Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data

Jeng, Yih; Li, Yi Wei; Chen, Chih Sung; Chien, Hsin Yi

doi:10.1016/j.jappgeo.2008.08.013

Cited by 36 publications

(17 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test the noise suppression competence of the proposed algorithm, we add the white Gaussian noise and harmonic noise onto the model profile to produce various signal-to-noise (S/N) ratios of 20 dB, 10 dB, 5 dB, 2 dB, and 1 dB. These S/N ratios are rather rigorous in testing the filter performance because a common S/N ratio for an acceptable filtered image is between 30 and 50 dB (Cao, 2013;HuynhThu and Ghanbari, 2008;Jeng et al, 2009). Fig.…”

Section: Regularized Decompositionmentioning

confidence: 99%

“…By treating the two-dimensional (2D) GPR data as an image, several newer methodologies are proposed for GPR noise suppressing, and related researches are gradually growing in number and scope. Among the literature, the adaptive filtering is successfully employed to remove the random noise in GPR data (Jeng et al, 2009;Liu et al, 2006). By adapting the multiresolution wavelet analysis (MRA), the GPR noise can be well suppressed, and the S/N ratio is enhanced efficiently Nuzzo and Quarta, 2004).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A data-driven multidimensional signal-noise decomposition approach for GPR data processing

Chen

Jeng

2015

Computers & Geosciences

Self Cite

View full text Add to dashboard Cite

Section: Regularized Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A data-driven multidimensional signal-noise decomposition approach for GPR data processing

Chen

Jeng

2015

Computers & Geosciences

Self Cite

View full text Add to dashboard Cite

“…As expected, the logarithmic transform reduced the mode mixing problem, resulting in a better resolution in signal and noise recognition. Figure 5 shows a comparison of the results of EEMD filtering without and with the application of logarithmic transform, and compares them with the results of applying two other adaptive filtering techniques proposed by Jeng et al, 2009. To make the comparison more convincing, the amplitudes of all the figures are presented on the logarithmic scale.…”

Section: Synthetic Model Analysismentioning

confidence: 99%

A nonlinear method of removing harmonic noise in geophysical data

Jeng

Chen

2011

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract.A nonlinear, adaptive method to remove the harmonic noise that commonly resides in geophysical data is proposed in this study. This filtering method is based on the ensemble empirical mode decomposition algorithm in conjunction with the logarithmic transform. We present a synthetic model study to investigate the capability of signal reconstruction from the decomposed data, and compare the results with those derived from other 2-D adaptive filters. Applications to the real seismic data acquired by using an ocean bottom seismograph and to a shot gather of the ground penetrating radar demonstrate the robustness of this method. Our work proposes a concept that instead of Fourier-based approaches, the harmonic noise removal in geophysical data can be achieved effectively by using an alternative nonlinear adaptive data analysis method, which has been applied extensively in other scientific studies.

show abstract

“…Lopera et al [7] have proposed a novel approach to filter out these effects from 2-D off-ground monostatic GPR data by adapting and combining the radar antenna subsurface model of Lambot with phase-shift migration. Jeng et al [8] have suggested an adaptive filtering technique of random noise in near-surface seismic and ground-penetrating radar data. In this paper, we propose another method to filter GPR data from random noise, it is based on the combination between the discrete wavelet transform and the Multilayer Perceptron (MLP) neural network model.…”

Section: Introductionmentioning

confidence: 99%

Noise Attenuation from GPR Data Using Wavelet Transform and Artificial Neural Network

Ouadfeul¹,

Aliouane²

2014

IJAPM

View full text Add to dashboard Cite

Abstract:In this paper, a tentative of noise attenuation from the 3D Ground Penetrating Radar (GPR) data using the wavelet transform and the Multilayer Perceptron neural network model is implanted. GPR data recorded in Algerian Sahara are filtered firstly using the discrete wavelet transform, after that a MLP machine with three layers is trained in a supervised learning mode, the input is an extracted profile from the raw 3D GPR data and the output consists of the GPR data of the same profile but after filtering. The estimated weights of connection are used to propagate the remaining non-filtered data through the implanted machine, the calculated output consists to the filtered GPR data. Comparison between the filtered data using the MLP machine and the continuous wavelet transform shows that the neural network machine can be used for S/N ratio improvement of the noisy GPR data.

show abstract

Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data

Cited by 36 publications

References 18 publications

A data-driven multidimensional signal-noise decomposition approach for GPR data processing

A data-driven multidimensional signal-noise decomposition approach for GPR data processing

A nonlinear method of removing harmonic noise in geophysical data

Noise Attenuation from GPR Data Using Wavelet Transform and Artificial Neural Network

Contact Info

Product

Resources

About