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.

中文翻译：

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一种结合机载电磁学和岩土工程数据的基于机器学习的敏感冰川粘土区域尺度绘图方法

敏感的冰川粘土，通常被称为“快速粘土”，通常出现在高纬度的许多国家，北纬地区经常会发生破坏性的山体滑坡。快速粘土的盐含量与其剪切强度和电阻率密切相关。因此，它可以使用电磁方法比传统的侵入式方法更有效地进行映射，后者通常速度慢且成本高。然而，快速粘土的电阻率特征是非唯一的，导致对地球物理模型的解释含糊不清、不精确。在这项研究中，我们提出了一种使用机载电磁学预测快速粘土概率的改进方法。使用机器学习算法，我们将地球物理模型与岩土工程数据相结合，以解决其非唯一电阻率特征的问题。除了电阻率值，机器学习算法使用电阻率和空间属性的空间导数。我们使用从挪威中部道路建设项目收集的数据评估该方法的性能。结果表明，该方法能够在 14.8 公里的区域内使用少至 10 个钻孔对快速粘土发生做出合理而准确的预测²，并且它优于仅基于电阻率区间的简单解释。除了“最佳猜测”分类分类外，这些算法还输出概率估计，我们证明它们是不确定性的可靠指示。随着更多岩土工程数据被纳入训练数据，这些预测的准确性也趋于提高，这有助于弥补机载电磁数据的有限分辨率。鉴于该试验场粘土的岩石物理性质与其他地区的观测结果一致，我们预计该方法有可能通过提供有价值的早期洞察来提高快速粘土危险绘图的效率，从而节省大量时间和成本。基础设施规划和区域灾害测绘。