Abstract
Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN) to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other), accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found between till veneer and till blanket as well as glaciofluvial subclasses (esker, terraced, and hummocky ice-contact). Merging these classes respectively increased accuracy for independent test area to 68% on average. Relative to the more widely used Random Forest machine learning algorithm, this represents an improvement in accuracy of 4%. Furthermore, the CNN produced better results for less frequent classes with distinct spatial structure.
Highlights
Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada
We evaluate convolution neural networks (CNN) as means to improve surficial materials Remote Predictive Mapping (RPM) for the case where a model is trained and applied in the same spatial domain and where it is trained from one area and applied in another
CNNs are an interesting advancement in machine learning combining spectral and spatial properties, feature optimization for the specific classification task, and the ability to adapt pre-trained models to new tasks
Summary
Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Mapping large remote regions can be a major challenge requiring significant labour and cost for timely production. With access to remotely sensed imagery, new machine learning approaches are emerging that support the surficial geological mapping of vast northern regions appropriate for regional scale mineral exploration and related land-use management. This is relevant in Northern Canada, which is a huge territory that cannot be mapped following a systematic field sampling approach [1]. Interpretation is subjective, labour-intensive and difficult to repeat, while expert knowledge can be challenging to maintain and transfer [2]. Fieldwork in remote regions is costly and logistically challenging
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.