• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.49-55
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• 2006

The purpose of the present study is to analyse characteristics of the background noise for the broadband seismic stations in KMA. It is well known that the background noise arises continuously from long period microseism, sea waves, minute changes of atmospheric pressure, seasonal temperature change of the ground surface, culture activities, and etc. The background noise shows spatial and temporal changes and it has various characteristics such as its spectral amplitudes in frequency domain are not constant Such the background noise gives considerable influences on the quality of seismic record. To investigate annual variations, the background noise was separated into high frequency components of above 1Hz More larger average amplitude is found in winter than other seasons. The average amplitude for 12 seismic stations are compared. It is known that the background noise is considerably larger in stations located in island region such as Jeju, Ulleungdo, and Bagryeongdo seismic stations. However the noise is relatively small in inland stations such as Chuncheon, Chungju and Uljin seismic stations.

• 한국지구물리탐사학회:학술대회논문집
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• 2009.10a
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• pp.54-59
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• 2009

Seismic station of high noise level has difficulties detecting relatively weak ground motions due to small earthquakes or teleseismic events because earthquake detectability of seismic station depends on seismic noise level. To figure out the capability of earthquake detection of a seismic network, therefore, seismic noise level of each station also needs to be considered, including the distribution of seismic stations. Recently, it has been known that most of broadband seismic stations in South Korea have affected by cultural noise in the frequencies higher than 1 Hz and show diurnal variations of noise level. In order to analyze the effect of diurnal variation of seismic noise level on earthquake detectability, we used the result of background seismic noise level analysis of seismograms of 30 broadband stations of KIGAM and KMA from 2005 to 2007. This study shows that earthquakes greater than magnitude 2.4 occurring within the Korean Peninsula can be detected at night while those greater than magnitude 2.6 can be detected in the daytime.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.71-84
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• 2022

When acquiring seismic data, various types of simultaneously recorded seismic noise hinder accurate interpretation. Therefore, it is essential to attenuate this noise during the processing of seismic data and research on seismic noise attenuation. For this purpose, machine learning is extensively used. This study attempts to attenuate noise in prestack seismic data using unsupervised machine learning. Three unsupervised machine learning models, N2NUNET, PATCHUNET, and DDUL, are trained and applied to synthetic and field prestack seismic data to attenuate the noise and leave clean seismic data. The results are qualitatively and quantitatively analyzed and demonstrated that all three unsupervised learning models succeeded in removing seismic noise from both synthetic and field data. Of the three, the N2NUNET model performed the worst, and the PATCHUNET and DDUL models produced almost identical results, although the DDUL model performed slightly better.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.395-403
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• 2014

This paper presents a new approach to improve the signal to noise ratio (SNR) for local seismic disaster preventing system in densely populated area. The seismic data measured in the local site includes various sensing noises (offset or measurement noise) and man-made/natural noises (road and rail traffic noise, rotating or hammering machinery noise, human activity noise such as walking and running, wind/atmospheric pressure-generated noise, etc.). These additive noises are different in time and frequency characters. The proposed method uses 3-stages processing to reduce these different additive noises. In the first stage, misalignment offset noise are diminished by time average processing, and then the second and third stages, coherent/incoherent noises such as man-made/natural noises are suppressed by array stacking. In addition, we derived the theoretical equation of the SNR gain improved by the proposed method. To evaluate the performance of the proposed method, computer simulations were performed with real seismic data and test equipment generated data as the input.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.8
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• pp.1309-1314
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• 2008

Seismic data is often contaminated with high-energy, spatially aliased noise, which has proven impractical to attenuate using Fourier techniques. Wavelet filtering, however, has proven capable of attacking several types of localized noise simultaneously regardless of their frequencies. In this study a 2-D stationary wavelet transform is used to decompose seismic data into its wavelet components. A threshold is applied to these coefficients to attenuate high amplitude noise, followed by an inverse transform to reconstruct the seismic trace. The stationary wavelet transform minimizes the phase-shift errors induced by thresholding that occur when the conventional discrete wavelet transform is used.

• Geophysics and Geophysical Exploration
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• v.3 no.3
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• pp.83-87
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• 2000

The noise prediction filter using a local/remote reference was developed to obtain a high quality data from seismic surveys over the area where seismic transmission power is limited. The method used in the noise prediction filter is a 3-layer neural network whose algorithm is backpropagation. A NRF (Noise Reduction Factor) value of about 3.0 was obtained with appling training and test data to the trained noise prediction filter. However, the scaling technique generally used for minimizing EM noise from electric and electromagnetic data cannot reduce seismic noise, since the technique can allow only amplitude difference between two time series measured at the primary and reference sites.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.41-48
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• 2000

We have installed the borehole seismic recording system at Hyodongri in eastern part of Kyungsan Basin, which has the advantages of reduction in noise by human activities and distorting effects of near-surface rocks. Here we describe briefly the borehole seismic station and recording system. And we analyse the characteistics of back ground the station obtained from borehole sensors. The back ground noise level in time domain is about 50~100$\mu$cm/sec. The average curve of noise spectrum is lower than NHNM(New High Noise Model)of GSN(Global Seismic Network)operated by USGS. The results could be useful prior information for study on earthquake records observed at Hydongri station.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.91-107
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• 2024

Single-channel seismic exploration has proven effective in delineating subsurface geological structures using small-scale survey systems. The seismic data acquired through zero- or near-offset methods directly capture subsurface features along the vertical axis, facilitating the construction of corresponding seismic sections. However, substantial noise in single-channel seismic data hampers precise interpretation because of the low signal-to-noise ratio. This study introduces a novel approach that integrate noise reduction and signal enhancement via matrix rank optimization to address this issue. Unlike conventional rank-reduction methods, which retain selected singular values to mitigate random noise, our method optimizes the entire singular value spectrum, thus effectively tackling both random and erratic noises commonly found in environments with low signal-to-noise ratio. Additionally, to enhance the horizontal continuity of seismic events and mitigate signal loss during noise reduction, we introduced an adaptive weighting factor computed from the eigenimage of the seismic section. To access the robustness of the proposed method, we conducted numerical experiments using single-channel Sparker seismic data from the Chukchi Plateau in the Arctic Ocean. The results demonstrated that the seismic sections had significantly improved signal-to-noise ratios and minimal signal loss. These advancements hold promise for enhancing single-channel and high-resolution seismic surveys and aiding in the identification of marine development and submarine geological hazards in domestic coastal areas.

• Geophysics and Geophysical Exploration
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• v.21 no.2
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• pp.94-102
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• 2018

To characterize virtual reflection images of deep subsurface by the method of seismic interferometry, we analyzed effects of offset range, ambient noise, missing data, and statics on interferograms. For the analyses, seismic energy was simulated to be generated by a 5 Hz point source at the surface. Vertical components of particle velocity were computed at 201 sensor locations at 100 m depths of 1 km intervals by the finite difference method. Each pair of synthetic seismic traces was cross-correlated to generate stacked reflection section by the conventional processing method. Wide-angle reflection problems in reflection interferometry can be minimized by setting a maximum offset range. Ambient noise, missing data, and statics turn to yield processing noise that spreads out from virtual sources due to stretch mutes during normal moveout corrections. The level of processing noise is most sensitive to amplitude and duration time of ambient noise in stacked sections but also affected by number of missing data and the amount of statics.