Predictive Geological Mapping with Convolution Neural Network Using Statistical Data Augmentation on a 3D Model

TL;DR

This paper presents a novel approach combining statistical data augmentation, 3D geological modeling, and a specialized CNN to produce preliminary geological maps from airborne magnetic data, enhancing mapping speed and objectivity.

Contribution

It introduces a data augmentation workflow using 3D models and advanced simulation techniques to train a CNN for geological mapping from magnetic data, enabling effective semi-supervised segmentation.

Findings

The CNN accurately segments surficial geology from magnetic data.

Attention maps effectively highlight geological structures at multiple scales.

Clustering of high-level features yields meaningful geological maps.

Abstract

Airborne magnetic data are commonly used to produce preliminary geological maps. Machine learning has the potential to partly fulfill this task rapidly and objectively, as geological mapping is comparable to a semantic segmentation problem. Because this method requires a high-quality dataset, we developed a data augmentation workflow that uses a 3D geological and magnetic susceptibility model as input. The workflow uses soft-constrained Multi-Point Statistics, to create many synthetic 3D geological models, and Sequential Gaussian Simulation algorithms, to populate the models with the appropriate magnetic distribution. Then, forward modeling is used to compute the airborne magnetic responses of the synthetic models, which are associated with their counterpart surficial lithologies. A Gated Shape Convolutional Neural Network algorithm was trained on a generated synthetic dataset to perform geological mapping of airborne magnetic data and detect lithological contacts. The algorithm also provides attention maps highlighting the structures at different scales, and clustering was applied to its high-level features to do a semi-supervised segmentation of the area. The validation conducted on a portion of the synthetic dataset and data from adjacent areas shows that the methodology is suitable to segment the surficial geology using airborne magnetic data. Especially, the clustering shows a good segmentation of the magnetic anomalies into a pertinent geological map. Moreover, the first attention map isolates the structures at low scales and shows a pertinent representation of the original data. Thus, our method can be used to produce preliminary geological maps of good quality and new representations of any area where a geological and petrophysical 3D model exists, or in areas sharing the same geological context, using airborne magnetic data only.