Determination of grave locations in Dedemezari Necropolis (Western Turkey) using magnetic field derivatives

In this paper, a processing chain is proposed for enhancing larger‐scale building structures in magnetometer data using the continuous wavelet transform for extracting the horizontal location, depth, and homogeneity (or shape) of subsurface objects. Even though the values estimated from our data do not clearly describe the archaeological site, they are used as a filter step for removing objects whose depth and homogeneity fall outside a predefined range. This yields a binary map, where valid points represent the horizontal source positions of magnetic anomalies. In a second step, curvilinear subsets of points are identified on a larger scale by the tensor voting framework indicating archaeological relevant structures, such as arcs and lines. Here, a point casts votes on neighbouring points and their values depend on the distance, and relative position of the points. All votes are summed up and the final value discriminates, whether a certain point (and therefore a magnetic source) is part of a salient curve (e.g. arc, line). Thresholding removes points with lower values while keeping those with larger values. Although the result shows many geometric features, it still needs to be combined with other data, e.g. from aerial photography or excavations. In combination, the extracted features continue those structures in the aerial photograph, thus expanding the knowledge about the archaeological site.

Section: Introductionmentioning

confidence: 99%

Enhancing signals from buried Roman structures in Magnetometer data by combining continuous wavelet transform and tensor voting

Jungmann

Dolenz

Clauser

et al. 2017

“…All the works on edge detection of buried structures can be considered as targeting towards the same direction (e.g. Hansen, 1995, 2000;Tabbagh et al, 1997;Godio and Piro, 2005;Buyuksarac et al, 2008;Milea et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Use of Edge Delineating Methods in Interpreting Magnetic Archaeological Prospection Data

Stampolidis

Τσοκασ

2012

Various edge delineating methods (edge mappers) are tested on different types of synthetic data produced by calculating the response of a model of buried ancient ruins. The model was constructed to resemble a commonly occurring situation with ruins of two historical phases buried one beneath the other. These weak signals of relatively small wavelengths of the archaeological prospection comprise tough tests for the mappers considered. Additionally, a particular mapper, the balanced horizontal gradient magnitude scaled by the analytic signal is introduced in this study. An example of applying the mappers to real data is also presented. All the mappers proved capable of locating the lateral limits of the subsurface targets but with varying success. Also some of them are very sensitive to noise, rendering them useless for practical application. Some others proved robust in the presence of noise. For a number of them, the varying strength of the aligned maximum values proves an indicator of the variation of the burial depth of the targets. Their merits and drawbacks are demonstrated and discussed with respect to their application on the various types of magnetic archaeological prospection data. Also, relative comparisons are presented. Copyright © 2012 John Wiley & Sons, Ltd.

“…Using the first‐order derivatives for the definition of the analytical signal, and also the higher order ones, we can derive three other functions (Thurston and Smith, ), which are the amplitude of the analytical signal, its local phase and its local wavenumber, given respectively by the equations

|A (x)| = \sqrt{{((), \frac{\partial M}{\partial x})}^{2} + {((), \frac{\partial M}{\partial z})}^{2}}

θ = {tan}^{- 1} [\frac{\partial M}{\partial z} / \frac{\partial M}{\partial x}]

and

k = \frac{1}{{|A (x)|}^{2}} (\frac{\partial^{2} M}{\partial x \partial z} \frac{\partial M}{\partial x} - \frac{\partial^{2} M}{\partial x^{2}} \frac{\partial M}{\partial z})

These functions are called complex attributes and have been used successfully in interpreting potential‐field data and they have been introduced to archaeological prospection (Tsokas and Hansen, , ; Tabbagh et al ., ; Godio and Piro, ; Buyuksarac et al ., ; Cheyney et al ., ). The particular geophysical and mathematical properties that made the method attractive for general geophysical prospecting have also proved very useful for archaeological exploration.…”

Section: Introductionmentioning

confidence: 99%

“…These functions are called complex attributes and have been used successfully in interpreting potential-field data and they have been introduced to archaeological prospection Hansen, 1995, 2000;Tabbagh et al, 1997;Godio and Piro, 2005;Buyuksarac et al, 2008;Cheyney et al, 2010). The particular geophysical and mathematical properties that made the method attractive for general geophysical prospecting have also proved very useful for archaeological exploration.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Effect of Instrumental and Discretization Errors on Derivative‐based Potential‐field Geophysical Methods

Milea

Papazachos

Tsokas

et al. 2012

Geophysical field measurements are subject to errors, which consequently comprise part of the data and subsequently they are propagated through processing techniques. Some of these errors can be controlled and eliminated, but others such as the errors introduced by the measuring instrument cannot be avoided. Moreover, the field data are collected as discrete samples, a procedure that introduces errors by itself. These two types of error are examined in the present study regarding their propagation and influence in estimating derivatives and subsequently functions of derivatives, such as the complex attributes. For this reason we consider magnetic anomalies (signals) of typical buried structures that are common in archaeological prospection, such as concealed ditches. In order to have analytical expressions for the magnetic anomaly signal, the structures are simulated by polygonal bodies. The magnetic anomaly signals are subsequently discretized and anomaly derivatives as well as complex attributes are computed using numerical methods. These discrete function values are compared with the corresponding values obtained from the analytical solutions in order to assess the discretization errors involved. Similarly, calculations are repeated after contaminating the signals with Gaussian noise in an attempt to simulate the effect of instrumental error. A parametric study of these two error sources is performed, showing that they have a contradictory effect: larger sampling intervals increase, as expected, the numerical divergence of ideal data (no noise) from their analytical form. On the other hand, for realistic data (with random noise) the increase of the measuring step decreases the effect of instrument error. The combined influence of both sources of errors shows that the various derivative‐based measures exhibit a minimum of the introduced total error for different combinations of discretization and measurement errors. The quantitative study of the combined error provides useful suggestions on the best way that magnetic surveys should be carried out when the use of derivatives is employed for the final data interpretation. Copyright © 2012 John Wiley & Sons, Ltd.