• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1405-1419
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• 2018

An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.227-234
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• 2020

In this paper, we propose wav-U-Net to improve speech enhancement in heavy noisy environments, and it has implemented three principal techniques. First, as input data, we use 128 modified Mel-scale filter banks which can reduce computational burden instead of 512 frequency bins. Mel-scale aims to mimic the non-linear human ear perception of sound by being more discriminative at lower frequencies and less discriminative at higher frequencies. Therefore, Mel-scale is the suitable feature considering both performance and computing power because our proposed network focuses on speech signals. Second, we add a simple ResNet as pre-processing that helps our proposed network make estimated speech signals clear and suppress high-frequency noises. Finally, the proposed U-Net model shows significant performance regardless of the kinds of noise. Especially, despite using a single channel, we confirmed that it can well deal with non-stationary noises whose frequency properties are dynamically changed, and it is possible to estimate speech signals from noisy speech signals even in extremely noisy environments where noises are much lauder than speech (less than SNR 0dB). The performance on our proposed wav-U-Net was improved by about 200% on SDR and 460% on NSDR compared to the conventional Jansson's wav-U-Net. Also, it was confirmed that the processing time of out wav-U-Net with 128 modified Mel-scale filter banks was about 2.7 times faster than the common wav-U-Net with 512 frequency bins as input values.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.6 s.348
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• pp.47-52
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• 2006

This paper proposes a novel low-power $32bit\times32bit$ multiplier for deep submicron technologies beyond 130nm. As technology becomes small, static power due to leakage current significantly increases, and it becomes comparable to dynamic power. Recently, shutdown method based on MTCMOS is widely used to reduce both dynamic and static power. However, it suffers from severe power line noise when restoring whole large-size functional block. Therefore, the proposed multiplier mitigates this noise by shutting down and waking up sequentially along with pipeline stage. Fabricated chip measurement results in $0.35{\mu}m$ technology and gate-transition-level simulation results in 130nm and 90nm technologies show that it consumes $66{\mu}W,\;13{\mu}W,\;and\;6{\mu}W$ in idle mode, respectively, and it reduces power consumption to $0.04%\sim0.08%$ of active mode. As technology becomes small, power reduction efficiency degrades in the conventional clock gating scheme, but the proposed multiplier does not.

• Geophysics and Geophysical Exploration
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• v.11 no.2
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• pp.127-136
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• 2008

To identify the potential area of gas hydrate in the Ulleung Basin, 2-D and 3-D seismic surveys using R/V Tamhae II were conducted in 2005 and 2006. Seismic survey equipment consisted of navigation system, recording system, streamer cable and air-gun source. For reliable velocity analysis in a deep sea area where water depths are mostly greater than 1,000 m and the target depth is up to about 500 msec interval below the seafloor, 3-km-long streamer and 1,035 $in^3$ tuned air-gun array were used. During the survey, a suite of quality control operations including source signature analysis, 2-D brute stack, RMS noise analysis and FK analysis were performed. The source signature was calculated to verify its conformity to quality specification and the gun dropout test was carried out to examine signature changes due to a single air gun's failure. From the online quality analysis, we could conclude that the overall data quality was very good even though some seismic data were affected by swell noise, parity error, spike noise and current rip noise. Especially, by checking the result of data quality enhancement using FK filtering and missing trace restoration technique for the 3-D seismic data inevitably contaminated with current rip noises, the acquired data were accepted and the field survey could be conducted continuously. Even in survey areas where the acquired data would be unsuitable for quality specification, the marine seismic survey efficiency could be improved by showing the possibility of noise suppression through onboard data processing.