• 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.

• The Journal of Engineering Geology
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• v.28 no.3
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• pp.465-473
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• 2018

The application of singular value decomposition (SVD) filtering is examined for attenuation of the ground-roll in land seismic data. Prior to the SVD computation to seek singular values containing the highly correlatable reflection energy, processing steps such as automatic gain control, elevation and refraction statics, NMO correction, and residual statics are performed to enhance the horizontal correlationships and continuities of reflections. Optimal parameters of SVD filtering are effectively chosen with diagnostic display of inverse NMO (INMO) corrected CSP (common shot point) gather. On the field data with dispersion of ground-roll overwhelmed, continuities of reflection events are much improved by SVD filtering than f-k filtering by eliminating the ground-roll with preserving the low-frequency reflections. This is well explained in the average amplitude spectra of the f-k and SVD filtered data. The reflectors including horizontal layer of the reservoir are much clearer on the stack section, with laminated events by SVD filtering and subsequent processing steps of spiking deconvolution and time-variant spectral whitening.

• Journal of Aerospace System Engineering
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• v.16 no.2
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• pp.39-48
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• 2022

The purpose of this paper was to study how to improve the occurrence of abnormal altitude values of radio altimeter, due to RF interference signals during the flight of aircraft. In flight missions, since it performs a roll-out after several high maneuvers, accurate altitude must be displayed to effectively perform flight missions. Thus, a root cause analysis and trouble shooting were performed for the display of abnormal altitude values of radar altimeters, and a method of reducing RF interference signals by installing an attenuator was examined. Additionally, the verification results for the improvements are also described.