3D Migration Depth Focus Velocity Analysis of Hand-Held Ground Penetrating Radar

Author:

Dong Zejun,Feng XuanORCID,Zhou HaoqiuORCID,Zou LilongORCID,Sato Motoyuki

Abstract

Hand-held ground penetrating radar (GPR) systems have been widely applied to landmine detections during recent decades. The accuracy of an imaging result by migration for a hand-held GPR is strongly related to the accuracy of subsurface velocity distribution obtained from multi offset data. For shallow targets like landmines, the hyperbolas are usually not distinct in 2D slices and are masked by the surface reflections. In this article, we propose a 3D migration depth focus velocity analysis method for hand-held GPRs to estimate the background velocity of the subsurface. This method is performed based on the images generated by migrations. The objective function is defined as the proportion of the target on the depth slice containing the target. After migrating a GPR radargram with different velocities, the background velocity, which minimizes the objective function, can be determined by comparing the imaging results by migration using different velocities. To test the proposed method, we apply this procedure to experimental GPR data collected with an advanced landmine imaging system (ALIS) in the laboratory. Subsequently, the velocity of the background is obtained, 3D diffraction migration with the obtained velocity achieves subsurface imaging with high quality. The accurate position and depth of the target are obtained from the optimal migration image.

Funder

Japan Society for the Promotion of Science

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Discrete Tomography Approach for Subsurface Object Detection by Artificial Neural Network;2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS);2023-08-19

2. Vertex extraction-guided local back-projection algorithm for velocity estimation using ground penetrating radar data;International Journal of Remote Sensing;2022-07-03

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