Utility Of Ground-penetrating Radar Research Articles

Innovative method for recognizing subgrade defects based on a convolutional neural network

Subgrade defects originate below the base of an asphalt pavement and they contribute significantly to pavement damage. The detection of subgrade defects is considered challenging because the recognition of defects is difficult. Therefore, the utilization of ground penetrating radar (GPR) to detect subgrade defects has attracted significant interest in recent years. However, the use of manually processed GPR images for classifying defects is inefficient and inaccurate. Thus, in this study, we applied convolutional neural networks (CNNs) to GPR images for automatically classifying subgrade defects (e.g., uneven settlement, sinkholes, and subgrade cracks). Two CNNs called multi-stage CNN and cascade CNN with different structures were established to accomplish the tasks automatically. The main difference between the two CNNs is that the cascade CNN is a classifier 2, which is for recognition and trained only using hard samples. Each CNN was developed in training, validation, and testing processes. Based on the training and testing results, sensitivity analysis was performed to verify the stability of the CNNs. We compared state-of-the-art methods for defect detection and the CNN-based method in order to verify the superior performance of the CNNs. Finally, we tested an application of the CNN-based method to show that it is transferrable to other asphalt pavements. The training results indicated that the cascade CNN classified subgrade defects with 97.35% accuracy during training and 96.80% in validation, while the multi-stage CNN classified subgrade defects with 91.35% accuracy during training and 90.45% in validation. The sensitivity analysis results showed that the cascade CNN exhibited the expected stability in terms of the transmitting frequency, i.e., the frequency of a high-frequency electromagnet wave from the transmitting antenna of the GPR, and different highway structures, whereas the multi-stage CNN did not. In addition, compared with Sobel edge detection and K-value clustering analysis, the CNN-based method obtained more robust performance at subgrade defect detection under various conditions using raw images. These results indicate that the CNN-based method performs well and it can classify subgrade defects in realistic situations.

Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks

Concealed cracks in asphalt pavement are the cracks that originate below the surface of the pavement. These cracks are a major contributing factor to pavement damage, in addition to being a major contributing factor to the formation of reflection cracks. The detection of a concealed crack is considered challenging because the location of the crack is, by definition, difficult to find. Therefore, the research on the utilization of ground penetrating radar (GPR) to locate concealed cracks has gained significant interest in recent years. However, the manually processed GPR image used for the recognition, location, and measurement of concealed cracks is inefficient and inaccurate. This project presents an application of convolutional neural networks (CNNs) to GPR images that automatically recognizes, locates, measures, and produces a 3D reconstruction of concealed cracks. In this project, three different CNNs (recognition, location, and feature extraction) were established to accomplish the aforementioned tasks automatically. Each CNN is developed through processes of structural design, training, and testing. The recognition CNN was designed to distinguish concealed cracks from other types of damage in a GPR image, the location CNN determined the location and length measurement of concealed crack images based on the results provided by the recognition CNN, and crack feature points were extracted by the feature extraction CNN to establish the 3D reconstruction models of the concealed cracks. The 3D reconstruction models were then used to calculate crack volume and predict the growth tendency of cracks. The results indicated that the recognition CNN is able to distinguish concealed cracks from other types of damages in 6482 GPR images with zero errors. In addition, the length recognition results calculated from the location CNN possess a 0.2543cm mean squared error, a 0.978cm maximum length error, and a 0.504cm average error in the test samples. Meanwhile, the feature extraction CNN is able to provide feature points for a 3D reconstruction model. The results of this study suggest that the CNNs could be accurately used for the recognition, location, and 3D reconstruction of concealed cracks in asphalt pavement in real-world applications.

MOUND BUILDING AT LAKE JACKSON (8LE1), TALLAHASSEE, FLORIDA: NEW INSIGHTS FROM GROUND PENETRATING RADAR

The Lake Jackson Mounds site (8LE1), located near Tallahassee, Florida, has long been considered to be a frontier Mississippian center. This assertion is primarily based on elaborate burial goods recovered during salvage excavations in the 1970s. Ground penetrating radar (GPR) on the two largest intact mounds at Lake Jackson revealed new information about their morphology and construction histories. These findings demonstrate that mound-building practices at the site were distinct from earlier, local Woodland mound-building traditions, and more similar to those of other Mississippian centers, such as Etowah and Moundville. Lake Jackson revitalized mound building in the Tallahassee area under the influence of external connections with groups in the Mississippian interaction network. These findings show how mound building was an integral practice for expressing and expanding Mississippian ideologies and rituals. This work also shows the utility of GPR in exploring mounds' morphologies and construction histories.

