A machine learning–based approach to regional‐scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data

ABSTRACT Sensitive glaciomarine clays, often referred to as ‘quick clay’, commonly occur in many countries at high, northerly latitudes, causing frequent and occasionally devastating landslides. The salt content of quick clay is strongly correlated to both its shear strength and electrical resistivity. Hence, it can be mapped using electromagnetic methods more efficiently than traditional intrusive methods, the latter of which can often be slow and costly. However, the resistivity signature of quick clay is non‐unique, leading to ambiguous, imprecise interpretations of geophysical models. In this study, we present an improved method for predicting the probability of quick clay using airborne electromagnetics. Using machine learning algorithms, we combine geophysical models with geotechnical data to address the issue of their non‐unique resistivity signature. Beyond resistivity values, the machine learning algorithms use spatial derivatives of resistivity and spatial attributes. We evaluate the performance of this method using data collected from a road construction project in central Norway. Results show that this method is able to make plausible and accurate predictions of quick clay occurrence using as few as 10 boreholes across an area of 14.8 km2, and that it outperforms a simple interpretation based on resistivity intervals alone. In addition to a ‘best guess’ categorical classification, these algorithms output probability estimates, and we demonstrate that they are a reliable indication of uncertainty. The accuracy of these predictions also tends to increase as more geotechnical data are included as training data, helping compensate for the limited resolution of the airborne electromagnetics data. Given that the petrophysics of the clays at this test site are consistent with observations in other regions, we expect this method has the potential to make quick clay hazard mapping more efficient by offering valuable early‐phase insights, leading to large time and cost savings for both infrastructure planning and regional hazard mapping.