Effective GPR Data Acquisition and Imaging

Tuesday, 16 December 2014
Motoyuki Sato, Tohoku University, Center for Northeast Asian Studies, Sendai, Japan
We have demonstrated that dense GPR data acquisition typically antenna step increment less than 1/10 wave length can provide clear 3-dimeantiona subsurface images, and we created 3DGPR images. Now we are interested in developing GPR survey methodologies which required less data acquisition time. In order to speed up the data acquisition, we are studying efficient antenna positioning for GPR survey and 3-D imaging algorithm.

For example, we have developed a dual sensor “ALIS”, which combines GPR with metal detector (Electromagnetic Induction sensor) for humanitarian demining, which acquires GPR data by hand scanning. ALIS is a pulse radar system, which has a frequency range 0.5-3GHz.The sensor position tracking system has accuracy about a few cm, and the data spacing is typically more than a few cm, but it can visualize the mines, which has a diameter about 8cm. 2 systems of ALIS have been deployed by Cambodian Mine Action Center (CMAC) in mine fields in Cambodia since 2009 and have detected more than 80 buried land mines.

We are now developing signal processing for an array type GPR “Yakumo”. Yakumo is a SFCW radar system which is a multi-static radar, consisted of 8 transmitter antennas and 8 receiver antennas. We have demonstrated that the multi-static data acquisition is not only effective in data acquisition, but at the same time, it can increase the quality of GPR images. Archaeological survey by Yakumo in large areas, which are more than 100m by 100m have been conducted, for promoting recovery from Tsunami attacked East Japan in March 2011.

With a conventional GPR system, we are developing an interpolation method of radar signals, and demonstrated that it can increase the quality of the radar images, without increasing the data acquisition points. When we acquire one dimensional GPR profile along a survey line, we can acquire relatively high density data sets. However, when we need to relocate the data sets along a “virtual” survey line, for example a straight line near the real survey line along which the data were acquired, we can interpolate the data using the acquired data sets. Combining with 3-dimenstional migration algorithm, we found that sparse data acquisition can re-construct subsurface images which were equivalent to that reconstructed from very dense data sets.