Putting it all together: Geophysical data integration

Kvamme, Kenneth L.; Ernenwein, Eileen G.; Menzer, Jeremy

doi:10.1016/b978-0-12-812429-1.00009-x

Cited by 12 publications

(8 citation statements)

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“…Researchers wish to generate a more complete picture of what lies beneath the ground and combining multiple methods that each reveal different subsurface features is the best way to do so. Recent interest in methods of data integration and fusion capitalize on such composite information to generate imagery and compound data that improves understanding of the content and structure of the subsurface (Böniger & Tronicke, ; Campana & Piro, ; Kvamme, Ernenwein, & Menzer, ).…”

Section: Introductionmentioning

confidence: 99%

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Geophysical correlation: global versus local perspectives

Kvamme¹

2018

Archaeological Prospection

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Robust anomalies that point to the same buried features frequently occur in diverse data sets from multi‐method surveys [e.g. ground penetrating radar (GPR), magnetic gradiometry, electrical resistivity]. Relationships between such corresponding anomalies traditionally have been noted subjectively, through visual comparisons of mappings or overlays of geophysical results. These circumstances create a paradox because theory and correlational studies generally suggest largely independent dimensions, while mappings of geophysical results often illustrate parallel anomalies which suggest that robust correlations should exist. Through application of local Pearson's r in small neighbourhoods, with radii ranging from 0.71 to 1.6 m, a means is offered, and demonstrated, to quantify and map local correlations between two geophysical data sets. A case study examines relationships between apparent electrical resistivity, a GPR depth‐slice, and magnetic susceptibility data acquired at Army City, a Great War troop support town in Kansas, now obliterated except for subsurface remains. Intricate spatial patterns of positive and negative correlations between these modalities are illustrated that vary in complexity with neighbourhood size. Neighbourhoods of moderate size seem preferable because the spatial patterning of correlations is enhanced locally and region‐wide. With focus given to positive correlations that point to parallel anomalies, overlays of high correlation frequently correspond with robust anomalies observed in each data set and offer objective criteria for assessments of correlation. However, it is also demonstrated that mutually robust anomalies do not necessarily exhibit high correlation and that high correlations frequently exist in regions devoid of apparent anomalies. The issue of assessing jointly parallel correlations between three or more geophysical data sets is also confronted using a form of local principal components analysis. It is shown that high eigenvalues associated with first principal components that exhibit loadings in the same direction point to zones of mutually high parallel correlation.

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Section: Introductionmentioning

confidence: 99%

“…Recent interest in methods of data integration and fusion capitalize on such composite information to generate imagery and compound data that improves understanding of the content and structure of the subsurface (Böniger & Tronicke, 2010;Campana & Piro, 2009;Kvamme, Ernenwein, & Menzer, 2018).…”

mentioning

confidence: 99%

Geophysical correlation: global versus local perspectives

Kvamme¹

2018

Archaeological Prospection

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“…With developments in instrumentation and hardware, as well as algorithms and software, new paths have opened before the discipline, fostering the application of arrays of geophysical methods, usually combined with aerial photographs, digital terrain models (DTM), Lidar data, etc. (e.g., Piro et al, 2000;Kvamme, 2006Kvamme, , 2007Sarris et al, 2013;Karamitrou et al, 2017;Kvamme et al, 2019;Li et al, 2019). The growing amount of information on the subsurface setting provided by mapping the distribution of various physical parameters has prompted the development of algorithms combining and interpreting jointly the outcomes of various methods.…”

Section: Introductionmentioning

confidence: 99%

Joint Interpretation of Various Geophysical Data by Means of Image Fusion in Philippi in Northern Greece

Karamitrou¹,

Tsokas²,

Kaimaris³

et al. 2021

archeosciences

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“…Nevertheless, even in these cases the analysis is performed by processing separately the individual datasets and the person doing the interpretation typically compares the products of this analysis, i.e. the geophysical images by visual inspection, with few yet notable exceptions (Ernenwein & Kvamme, ; Kvamme, ; Kvamme et al, , ).…”

Section: Introductionmentioning

confidence: 99%

Fusion of geophysical images in the study of archaeological sites

Karamitrou

Bogiatzis

Τσοκασ

2020

Archaeological Prospection

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In the last few years, the idea of combining images, called image fusion, appeared and it has become a critical area of research and development. Image fusion can be defined as the process of combining images, taken from the same scene and create one single image containing all the essential information of the original images. A single sensor is not always sufficient. Different sensors, effective in different environmental conditions, provide different information of a scene. The underlying idea in this article is to combine geophysical images taken with different sensors, from the same location, aiming to improve the detectability of possible archaeological targets.Three different fusion approaches were used; fusion by calculating the average of the individual images, and through the use of wavelet and curvelet transforms. Furthermore, taking advantage of the curvelet domain we exploit possible prior angle information to enhance the angles where the remnants are expected. We applied the methods in seven different pairs of geophysical images taken from two different archaeological areas. In all cases the fused images produced significantly better results than each of the original geophysical images separately.

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Putting it all together: Geophysical data integration

Cited by 12 publications

References 88 publications

Geophysical correlation: global versus local perspectives

Geophysical correlation: global versus local perspectives

Joint Interpretation of Various Geophysical Data by Means of Image Fusion in Philippi in Northern Greece

Fusion of geophysical images in the study of archaeological sites

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