ultraviolet (UV) end of the spectrum ( 90%), gradually dropped to near zero at 1500 nm, and then fluctuated between zero and 20% up to 2500 nm. All pelts could be distinguished from clean snow at many wavelengths. The polar bear pelts had higher and more uniform averaged reflectance from about 600–1100 nm than most other pelts, but discrimination was challenging due to variation in pelt color and intensity among individuals within each species. Results suggest promising approaches for using remote sensing tools with a broad spectral range to discriminate polar bears and other mammals from clean snow. Further data from live animals in their natural environment are needed to develop functions to discriminate among species of mammals and to determine whether other environmental elements may have similar reflectance.

Quality Control Assessment of the Mission Airborne Carbon 13 (MAC-13) Hyperspectral Imagery from Costa Rica

Abstract. A data quality assessment of airborne hyperspectral imagery (HSI) from Mission Airborne Carbon 2013 (MAC13) is presented. Because data quality is fundamentally important for modeling landscape biophysical characteristics from HSI, this article presents an assessment related to spectral alignment, spectroradiometric calibration, and geocorrection for 2,700 km2 of imagery acquired with the CASI-1500 and SASI-644 systems (375 nm – 2523 nm, 2.5 m resampled pixel size). MODIS, in-situ and image-based estimations of aerosol optical depth are compared for calculations of visibility for atmospheric correction. Information content (dimensionality) across the 5 ecosystems and 2 developed areas are also compared to illustrate the benefit of the extensive spectral resolution of the data. New approaches to the offset corrections of the imagery improved the accuracy of the calibrated results (radiance and reflectance). Assessment of visibility values applied to the atmospheric correction adduced that apparent reflectance computed using in-scene modeled visibility produced the most similar results to ground spectra. Dimensionality analysis revealed increased information content for all ecosystems when both sensors were considered. While not every HSI issue can be completely compensated for, an appreciation of common artifacts allows users to make more informed decision about their impact on planned analysis.

Enhancement of magnetic data by logarithmic transformation

A common problem encountered when displaying magnetic data as a color image is that small amplitude variations, which may have geologic significance, might be lost due to the large dynamic range of the whole data set. The human eye is capable of distinguishing only a limited range of variations within the color spectrum. Enhancement of the low-amplitude features is usually achieved through some type of image transform.

A network extraction tool for mineral exploration: a case study from the Wopmay Orogen, Northwest Territories, Canada

Many mineral exploration initiatives target regional- and local-scale lineaments (e.g. fault systems and dyke swarms) as they may act as conduits for mineralized fluids. In this work, we apply an automatic lineament ‘network extraction’ method that draws on similar processes as the Blakely-Simpson peak detection algorithm and a stream network extraction algorithm commonly used in the mapping of drainage patterns from a topographic surface (e.g. DEM, DTM) within a Geographic Information System (GIS) environment. We apply the network extraction algorithm to a magnetic surface (grid) rather than a topographic surface. The method uses a simple quadratic surface across a 3 × 3 window to determine the degree of surface slope and if the centre cell of the window represents a localised low point in the surface. Thus this routine is particularly effective at identifying magnetic lows that may represent faults, which have undergone magnetite depletion (e.g. hematization). These lineament solutions provide insight into mineral exploration vectors through the computation of rose diagrams, fracture density plots and intersection locations. These diagrams, plots, and locations are used in conjunction with other geophysical layers (e.g. radiometrics) to help identify potential mineral exploration targets. We successfully applied this algorithm to an aeromagnetic dataset from the Wopmay Orogen in Northwestern Canada. This area is characterised by extensive regional and localised fault systems and dyke swarms, along with promising polymetallic hydrothermal mineral occurrences. Key areas for follow up exploration are identified through a combined study of geophysical grids and lineament analysis. Network extraction is a routine applied to magnetic data to identify lineaments (e.g. faults and dykes). When these extracted lineaments are used in conjunction with existing geophysical and geological data, vectors for mineral potential may be identified. Network extraction was successfully applied to an aeromagnetic data set from Northwestern Canada.

