buried objects
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2022 ◽  
pp. 15-36
Author(s):  
Elhoucine Essefi

Traditionally, forensic geophysics involves the study, search, localization, and mapping of buried objects or elements within soil, buildings, or water using geophysics tools for legal purposes. Recently, with the evolution of environmental crimes, forensic geophysics gave special care to detection, location, and quantification of polluting products. New techniques including the magnetic susceptibility have emerged to investigate this type of crimes. After discussing the state of the art of forensic geophysics, this chapter proposed the magnetic susceptibility as an efficient tool of environmental crimes detection. A case study of pollution detection was proposed from Tunisia. Being a fast and cheap technique, magnetic surveys represent a real promise for environmental forensic geophysics.


2021 ◽  
Vol 19 ◽  
pp. 173-178
Author(s):  
Christian Siebauer ◽  
Heyno Garbe

Abstract. The basic question of this paper was, whether a detected anomaly found in the ground during an explosives disposal process is actually a non-detonated bomb or non-dangerous metallic scrap. Based on a borehole radar, an approach is to be presented in which first a 2-dimensional contour of the object is created with the aid of a spatial runtime evaluation. By repeating this step at different depths with subsequent graphic overlay, a 3D shape of the buried object is created. The method is first tested using a simulation model with inhomogeneous soil. In the second step the method will be applied and evaluated using a field measurement of a real object. The results shows that both 2D and 3D evaluations reflect the position and orientation of the object. Furthermore, the shape and the dimensions can be estimated, with the restriction that the 3D contour has distortions along the vertical axis. The aim of this work is to show an application of borehole radar, with which the identification of buried objects should be facilitated.


2021 ◽  
Vol 130 (17) ◽  
pp. 175307
Author(s):  
Chia-Chi Liu ◽  
Yen Huang ◽  
Tzu-Hung Chuang ◽  
Deng-Sung Lin ◽  
Der-Hsin Wei

2021 ◽  
Vol 1 (3) ◽  
pp. 130-137
Author(s):  
Alastair Ruffell ◽  
Neil Powell

The aqueous search for objects covered by sediment is a common and challenging problem. Here we outline a sequential methodology for the assessment of targets identified by sub-bottom profiling. This comprises desktop study of available data; background hydrological information gathering (bathymetry, sediment cover, water chemistry); acoustic sub-bottom imaging (water-penetrating radar, sonar); geolocation and probing of sub-bottom anomalies; and deployment of suitable scent dogs. This procedure creates a hierarchy of targets for examination by dive teams and thence recovery.


2021 ◽  
Vol 1997 (1) ◽  
pp. 012018
Author(s):  
H Ali ◽  
A F Ahmad Zaidi ◽  
W K Wan Ahmad ◽  
M S Zanar Azalan ◽  
T S Tengku Amran ◽  
...  

2021 ◽  
Author(s):  
Jay Clausen ◽  
Rosa Affleck ◽  
Christopher Felt ◽  
Michael Musty ◽  
Steven Peckham ◽  
...  

The objective of this study was to determine the effect of environmental phenomonology on the ability to detect buried objects and to provide a predictive capability of when targets are best detectable with IR sensors. Jay Clausen presented this material at the ERDC RD20 Conference.


2021 ◽  
Vol 10 (4) ◽  
pp. 1-27
Author(s):  
Shengxin Jia ◽  
Veronica J. Santos

The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with granular media particles in teleoperated systems. We evaluate the effects of linear and nonlinear classifier model architectures and three tactile sensor modalities (vibration, internal fluid pressure, fingerpad deformation) on the accuracy of estimates of fingertip contact state. We propose an architecture called the Sparse-Fusion Recurrent Neural Network (SF-RNN) in which sparse features are autonomously extracted prior to fusing multimodal tactile data in a fully connected RNN input layer. The multimodal SF-RNN model achieved 98.7% test accuracy and was robust to modest variations in granular media type and particle size, fingertip orientation, fingertip speed, and object location. Fingerpad deformation was the most informative modality for haptic exploration within granular media while vibration and internal fluid pressure provided additional information with appropriate signal processing. We introduce a real-time visualization of tactile percepts for remote exploration by constructing a belief map that combines probabilistic contact state estimates and fingertip location. The belief map visualizes the probability of an object being buried in the search region and could be used for planning.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Luca Bossi ◽  
Pierluigi Falorni ◽  
Saverio Priori ◽  
Roberto Olmi ◽  
Lorenzo Capineri

AbstractMicrowave imaging of shallow buried objects has been demonstrated with holographic radar for landmine detection, civil engineering and cultural heritage. A key component of this system is the antenna based on a truncated cylindrical waveguide with two feeds. This paper investigates for the first time a manufacturing technology based on the 3D printing of a volumetric cylindrical plastic antenna. The investigation of this manufacturing technology was motivated by the reduction in the antenna size and customization of the electromagnetic characteristics to the radio frequency electronics mounted on the robotic scanning system. The antenna that was designed using a simulator and filled with polylactic acid plastic material (relative dielectric permittivity Ɛr = 2.5) is compared to the metal antenna, both operating at around 2 GHz. The goal was to replicate the characteristics of the void core antenna to be able to provide the same quality/information of the microwave images of shallow buried objects. Finally, we compared the scan results of dielectric and metal targets both in the air and in natural soil. From the observation of some of the characteristics of the images, such as dynamics, morphology of the target, signal-to-noise ratio, and operating distance, we demonstrate that 3D printing for volumetric cylindrical waveguide antenna could be used to obtain compact and easily adaptable antennas for different applications in remote sensing.


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