(Received 10 April 2004; accepted 14 May 2005)
Published Online: 31 August 2005
| ||Format||Pages||Price|| |
|PDF (452K)||6||$25||  ADD TO CART|
Cite this document
A search for the body of a victim of terrorist abduction and murder was made in a graveyard on the periphery of a major conurbation in Northern Ireland. The area is politically sensitive and the case of high profile. This required non-invasive, completely non-destructive and rapid assessment of the scene. A MALA RAMAC ground-penetrating radar system was used to achieve these objectives. Unprocessed and processed 400 MHz data show the presence of a collapse feature above and around a known 1970s burial with no similar collapse above the suspect location. In the saturated, clay-rich sediments of the site, 200 MHz data offered no advantage over 400 MHz data. Unprocessed 100 MHz data shows a series of multiples in the known burial with no similar features in the suspect location. Processed 100 MHz lines defined the shape of the collapse around the known burial to 2 m depth, together with the geometry of the platform (1 m depth) the gravedigger used in the 1970s to construct the site. In addition, processed 100 MHz data showed both the dielectric contrast in and internal reflection geometry of the soil imported above the known grave. Thus the sequence, geometry, difference in infill and infill direction of the grave was reconstructed 30 years after burial. The suspect site showed no evidence of shallow or deep inhumation. Subsequently, the missing person's body was found some distance from this site, vindicating the results and interpretation from ground-penetrating radar. The acquisition, processing, collapse feature and sequence stratigraphic interpretation of the known burial and empty (suspect) burial site may be useful proxies for other, similar investigations. GPR was used to evaluate this site within 3 h of the survey commencing, using unprocessed data. An additional day of processing established that the suspect body did not reside here, which was counter to police and community intelligence.
Queen's University, Belfast,
Stock #: JFS2004156