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Geological maps are essential for various Earth science applications, but their creation often relies on conceptual or numerical models that extrapolate sparse field data. Traditional geostatistical techniques and machine learning methods have limitations in handling complex spatial relationships and auxiliary data.
The authors propose a new architecture called Spatially Constrained Bayesian Network (SCB-Net) that combines deep learning techniques with Bayesian inference to generate accurate lithological predictions while quantifying uncertainty.
The SCB-Net model consists of two parts: the first part focuses on extracting meaningful features from auxiliary data (e.g., satellite imagery, geophysical data), while the second part integrates the learned representations with sparse field observations to produce spatially constrained predictions.
The authors developed a custom loss function that addresses class imbalance and spatial smoothing issues, and they incorporated Monte Carlo dropout to estimate model uncertainty.
The SCB-Net was applied to two study areas in northern Quebec, Canada, demonstrating its ability to generate field-data-constrained lithological maps and assess prediction uncertainty for improved decision-making.
The results show that the use of spatial constraints significantly improved the accuracy of lithological predictions, particularly for rock types with similar physical properties that are difficult to distinguish using auxiliary data alone.
The authors also explored a transfer learning strategy, where a pre-trained model on the northeast area was fine-tuned for the northern area, leading to a 50% reduction in training time and a 3% increase in overall performance.
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by Victor Silva... às arxiv.org 04-01-2024
https://arxiv.org/pdf/2403.20195.pdfPerguntas Mais Profundas