The characterisation of the subsurface of a landslide is a critical step in developing ground models that inform planned mitigation measures, remediation works or future early-warning of instability. When a landslide failure may be imminent, the time pressures on producing such models may be great. Geoelectrical and seismic geophysical surveys are able to rapidly acquire volumetric data across large areas of the subsurface at the slope-scale. However, analysis of the individual model derived from each survey is typically undertaken in isolation, and a robust, accurate interpretation is highly dependent on the experience and skills of the operator. We demonstrate a machine learning process for constructing a rapid reconnaissance ground model, by integrating several sources of geophysical data in to a single ground model in a rapid and objective manner. Firstly, we use topographic data acquired by a UAV survey to co-locate three geophysical surveys of the Hollin Hill Landslide Observatory in the UK. The data are inverted using a joint 2D mesh, resulting in a set of co-located models of resistivity, P-wave velocity and S-wave velocity. Secondly, we analyse the relationships and trends present between the variables for each point in the mesh (resistivity, P-wave velocity, S-wave velocity, depth) to identify correlations. Thirdly, we use a Gaussian Mixture Model (GMM), a form of unsupervised machine learning, to classify the geophysical data into cluster groups with similar ranges and trends in measurements. The resulting model created from probabilistically assigning each subsurface point to a cluster group characterises the heterogeneity of landslide materials based on their geophysical properties, identifying the major subsurface discontinuities at the site. Finally, we compare the results of the cluster groups to intrusive borehole data, which show good agreement with the spatial variations in lithology. We demonstrate the applicability of integrated geophysical surveys coupled with simple unsupervised machine learning for producing rapid reconnaissance ground models in time-critical situations with minimal prior knowledge about the subsurface.

滑坡地下特征化是开发地面模型的关键步骤，该模型可为计划的缓解措施，修复工作或未来的不稳定预警提供信息。当滑坡破坏迫在眉睫时，制作此类模型的时间压力可能会很大。地电和地震地球物理勘测能够以坡度比例快速获取地下大面积区域的体积数据。但是，对每个调查得出的单个模型的分析通常是独立进行的，而可靠，准确的解释高度依赖于操作员的经验和技能。我们演示了用于构建快速侦察地面模型的机器学习过程，通过快速客观地将多个地球物理数据源集成到单个地面模型中。首先，我们使用通过无人机调查获得的地形数据来共同定位英国霍林山滑坡观测站的三个地球物理调查。使用联合2D网格对数据进行反转，从而得到一组位于同一位置的电阻率，P波速度和S波速度模型。其次，我们分析网格中每个点的变量之间的关系和趋势（电阻率，P波速度，S波速度，深度）以识别相关性。第三，我们使用高斯混合模型（GMM）（一种无监督机器学习的形式）将地球物理数据分类为具有相似范围和测量趋势的群集组。通过概率性地将每个地下点分配给一个聚类组而创建的结果模型可以根据滑坡物质的地球物理特性来表征滑坡物质的异质性，从而确定该地点的主要地下不连续性。最后，我们将聚类组的结果与侵入性井眼数据进行了比较，这些数据与岩性的空间变化具有很好的一致性。