Search Results (6)

For archaeological studies, the expected outcome of a Ground Penetrating Radar (GPR) survey is a series of time-slices (or depth-slices) that mark the position of buried structures at different depths. The clarity of these time-slices is strongly site-dependent and is particularly worsened in the presence of even small percentages of clay, which strongly attenuates the GPR signal. This is the condition affecting the Greek–Roman archaeological site of Tindari (Sicily, Italy). Here, we performed a multichannel GPR survey particularly focusing on a residential insula. In order to increase the signal-to-noise ratio, we tested two processing strategies: a conventional in-line stacking and a new concept of off-line stacking. This last was performed dividing spatially adjacent channels of the GPR multichannel system into groups and stacking the signals of each group at each specific location. We observed that off-line stacking improves the signal-to-noise ratio in 2D sections and time-slices quality. Comparisons showed that off-line stacking has a clear advantage over traditional in-line stacking, at least for the specific application reported in this paper. Off-line stacking of GPR multichannel systems is, therefore, simple but very effective in increasing the investigation depth, especially in challenging environments. Full article

In the process of using multi-channel ground-penetrating radar (GPR) for underground cavity exploration, the acquired 3D data include reflection data from underground cavities or various underground objects (structures). Reflection data from unspecified structures can interfere with the identification process of underground cavities. This study aims to identify underground cavities using a C-GAN model with an applied ResBlock technique. This deep learning model demonstrates excellent performance in the image domain and can automatically classify the presence of cavities by analyzing 3D GPR data, including reflection waveforms (A-scan), cross-sectional views (B-scan), and plan views (C-scan) measured from the ground under roads. To maximize the performance of the C-GAN model, a background filtering algorithm (BFA) was developed and applied to enhance the visibility and clarity of underground cavities. To verify the performance of the developed BFA, 3D data collected from roads in Seoul, Republic of Korea, using 3D GPR equipment were transformed, and the C-GAN model was applied. As a result, it was confirmed that the recall, an indicator of cavity prediction, improved by approximately 1.15 times compared to when the BFA was not applied. This signifies the verification of the effectiveness of the BFA. This study developed a special algorithm to distinguish underground cavities. This means that in the future, not only the advancement of separate equipment and systems but also the development of specific algorithms can contribute to the cavity exploration process. Full article

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations. Full article

Artificial Neural Network (ANN) approaches are applied to detect and determine the object class using a special set of the UltraWideBand (UWB) pulse Ground Penetrating Radar (GPR) sounding results. It used the results of GPR sounding with the antenna system, consisting of one radiator and four receiving antennas located around the transmitting antenna. The presence of four receiving antennas and, accordingly, the signals received from four spatially separated positions of the antennas provide a collection of signals received after reflection from an object at different angles and, due to this, to determine the location of the object in a coordinate system, connected to the antenna. We considered the sums and differences of signals received by two of the four antennas in six possible combinations: (1 and 2, 1 and 3, 2 and 3, 1 and 4, etc.). These combinations were then stacked sequentially one by one into one long signal. Synthetic signals constructed in such a way contain many more notable differences and specific information about the class to which the object belongs as well as the location of the searched object compared to the signals obtained by an antenna system with just one radiating and one receiving antenna. It therefore increases the accuracy in determining the object’s coordinates and its classification. The pulse radiation, propagation, and scattering are numerically simulated by the finite difference time domain (FDTD) method. Results from the experiment on mine detection are used to examine ANN too. The set of signals from different objects having different distances from the GPR was used as a training and testing dataset for ANN. The training aims to recognize and classify the detected object as a landmine or other object and to determine its location. The influence of Gaussian noise added to the signals on noise immunity of ANN was investigated. The recognition results obtained by using an ANN ensemble are presented. The ensemble consists of fully connected and recurrent neural networks, gated recurrent units, and a long-short term memory network. The results of the recognition by all ANNs are processed by a meta network to provide a better quality of underground object classification. Full article

Due to the capabilities of non-destructive testing of inaccessible objects, GPR (Ground Penetrating Radar) is used in geology, archeology, forensics and increasingly also in engineering tasks. The wide range of applications of the GPR method has been provided by the use of advanced technological solutions by equipment manufacturers, including multi-channel units. The acquisition of data along several profiles simultaneously allows time to be saved and quasi-continuous information to be collected about the subsurface situation. One of the most important aspects of data acquisition systems, including GPR, is the appropriate methodology and accuracy of the geoposition. This publication aims to discuss the results of GPR measurements carried out using the multi-channel Leica Stream C GPR (IDS GeoRadar Srl, Pisa, Italy). The significant results of the test measurement were presented the idea of which was to determine the achievable accuracy depending on the georeferencing method using a GNSS (Global Navigation Satellite System) receiver, also supported by time synchronization PPS (Pulse Per Second) and a total station. Methodology optimization was also an important aspect of the discussed issue, i.e., the effect of dynamic changes in motion trajectory on the positioning accuracy of echograms and their vectorization products was also examined. The standard algorithms developed for the dedicated software were used for post-processing of the coordinates and filtration of echograms, while the vectorization was done manually. The obtained results provided the basis for the confrontation of the material collected in urban conditions with the available cartographic data in terms of the possibility of verifying the actual location of underground utilities. The urban character of the area limited the possibility of the movement of Leica Stream C due to the large size of the instrument, however, it created the opportunity for additional analyses, including the accuracy of different location variants around high-rise buildings or the agreement of the amplitude distribution at the intersection of perpendicular profiles. Full article

A site located on the island of Brač is known in history for world-famous architectural stone and stone mining, dating all the way back to ancient Greek and Roman times. The most famous building constructed from the stone from Brač is the Diocletian Cesar Palace in the town Split. Prospective new locations for quarries are still required because the demand for the stone from the island is still high. This paper presents a review of undertaken geophysical investigations, as well as engineering geologic site prospection, with the purpose of determining if the rock mass quality is suitable for the mining of massive blocks needed for an architectural purpose—dimension stones. Several surface noninvasive geophysical methods were applied on the site, comprising of two seismic methods, multichannel analysis of surface waves (MASW) and shallow refraction seismic (SRS) electrical methods of electrical resistivity tomography (ERT), as well as electromagnetic exploration with ground penetrating radar (GPR). Results of geophysical investigations were compared to the engineering geologic prospection results, as well to the visible rock mass structure and observed discontinuities on the neighboring existing open mine quarry. Rock mass was classified into three categories according to its suitability for dimension stone exploitation. Each category is defined by compressional and shear seismic velocities as well as electrical resistivity. It has been found that even small changes in moisture content within the large monolithic rock mass can influence measured values of electrical resistivity. In the investigated area, dimension stone quarrying is advisable if the rock mass has values of resistivity higher than 3000 Ωm, as well as compressional seismic velocities higher than 3000 m/s and shear wave velocities higher than 1500 m/s. Georadar was found to be a good tool for the visual determination of fissured systems, and was used to confirm findings from other geophysical methods. Full article