Browsing by Author "Naprstek, Tomas"

Now showing 1 - 4 of 4

Convolutional neural networks applied to the interpretation of lineaments in aeromagnetic data
(2022-01-01) Naprstek, Tomas; Smith, Richard S.
Parameter estimation in aeromagnetics is an important tool for geologic interpretation. Due to aeromagnetic data being highly prevalent around the world, it can often be used to assist in understanding the geology of an area as a whole or for locating potential areas of further investigation for mineral exploration. Methods that automatically provide information such as the location and depth to the source of anomalies are useful to the interpretation, particularly in areas where a large number of anomalies exist. Unfortunately, many current methods rely on high-order derivatives and are therefore susceptible to noise in the data. Convolutional neural networks (CNNs) are a subset of machine-learning methods that are well-suited to image processing tasks, and they have been shown to be effective at interpreting other geophysical data, such as seismic sections. Following several similar successful approaches, we have developed a CNN methodology for estimating the location and depth of lineament-type anomalies in aeromagnetic maps. To train the CNN model, we used a synthetic aeromagnetic data modeler to vary the relevant physical parameters, and we developed a representative data set of approximately 1.4 million images. These were then used for training classification CNNs, with each class representing a small range of depth values. We first applied the model to a series of difficult synthetic data sets with varying amounts of noise, comparing the results against the tilt-depth method. We then applied the CNN model to a data set from northeastern Ontario, Canada, that contained a dike with known depth that was correctly estimated. This method is shown to be robust to noise, and it can easily be applied to new data sets using the trained model, which has been made publicly available.
Modelling radio imaging method data using electric dipoles in a homogenous whole space
(Laurentian University of Sudbury, 2014-12-17) Naprstek, Tomas
Information as to how the signal of the Radio Imaging Method (RIM) changes when the system parameters change or the rock properties change is not well documented. Having a better understanding of the impact of these changes would assist in the design of surveys and the interpretations of RIM data. To quantify the impacts, a modelling program was created by representing the transmitter as an electric dipole. It outputs the amplitude and phase of the electric field in a homogeneous whole space. The system parameters were varied to investigate their effects on the measured signal. It was found that increasing the conductivity or the magnetic permeability resulted in amplitude attenuation and sharper anomalies, while increasing the dielectric permittivity resulted in increased amplitude and broader anomalies. A case study was performed using data from Drury Township, near Sudbury, Ontario, Canada. The mostly homogeneous section of field data was fit with synthetic data whose conductivity values ranged in the 10-3 S/m magnitude. A better fit was found using a conductivity of 3*10-4 S/m, by increasing the relative dielectric permittivity from 1 to between 18 and 20. It was concluded that the program was effective at fitting homogeneous sections of field data, and was developed into RIM forward model software for easy use.
A new method for interpolating linear features in aeromagnetic data
(Society of Exploration Geophysicists, 2019-03-13) Smith, Richard S.; Naprstek, Tomas
When aeromagnetic data are interpolated to make a gridded image, thin linear features can result in “boudinage” or “string of beads” artifacts if the anomalies are at acute angles to the traverse lines. These artifacts are due to the undersampling of these types of features across the flight lines, making it difficult for most interpolation methods to effectively maintain the linear nature of the features without user guidance. The magnetic responses of dikes and dike swarms are typical examples of the type of geologic feature that can cause these artifacts; thus, these features are often difficult to interpret. Many interpretation methods use various enhancements of the gridded data, such as horizontal or vertical derivatives, and these artifacts are often exacerbated by the processing. Therefore, interpolation methods that are free of these artifacts are necessary for advanced interpretation and analysis of thin, linear features. We have developed a new interpolation method that iteratively enhances linear trends across flight lines, ensuring that linear features are evident on the interpolated grid. Using a Taylor derivative expansion and structure tensors allows the method to continually analyze and interpolate data along anisotropic trends, while honoring the original flight line data. We applied this method to synthetic data and field data, which both show improvement over standard bidirectional gridding, minimum curvature, and kriging methods for interpolating thin, linear features at acute angles to the flight lines. These improved results are also apparent in the vertical derivative enhancement of field data. The source code for this method has been made publicly available.
New methods for the interpolation and interpretation of lineaments in aeromagnetic data
(2020-06-16) Naprstek, Tomas
Aeromagnetic data is one of the most widely collected types of geophysical data. In mineral exploration it can assist in mapping geological features, as well as indicate potential locations of economic interest. Due to the method in which aeromagnetic surveys are flown, an interpolation process must be completed before any map-based interpretation can be accomplished. One artifact common to many interpolation methods is that of “beading”, which is a discontinuous sequence of circular magnetic features that are at acute angles to the traverses, often caused by thin, linear geologic features such as dykes. Developing an interpolation method that “trends” or images these beads as continuous features on magnetic images would allow automatic and reliable quantitative methods to be used for interpretation by geologists and geophysicists. First, a new interpolation method was developed for aeromagnetic data. Utilizing a Taylor derivative expansion and structure tensors, it iteratively enhances trends evident across flight lines to manifest as linear features on the interpolated grid. When applied to both synthetic data and field data, the new method showed improvement over standard bidirectional gridding, minimum curvature, and kriging methods for interpolating thin, linear features at acute angles to the flight lines .Following this, a machine-learning interpolation approach was developed for aeromagnetic data using support vector machines and random forests. By using multiple standard interpolation methods as input to the machine-learning models, a filter-like approach was developed. These models could produce aeromagnetic maps that were overall more accurate than any single interpolation method, but not as effective as the Taylor derivative expansion method on lineament features. Finally, convolution neural networks were applied to estimate the source parameters characterizing lineament anomalies. A synthetic aeromagnetic data modeler was used to vary relevant physical parameters, and a representative dataset of approximately 1.4 million images was developed. These were then used for training convolution neural networks to estimate the strike and depth of sources. Applying the trained networks to a real-world dataset that was interpolated by the Taylor derivative expansion method, they located a dyke and estimated a depth consistent with a previous borehole investigation.