Recently there has been an increase of work dedicated to developing a more objective soil provenancing capability. Notwithstanding the significant progress made, the presented provenancing techniques have predominately been based upon interpolation grids, generated from often arbitrary decisions of the user (e.g., grid cell size, grid placement, interpolation model, etc.). To address the acknowledged reproducibility issues, this paper introduces a spatial modelling technique based upon Voronoi Tessellations that is free from arbitrary user decisions. Termed herein as Voronoi Natural Neighbours Tessellation (VNNT), the proposed approach segments the survey area into many “honeycomb-like” polygons. Of which, the exact number, shape, location, and orientation of polygons are inherently dependent upon the original density of input sampling points from the survey, not a user’s subjective decision.

Utilising compositional geochemistry data from a fit-for-purpose topsoil survey and eleven “blind” soil samples from Canberra, Australia, we compare this proposed VNNT approach against a simpler Voronoi Tessellation, and a previously presented 500 m × 500 m grid following a modified and upscaled Natural Neighbour interpolation. Aside from also being computationally less intensive, our results indicated the proposed VNNT approach regularly yielded at least equal, or often more accurate provenance predictions than that of the gridded Natural Neighbour interpolation. Importantly, the delineation of individual polygons is fundamentally dependent upon the survey’s real sampling design, and most truthfully reflects the underlying sampling density, and associated uncertainties. Consequently, the VNNT approach is significantly less susceptible to expert bias as a result of subjective decision-making and “fine-tuning” of interpolation parameters.

最近，致力于开发更客观的土壤来源能力的工作有所增加。尽管取得了显着的进步，所提出的出处技术主要基于插值网格，插值网格是根据用户的任意决定（例如，网格单元大小、网格放置、插值模型等）生成的。为了解决公认的再现性问题，本文引入了一种基于 Voronoi 曲面细分的空间建模技术，该技术不受用户任意决策的影响。所提出的方法在本文中被称为Voronoi自然邻域细分（VNNT），将勘测区域分割成许多“蜂窝状”多边形。其中，确切的数量、形状、位置、

利用来自适合目的的表土调查的成分地球化学数据和来自澳大利亚堪培拉的 11 个“盲”土壤样本，我们将所提出的 VNNT 方法与更简单的 Voronoi 曲面细分以及之前提出的经过修改的 500 m × 500 m 网格进行了比较和升级的自然邻点插值。除了计算强度较低之外，我们的结果表明，所提出的 VNNT 方法通常会产生至少与网格自然邻点插值相同或通常更准确的出处预测。重要的是，各个多边形的描绘从根本上取决于调查的真实抽样设计，并且最真实地反映了潜在的抽样密度和相关的不确定性。最后，