Advancing archaeological geophysics
- 出版日期：2008.
- 页数：1 online resource.
- 第一责任说明：Eileen G. Ernenwein.
- 分类号：a483
- ISBN：9780549964209(e-book) :
MARC全文
62h0063985 20140610103003.0 cr an |||||||| 140610s2008 a fsbm |000|0 eng | AAI3341211 9780549964209(e-book) : CNY416.00 NGL eng NGL a483 Ernenwein, Eileen G. Advancing archaeological geophysics [electronic resource] : interpreting the archaeological landscape, ground-penetrating radar data processing, and multi-sensor fusion / Eileen G. Ernenwein. 2008. 1 online resource. Thesis (Ph.D.)--University of Arkansas, 2008. The human past has been the subject of scientific inquiry for centuries, and has long been approached by the study of material remains from traditional archaeological excavations. In recent decades the advancing fields of geophysics and geographic information systems have greatly improved the archaeological toolkit, and research to improve these methods is ongoing. This dissertation focuses on important aspects of geophysical survey as an approach to landscape-scale archaeology, each presented as stand-alone scientific papers that utilize a 1.2 hectare four-dimensional (ground-penetrating radar, magnetometry, magnetic susceptibility, and conductivity) dataset collected at Pueblo Escondido, a large prehistoric village of the Mogollon culture in southern New Mexico. Chapter 2 presents a case study showing the benefits of multidimensional geophysical surveys over large areas at archaeological sites. When paired with traditional archaeological excavations, it is possible to interpret the archaeological landscape on a much broader scale than is possible using excavations alone. At Pueblo Escondido, this approach led to a revised understanding of the architectural remains with broad regional significance. Chapter 3 describes new problems related to GPR surveys over large areas or extended periods of time, including issues related to correcting trace misalignments, edge discontinuities, and striping. Data processing solutions are offered. Chapter 4 presents an exploration of image classification methods for integrating multiple geophysical datasets. Unsupervised classification utilizing K-means cluster analysis and supervised classification using Mahalanobis Distance are described. The latter yielded a predictive model of archaeological features and identified some features that were not easily identified in the original datasets. Archaeology. ; Prospecting ; Archaeological geology. Geophysical methods. University of Arkansas. http://pqdt.calis.edu.cn/detail.aspx?id=x6j5mX0VrNs%3d 010001 Bs1906 rCNY416.00 ; h1 bs1406