Applying Machine Learning to identify Geological structures from AEM data in Sweden Mats Lundh Gulbrandsen1*, Torben Bach1, Peter Dahlqvist2, Lena Persson2, Mehrdad Bastani2 1 I•GIS, Voldbjergvej 14 A, 8240, Risskov, DK, 2 Geological Survey of Sweden, Villavägen 18, Uppsala, Sweden *
[email protected]
Airborne Electromagnetic (AEM) data has during the past decade proven to be very valuable to hydrogeological modeling. As a result, larger and larger AEM surveys are acquired, making objective and effective manual modeling, considering all available data, practically impossible. The amount and nature of the AEM data makes it however very suitable for different machine learning techniques. We have developed tools based on machine learning methods such as clustering as statistical supervised regression allowing Geologists to make objective hydrogeological layers models in complex geological environments. The methods implemented in our geological modelling software, GeoScene3D, allow modelling that include all available geophysical data and at the same time treasure geological expert knowledge.
Figure 3: An overview of the data. Left: Resistivity surface in the uppermost 2 meters, and the red triangles represent well data. The flight-line highlighted in magenta is shown in the lower panel to the right, and a 3D view of cross sections together with wells are seen in the upper panel to the right.
Figure 4: Upper: A resistivity cross section (see Fig. 3). Lower: An interpreted geological layer model of the overall geological strucers.
Figure 1: Schematic overview of the Smart Interpretation method
In this study, two different approaches using Smart Interpretation on the Gotland data is tested. Workflow Case 1: Step 1) Information about the base of the top soil layer is extraction form well data. Step 2) These points are used as input points in the SI method. Step 3) Interpolate the top soil surface.
Figure 5: Results of case 1: The top soil interpreted using Smart Interpretation (left) compared to the manual model (right). The uncertainty on the SI model is in the middle. The SI model is made using 456 well points, where as the manual model is created based on 2084 points.
Workflow Case 2: Step 1) Cluster the AEM models (a preprocessor stabilizing SI) Step 2) Manually interpret the layer within each cluster Step 3) Use these as input to SI Step 4) Interpolate the base of marl surface. Figure 6: Results of case 2: Left: The Result of applying clustering to the AEM models. Right: The base of marl interpreted using Smart Interpretation (left) compared to the manual model (right). The uncertainty on the SI model is in the middle. The SI model is made using 73 manual points, where as the manual model is created based on 2557 points.
Figure 2: Study atea on Gotland.
This study shows two case studies using Smart Interpretation to map two different boundary layers on Gotland. The modelling has been done in the geological modelling software GeoScene3D, and this case study show that the new Machine Learning tools implemented in the software allow efficient and objective models that both are consistent with all available data and at the same time treasure geological knowledge.
