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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada.

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  • 1Alberta Geological Survey, Alberta Energy Regulator, Edmonton, Alberta, Canada.

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Machine learning accurately maps sediment thickness (depth to bedrock) using advanced spatial techniques. This approach overcomes limitations of traditional methods in varied terrain, improving geological and environmental predictions.

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Area of Science:

  • Geoscience
  • Machine Learning
  • Spatial Analysis

Background:

  • Depth to bedrock (DTB) maps are crucial for hydrogeology, engineering, and resource management.
  • Traditional DTB estimation methods struggle with accuracy in complex, varied topography.
  • Accurate DTB mapping is essential for numerous practical applications.

Purpose of the Study:

  • To develop and evaluate a machine learning approach for predicting DTB across Alberta, Canada.
  • To improve the accuracy and reliability of DTB mapping, especially in challenging terrains.
  • To compare the performance of machine learning with traditional spatial interpolation methods.

Main Methods:

  • Utilized borehole litholog data to train a natural language model, expanding the dataset.
  • Employed machine learning algorithms (XGBoost, Random Forests, Cubist) with spatial feature engineering.
  • Incorporated geographic coordinates, proximity measures, and spatially lagged DTB estimates.

Main Results:

  • Machine learning models incorporating spatially lagged variables significantly outperformed those using auxiliary predictors or coordinates alone.
  • The proposed method demonstrated reliable DTB predictions across diverse physiographic regions and at a provincial scale.
  • Spatially lagged variables improved predictive performance without generating spurious spatial artifacts.

Conclusions:

  • The developed machine learning approach offers a reliable and broadly suitable method for DTB mapping.
  • This technique enhances the accuracy of sediment thickness estimations in areas with complex terrain.
  • The findings have significant implications for geoscience studies and applied fields requiring DTB data.