scholarly journals Investigation of Rawa Dano Volcanic Deposits and Its Paleotopography Using Ground-Penetrating Radar

2021 ◽  
Vol 873 (1) ◽  
pp. 012069
Author(s):  
Maryadi Maryadi ◽  
Fira Mariah Sausan Champai ◽  
I Nyoman Triananda ◽  
Andi Darmawan ◽  
Gamma Abdul Jabbar
Keyword(s):  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Volcanic Eruptions ◽  
Geological Investigation ◽  
Radar Images ◽  
Volcanic Deposits ◽  
Geological Event ◽  
Ground Penetrating ◽  
Pyroclastic Fall ◽  
Fall Deposits

Abstract The detailed mechanisms of volcanic eruptions happened around Rawa Dano, Banten, Indonesia, remain undiscovered. One of the key features to this geological event is the presence of a 13.7 km × 6.5 km caldera-like morphology in the middle of Banten tuff deposits. Surface geological investigation in the area indicates that the eruptions are massive and occurred in several periods. Low-frequency ground-penetrating radar (GPR) signals are used as an aid to identify the unexposed part of the deposits in this volcanological study. Common-offset GPR surveys were carried out along three measurement lines traversing over the deposit outcrops. An outcrop which is exposed after sand mining activities at one of the survey locations shows dipping interfaces between the upper pyroclastic flow deposits, pumice-rich deposits, paleosol, and the lower pyroclastic fall deposits. These stratigraphic contacts are detected as well under the surface which are clearly recognizable in radar images. The GPR cross-section also shows some other reflections due to different deposit types. The overall results of the GPR profiles give the idea about the thickness of each type of volcanic deposits and the paleotopography in the surrounding area.

Seismic reflection and ground‐penetrating radar imaging of a shallow aquifer

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1310-1317 ◽  
Author(s):  
Steven J. Cardimona ◽  
William P. Clement ◽  
Katharine Kadinsky‐Cade
Keyword(s):  
Water Table ◽  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Shallow Aquifer ◽  
Cone Penetrometer ◽  
Gradual Change ◽  
Cone Penetration ◽  
Geotechnical Data ◽  
Radar Images ◽  
Ground Penetrating

In 1995 and 1996, researchers associated with the US Air Force’s Phillips and Armstrong Laboratories took part in an extensive geophysical site characterization of the Groundwater Remediation Field Laboratory located at Dover Air Force Base, Dover, Delaware. This field experiment offered an opportunity to compare shallow‐reflection profiling using seismic compressional sources and low‐frequency ground‐penetrating radar to image a shallow, unconfined aquifer. The main target within the aquifer was the sand‐clay interface defining the top of the underlying aquitard at 10 to 14 m depth. Although the water table in a well near the site was 8 m deep, cone penetration geotechnical data taken across the field do not reveal a distinct water table. Instead, cone penetration tests show a gradual change in electrical properties that we interpret as a thick zone of partial saturation. Comparing the seismic and radar data and using the geotechnical data as ground truth, we have associated the deepest coherent event in both reflection data sets with the sand‐clay aquitard boundary. Cone penetrometer data show the presence of a thin lens of clays and silts at about 4 m depth in the north part of the field. This shallow clay is not imaged clearly in the low‐frequency radar profiles. However, the seismic data do image the clay lens. Cone penetrometer data detail a clear change in the soil classification related to the underlying clay aquitard at the same position where the nonintrusive geophysical measurements show a change in image character. Corresponding features in the seismic and radar images are similar along profiles from common survey lines, and results of joint interpretation are consistent with information from geotechnical data across the site.

The use of ground penetrating radar to map an ancient village buried by volcanic eruptions

1998 ◽  
Vol 40 (1-3) ◽  
pp. 49-58 ◽  
Author(s):  
Miho Tohge ◽  
Fumio Karube ◽  
Megumi Kobayashi ◽  
Akio Tanaka ◽  
Katsumi Ishii
Keyword(s):  
Ground Penetrating Radar ◽  
Volcanic Eruptions ◽  
Ground Penetrating

scholarly journals Object Detection in Ground-Penetrating Radar Images Using a Deep Convolutional Neural Network and Image Set Preparation by Migration

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Shinichiro Iso ◽  
Kyosuke Onishi ◽  
Toshifumi Matsuoka
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Neural Network Approach ◽  
Radar Images ◽  
Image Set ◽  
Ground Penetrating ◽  
Better Than

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.

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