Integration of different kinds of data is an important issue in archaeological prospection. However, the current methodological approaches are underdeveloped and rarely use the data to their maximum potential. Common approaches to integration in the geophysical sciences are mostly just various forms of comparison. We argue that true integration should involve the mathematical manipulation of input data such that the original values of the input data are changed, or that new variables are produced. To address this important research gap, we present an innovative approach to the analysis of geochemical and geophysical datasets in prospection-focused disciplines. Our approach, which we refer to as “multiscalar integration” to differentiate it from simpler methods, involves the application of mathematical methods and tools to process the data in a unified way. To demonstrate our approach, we focus on integrating geophysical data (magnetometry) with geochemical data (elemental content). Our approach comprises three main stages: Quantification of the data deviation from random distributions, linear modelling of geophysical and geochemical data and integration based on weighting of the different elements derived in previous steps. All the steps of the workflow can be also applied separately and independently as needed or preferred. Our approach is implemented in the R environment for statistical computing. All data, functions and scripts used in the work are available from open access repositories (Zenodo.org and Github.com) so that others can test, modify and apply our proposed methods to new cases and problems. Our approach has the following advantages: (1) It allows the rapid exploration of multiple data sources in an unified way; (2) it can increase the utility of geochemical data across diverse prospection disciplines; (3) it facilitates the identification of links between geochemical and geophysical data (or generally, between point-based and raster data); (4) it innovatively integrates various datasets by weighting the information provided by each; (5) it is simple to apply following a step-by-step framework; (6) the code and workflow is fully open to allow for customization, improvements and additions.

不同类型数据的整合是考古勘探中的一个重要问题。然而，当前的方法学方法尚不成熟，很少能最大限度地发挥数据的潜力。地球物理科学中常见的整合方法大多只是各种形式的比较。我们认为，真正的积分应该涉及对输入数据的数学操作，以便改变输入数据的原始值，或者产生新的变量。为了弥补这一重要的研究空白，我们提出了一种创新方法来分析以勘探为重点的学科中的地球化学和地球物理数据集。我们的方法被称为“多标量积分”，以区别于更简单的方法，涉及应用数学方法和工具以统一的方式处理数据。为了演示我们的方法，我们专注于将地球物理数据（磁力测量）与地球化学数据（元素含量）相结合。我们的方法包括三个主要阶段：对随机分布的数据偏差进行量化、地球物理和地球化学数据的线性建模以及基于先前步骤中得出的不同元素的权重进行整合。工作流程的所有步骤也可以根据需要或偏好单独且独立地应用。我们的方法是在 R 环境中实现的，用于统计计算。工作中使用的所有数据、函数和脚本都可以从开放访问存储库（Zenodo.org 和 Github.com）获取，以便其他人可以测试、修改我们提出的方法并将其应用于新案例和问题。 我们的方法具有以下优点：（1）它允许以统一的方式快速探索多个数据源； (2)它可以提高地球化学数据在不同勘探学科中的效用； (3) 它有助于识别地球化学和地球物理数据之间的联系（或者一般来说，基于点的数据和栅格数据之间的联系）； （4）通过对各个数据集提供的信息进行加权，创新性地整合了各个数据集； (5) 遵循分步框架，应用简单； (6) 代码和工作流程完全开放，允许定制、改进和添加。