用于规划、处理、可视化和评估考古资源海洋磁测量数据的地理空间测量工具

J. Bright
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摘要

海洋磁测量是考古学家发现、记录和描述铁磁水下文化资源(SCR)的常用工具。然而,很难用实际探测阈值来量化某一磁场调查的效果,因此很难准确地评估在某一地区是否存在考古遗迹。同样,调查计划方法和数据可视化技术同样具有挑战性,难以定量地接近。为了解决这些问题,海洋能源管理局可再生能源项目办公室与国家公园管理局水下资源中心合作,开展了一项实地研究计划,对已知的铁磁考古遗址进行了磁采样,以更好地了解它们各自的检测阈值。结合这些测试的结果,该小组在ArcGIS中开发了一系列定制的地理空间处理工具,以协助量化规划、处理和描述海洋磁测量的过程。现场测试工作在比斯坎国家公园进行,包括在已知的各种年代、大小和材料的铁磁考古物体周围执行预先确定的磁调查采样模式。然后,对获取的数据进行处理,得出物体磁矩的具体值,磁矩是量化感应磁场强度所需的主要变量,因此,也就是给定物体的空间检测阈值。这些数据依次用于改进感应磁场模型,然后将其纳入磁测量规划工具,以及用Python编写的地理空间处理方法,以实现磁测量数据的自动化集成、可视化、过滤和采集后评估。采样的SCR包括现代时期的钢壳船只,散布着大量铁制文物的碎片区,铁大炮和炮弹,历史上的锚,以及带有铁部件的木制帆船。测试地点的多样性包含了一系列通常在海洋环境中发现的考古材料。这些信息产生了对每种材料和场地类型的相对磁场强度的见解,允许根据给定调查期间目标物体的预期可探测性进一步完善感应磁场强度模型。开发了四个Python脚本,包括输入工具、生成调查边界工具、可视化工具和置信度建模工具。这些脚本共同组成了Magnetometer Survey V.1.0工具箱,通过ArcToolbox集成到ArcGIS中。一旦从数据采集程序中输出海洋磁测量数据,这些Python脚本将自动执行剩余的数据处理,并根据用户定义的参数促进定量QA/QC评估。因此,考古资源的海洋磁调查可以根据可重复和一致的程序进行规划、执行、处理和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Geospatial Survey Tools for Planning, Processing, Visualizing, and Assessing Marine Magnetic Survey Data for Archeological Resources
Marine magnetic surveys are a commonplace tool for archaeologists to discover, document, and characterize ferromagnetic submerged cultural resources (SCR). It is difficult, however, to quantify the efficacy of a given magnetic survey in terms of actual detection thresholds and, therefore, accurately assess the presence or absence of archaeological remains throughout a given area. Similarly, survey planning methods and data visualization techniques are likewise challenging to approach quantitatively. To address these issues, the Bureau of Ocean Energy Management's Office of Renewable Energy Programs partnered with the National Park Service's Submerged Resources Center to conduct a field research program whereby known ferromagnetic archaeological sites were magnetically sampled to better understand their respective detection thresholds. Incorporating the results of these tests, the team developed a series of custom geospatial processing tools in ArcGIS to assist in quantifying the process of planning, processing, and describing marine magnetic surveys. Field testing operations, which took place in Biscayne National Park, involved executing pre-determined magnetic survey sampling patterns around known ferromagnetic archaeological objects of various vintage, size, and materials. Acquired data was then processed to yield specific values for the object's magnetic moment, the primary variable needed to quantify induced magnetic field strength and, therefore, a given object's spatial threshold of detection. These were, in turn, used to refine induced magnetic field models subsequently incorporated into magnetic survey planning tools, as well as geospatial processing methods scripted in Python to automate magnetic survey data integration, visualization, filtering, and post-acquisition assessment. Sampled SCR included modern period steel-hulled vessels, diffused debris fields containing numerous scatters of iron artifacts, iron cannon and shot, historic anchors, and wooden sailing vessels with iron components. This diversity of test sites encompassed an array of archaeological materials typically found in a marine environment. Information yielded insights into the relative magnetic field strength of each these materials and site types, allowing models of induced magnetic field strength to be further refined in terms of a targeted object's anticipated detectability during a given survey. Four Python scripts were developed, including an Input tool, Generate Survey Boundary tool, Visualization tool, and Confidence Modeling tool. Collectively these scripts comprise the Magnetometer Survey V.1.0 toolbox, which integrates into ArcGIS via ArcToolbox. Once marine magnetic survey data is output from a data acquisition program, these Python scripts automate the remaining data processing and facilitate a quantitative QA/QC assessment based on user-defined parameters. As a result, marine magnetic surveys for archaeological resources can planned, executed, processed, and assessed according to a repeatable and consistent procedure.
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