scholarly journals Improved Hydrogeophysical Parameter Estimation from Empirical Mode Decomposition Processed Ground Penetrating Radar Data

2009 ◽  
Vol 14 (4) ◽  
pp. 171-178 ◽  
Author(s):  
A. D. Addison ◽  
B. M. Battista ◽  
C. C. Knapp
Keyword(s):  
Parameter Estimation ◽  
Empirical Mode Decomposition ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Mode Decomposition ◽  
Ground Penetrating

scholarly journals Object Detection of Ground-Penetrating Radar Signals Using Empirical Mode Decomposition and Dynamic Time Warping

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 230 ◽  
Author(s):  
Xi Wu ◽  
Christopher Adam Senalik ◽  
James Wacker ◽  
Xiping Wang ◽  
Guanghui Li
Keyword(s):  
Object Detection ◽  
Empirical Mode Decomposition ◽  
Ground Penetrating Radar ◽  
Dynamic Time Warping ◽  
Dielectric Constants ◽  
Time Warping ◽  
Feature Identification ◽  
Mode Decomposition ◽  
Dynamic Time ◽  
Ground Penetrating

An object detection method of ground-penetrating radar (GPR) signals using empirical mode decomposition (EMD) and dynamic time warping (DTW) is proposed in this study. Two groups of timber specimens were examined. The first group comprised of Douglas fir (Pseudotsuga menziesii) timber sections prepared in the laboratory with inserts of known internal characteristics. The second group comprised of timber girders salvaged from the timber bridges on historic Route 66 over 80 years. A GSSI Subsurface Interface Radar (SIR) System 4000 with a 2 GHz palm antenna was used to scan these two groups of specimens. GPR sensed differences in dielectric constants (DC) along the scan path caused by the presence of water, metal, or air within the wood. This study focuses on the feature identification and defect classification. The results show that the processing methods were efficient for the illustration of GPR information.

scholarly journals Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar

2021 ◽  
Vol 1878 (1) ◽  
pp. 012022
Author(s):  
Hasimah Ali ◽  
Mohd Shuhanaz Zanar Azalan ◽  
Ahmad Firdaus Ahmad Zaidi ◽  
Tengku Sarah Tengku Amran ◽  
Mohamad Ridzuan Ahmad ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Ground Penetrating Radar ◽  
Buried Objects ◽  
Mode Decomposition ◽  
Ground Penetrating

Reconstructing Properties of Subsurface from Ground-Penetrating Radar Data

PIERS Online ◽  
2006 ◽  
Vol 2 (6) ◽  
pp. 567-572
Author(s):  
Hui Zhou ◽  
Dongling Qiu ◽  
Takashi Takenaka
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating

scholarly journals Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

2021 ◽  
pp. 1-19
Author(s):  
Melchior Grab ◽  
Enrico Mattea ◽  
Andreas Bauder ◽  
Matthias Huss ◽  
Lasse Rabenstein ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Thickness Distribution ◽  
Radar Data ◽  
Tourism Industry ◽  
Swiss Alps ◽  
Ice Thickness ◽  
Hazard Management ◽  
Bed Topography ◽  
Ice Volume ◽  
Ground Penetrating

Abstract Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

Sign in / Sign up

Export Citation Format

Share Document