Identification of Unstable Subsurface Rock Structure Using Ground Penetrating Radar: An EEMD-Based Processing Method

Yang, Jianzhong; Duan, Yan-Tao

doi:10.3390/app10238499

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…However, the EMD method has the mode mixing phenomenon, so this paper used the EEMD algorithm to decompose the stator current. The EEMD method is a step forward from the EMD method, which can decompose any nonlinear and nonstationary signal into the sum of IMFs and a residual value ( r ) [ 31 , 32 ]. The decomposition steps are as follows:…”

Section: Decomposition and Reconstruction Of Current Signal Of Cutting Motormentioning

confidence: 99%

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Liu

et al. 2021

Entropy

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This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

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Section: Decomposition and Reconstruction Of Current Signal Of Cutting Motormentioning

confidence: 99%

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Liu

et al. 2021

Entropy

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“…Ground Penetrating Radar (GPR), as a non-destructive, efficient, and convenient geophysical detection instrument, has a wide variety of applications in urban infrastructure inspection [1], urban road internal disease detection [2], urban underground pipeline probing [3], rebar recognition for tunnel secondary lining [4], and identifying surrounding cracked rocks of underground caverns [5]. In different application scenarios, the ROIs of reflection hyperbola are difficult to identify quickly and accurately due to the different acquisition environments.…”

Section: Introductionmentioning

confidence: 99%

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

Zhang,

Dai,

Feng

et al. 2024

Applied Sciences

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Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images.

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Identification of Unstable Subsurface Rock Structure Using Ground Penetrating Radar: An EEMD-Based Processing Method

Cited by 2 publications

References 37 publications

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

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