The utility of ground-penetrating radar and its time-dependence in the discovery of clandestine burials

In the field of forensic investigation burial is a relatively common method of hiding a corpse. The location of clandestine graves is, however, a particularly difficult task in which multiple forensic disciplines such as anthropology, botany or archaeology can provide valuable assistance. The use of GPR (ground-penetrating radar) has recently been introduced as a method in the detection of these graves, but what is the true potential of this tool in an operative search scenario?In this study a total of 11 pig carcasses were buried in two wooded areas, each presenting a similar soil composition. The animals were subsequently exhumed at regular intervals, ranging from 2 to 111 weeks, using systematic GPR analysis of the burial sites and archaeological recovery of the subjects that were then autopsied. GPR proved to be useful in recognizing anomalies at the chosen depths of burial and appeared to be dependent on the state of decay of the samples, producing only slight anomalous readings in the presence of skeletal remains: at 92 weeks from burial the difference in signal was weak and at 111 weeks GPR survey offered no helpful information as to burial location. The experiment, in this particular context, determined the technique as being successful in the presence of recent burials, highlighting the need for a multidisciplinary approach in the operative search for buried human remains.

Application of ground penetrating radar (GPR) to delineate clay layers in wetlands. A case study in the Soto Grande and Soto Chico watercourses, Doñana (SW Spain)

The potential utility of ground penetrating radar (GPR) in delineating the geometry of clay layers under two watercourses of the Doñana National Park (SW Spain) has been checked. The interpretation of the GPR profiles, calibrated with piezometers, shows the presence of two clay layer roughly continuous along some 2 km. The work allowed to gain insight about the watercourses relationship with groundwater. Up to now, watercourses in the area have been associated as a matter of fact to the intersection of the water table with the topography. But this work shows the existence of shallow, roughly continuous clay layers under the watercourses that may generate perched phreatic levels even if the regional water table is too deep to crop out. Thus, the GPR proved to be very helpful and suitable in supporting hydrogeological studies in wetlands.

ABSTRACT: Testing the Utility of Ground-penetrating Radar for Outcrop Reservoir Analog Studies: An Example from Pennsylvanian Carbonate Strata in Kansas

ABSTRACT: Testing the Utility of Ground-penetrating Radar for Outcrop Reservoir Analog Studies: An Example from Pennsylvanian Carbonate Strata in Kansas

Utility of Ground-Penetrating Radar in Near-Surface, High-Resolution Imaging of Lansing-Kansas City (Pennsylvanian) Limestone Reservoir Analogs

High-resolution ground-penetrating radar (GPR) is a subsurface imaging tool that can extend results gained from studies of reservoir-analog outcrops and add detailed information about reservoir analogs that is unavailable from either seismic data or well control alone. Integration of GPR-reflection information and outcrop photomosaics allowed detailed study of subtle changes in lithology and bedding surfaces by comparing lateral and vertical changes in GPR-reflection character with outcrop features. Outcrops are valuable for confirming interpretations of reflections and providing velocity information for the GPR data. Outcrops of two Lansing-Kansas City Group limestone units, the Captain Creek Limestone and the Plattsburg Limestone, were used as test sites to determine the vertical imaging resolution, penetration depth, and reflection character of high-frequency (500 MHz) GPR in interbedded carbonate and shale units, where the carbonate units contained thin interbeds of shale. Features as small as 0.1-0.2 m (0.3-0.7 ft)--including major architectural elements (such as major and minor bounding surfaces) and internal features (such as fractures, internal bedding, and crossbedding)--were successfully imaged and confirmed by outcrop data. Variations in GPR-reflection character between geologic units allowed recognition of argillaceous limestone units in the subsurface. Although shale and soil at the surface generally impeded GPR signal penetration, thin shale layers and shale at bounding surfaces actually enhanced reflectivity and aided in interpretation. Our results indicate that GPR can be successfully used as an aid in outcrop studies to provide quantitative data for use in reservoir modeling.