The Correlation Coefficient as a Simple Tool for the Localization of Errors in Spectroscopic Imaging Data

The correlation coefficient (CC) was substantiated as a simple, yet robust statistical tool in the quality assessment of hyperspectral imaging (HSI) data. The sensitivity of the metric was also characterized with respect to artificially-induced errors. The CC was found to be sensitive to spectral shifts and single feature modifications in hyperspectral ground data despite the high, artificially-induced, signal-to-noise ratio (SNR) of 100:1. The study evaluated eight airborne hyperspectral images that varied in acquisition spectrometer, acquisition date and processing methodology. For each image, we identified a uniform ground target region of interest (ROI) that was comprised of a single asphalt road pixel from each column within the sensor field-of-view (FOV). A CC was calculated between the spectra from each of the pixels in the ROI and the data from the center pixel. Potential errors were located by reductions in the CCs below a designated threshold, which was derived from the results of the sensitivity tests. The spectral range associated with each error was established using a windowing technique where the CCs were recalculated after removing the spectral data within various windows. Errors were isolated in the spectral window that removed the previously-identified reductions in the CCs. Finer errors were detected by calculating the CCs across the ROI in the spectral range surrounding various atmospheric absorption features. Despite only observing deviations in the CCs from the 3rd–6th decimal places, non-trivial errors were detected in the imagery. An error was detected within a single band of the shortwave infrared imagery. Errors were also observed throughout the visible-near-infrared imagery, especially in the blue end. With this methodology, it was possible to immediately gauge the spectral consistency of the HSI data across the FOV. Consequently, the effectiveness of various processing methodologies and the spectral consistency of the imaging spectrometers themselves could be studied. Overall, the research highlights the utility of the CC as a simple, low monetary cost, analytical tool for the localization of errors in spectroscopic imaging data.

Nitrous oxide, methane and carbon dioxide dynamics from experimental pig graves.

Twelve pig carcasses were buried in single, shallow and deep (30 and 90 cm, respectively) graves at an experimental site near Ottawa, Ontario, Canada, with three shallow and three deep wrapped in black plastic garbage bags. An additional six carcasses were left at the surface to decompose, three of which were bagged. Six reference pits without remains were also dug. The objective of this three-year study was to examine the biogeochemistry and utility of nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) in grave detection and whether grave depth or cadaver condition (bagged versus bare) affected soil pore air concentrations and emission of the three gases. Graves showed significantly higher (α=0.05) concentrations and surface fluxes of N2O and CO2 than reference pits, but there was no difference in CH4 between graves and reference pits. While CH4 decreased with depth in the soil profiles, N2O and CO2 showed a large increase compared to reference pits. Shallow graves showed significantly higher emissions and pore air concentrations of N2O and CO2 than deep graves, as did bare versus bagged carcasses.

A simple adaptable data fusion methodology for geophysical exploration

We present a simple and adaptive method of data fusion using grey-scale grids for general geophysical exploration. The methodology relies upon: (1) understanding the physical property variations that might be associated with the mineral exploration target, and (2) applying appropriate (forward or inverse) grey-scaling to each input dataset so that before addition of the grids the anomalous patterns all express the phenomena of interest in the same sense (i.e. all positive anomalies). If the resulting fused dataset has a Gaussian population distribution then a linear grey-scale is applied to the data within the 95% (2σ) confidence interval; if it is non-Gaussian then the linear grey scale is applied to the entire dataset. The methodology has been applied to very low frequency (VLF), aeromagnetic and radiometric data measured during the 1980s over the Hemlo disseminated lode-gold deposit. The resulting fused data derived from our methodology produces a coherent region of anomalous geophysical response that is coincident in location and geometry to the surficial extent of the known mineralized zone of the deposit. Integration of multi-sensor response has the added advantage of significantly reducing the number of false-targets. Further, this method also illustrates the continued benefits that can be obtained from re-evaluation of older data. We present a simple grid/image-based method of data fusion for use with georectified geophysical data. In this study, we fuse aeromagnetic, radiometric and VLF data acquired over the Hemlo gold deposit of Northern Ontario, Canada. The results clearly show a strong and unique anomaly within the area of surficial mineralization.