The ground‐penetrating radar (GPR) method with the standard constant‐offset reflection mode allows to detect and map different types of archaeological structures, such as walls, foundations, floors and roads. The interpretation of the GPR data usually involves a detailed and time‐consuming analysis of large amounts of information, which entails nonnegligible chances of errors, especially under nonideal fieldwork conditions. The application of suitable automatic detection algorithms can be useful to more rapidly and successfully complete the interpretation task. In this work, we explore the use of supervised machine learning methodologies to automatically detect mud‐wall signatures in radargrams and to map the structures from these detections. Several algorithms, based on Viola–Jones cascade classifiers and the image feature descriptors Haar, histogram of oriented gradients and local binary patterns, were implemented. These algorithms were applied to datasets previously acquired in pre‐Inca and Inca‐Hispanic sites located in the Andean NW region of Argentina. The best algorithms provided very good detection rates for well‐preserved walls and acceptable rates for deteriorated walls, with a low number of spurious predictions. They also exhibited the ability to detect collapsed walls and fragments detached from them. These are remarkable results because mud walls are usually difficult to be detected by conventional analysis, owing to the complex and variable characteristics of their reflection patterns. The results of the automatic detection techniques were represented in plan views and three‐dimensional (3D) views that delineated in detail most of the structures of the sites. These algorithms are very fast, so applying them significantly reduces the interpretation times. In addition, once they have been trained using the patterns of one or several sites, they are directly applicable to other sites with similar characteristics.

中文翻译：

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自动检测探地雷达数据中的泥壁特征

具有标准恒定偏移反射模式的探地雷达（GPR）方法可以检测和绘制不同类型的考古结构，例如墙壁，地基，地板和道路。GPR数据的解释通常涉及对大量信息的详细且耗时的分析，这带来了不可忽略的错误机会，尤其是在非理想的野外工作条件下。合适的自动检测算法的应用对于更快，更成功地完成解释任务很有用。在这项工作中，我们探索使用有监督的机器学习方法来自动检测雷达图中的泥壁特征，并根据这些检测结果绘制结构图。基于Viola–Jones级联分类器和图像特征描述符Haar的几种算法，定向梯度和局部二进制模式的直方图已实现。这些算法已应用于先前在阿根廷安第斯西北地区的印加前和印加西班牙站点采集的数据集。最好的算法为保存完好的墙壁提供了非常好的检测率，为恶化的墙壁提供了可接受的检测率，并且虚假预测的数量很少。他们还具有检测倒塌的墙壁和从中脱落的碎片的能力。这些结果令人瞩目，因为泥浆壁的反射图案具有复杂而可变的特征，因此通常难以通过常规分析来检测。自动检测技术的结果以平面图和三维（3D）视图表示，这些视图详细描述了站点的大多数结构。这些算法非常快，因此应用它们可以大大减少解释时间。此外，一旦使用一个或多个站点的模式对它们进行了训练，它们就可以直接应用于具有类似特征的其他站点。