• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.8A
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• pp.1298-1310
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• 2001

이 논문에서는 채널 상태가 천천히 변할 때, 직교 주파수 분할 다중화-부호분할 다중접속 시스템에 적합한 채널 추정 방법을 제안하고 성능을 분석한다. 제안한 채널 추정 방법은 시간과 주파수 영역 모두에 파일럿 심볼을 삽입하고, 이산 퓨리에 변환 후 영을 채워 넣는 방법을 이용한 내삽법으로 모든 부채널의 전달 함수를 얻는다. 또한 변환 영역에서 저대역 여파를 통해 받은 파일럿 신호에 존재하는 가산성 백색 정규 잡음의 영향을 상당히 줄일 수 있다. 이 때 저대역 여파기의 차단 주파수는 채널의 다중경로 수에 따라 정해진다. 같은 방법을 이용한 내삽법을 시간축으로 적용하여 시간에 따라 변하는 부채널의 채널 응답을 얻을 수 있다. 이 논문에서는 여러 가지 내삽법에 대한 평균 제곱 오차 성능을 수식적으로 제시한다. 제안한 저대역 여파를 결합한 내삽법을 쓰면 채널의 통계적 특성에 관한 정보 없이, 그리고 훨씬 적은 계산량으로 선형 최소 평균 제곱 오차 추정기와 비슷한 성능을 얻는다. 레일리 감쇄 채널에 대한 모의 실험을 통해 같은 비트 오류율에 대한 신호 대 잡음 비의 이득이 있음을 보인다.

• Geophysics and Geophysical Exploration
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• v.21 no.2
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• pp.103-111
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• 2018

The recent research aim of seismic trace interpolation is to effectively interpolate the data with spatial aliasing. Among various interpolation methods, the Matching Pursuit interpolation, that finds the proper combination of basis functions which can best recover traces, has been developed. However, this method cannot interpolate aliased data. Thus, the multi-component Matching Pursuit interpolation and moveout correction method have been proposed for interpolation of spatially aliased data. It is difficult to apply the multi-component Matching Pursuit interpolation to interpolating the OBC (Ocean Bottom Cable) data which is the multi-component data obtained at the ocean bottom because the isolation of P wave component is required in advance. Thus, in this study, we dealt with an effective single-component matching Pursuit interpolation method in OBC data where P-wave and S-wave are mixed and spatial aliasing is present. To do this, we proposed the Ricker wavelet based single-component Matching Pursuit interpolation workflow with moveoutcorrection and systematically investigated its effectiveness. In this workflow, the spatial aliasing problem is solved by applying constant value moveout correction to the data before the interpolation is performed. After finishing the interpolation, the inverse moveout correction is applied to the interpolated data using the same constant velocity. Through the application of our workflow to the synthetic OBC seismic data, we verified the effectiveness of the proposed workflow. In addition, we showed that the interpolation of field OBC data with severe spatial aliasing was successfully performed using our workflow.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1083-1091
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• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• Geophysics and Geophysical Exploration
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• v.17 no.2
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• pp.88-94
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• 2014

Due to mechanical failure or geographical accessibility, the seismic data can be partially missed. In addition, it can be coarsely sampled such as crossline of the marine streamer data. This seismic data that irregular sampled and spatial aliased may cause problems during seismic data processing. Accurate and efficient interpolation method can solve this problem. Futhermore, interpolation can save the acquisition cost and time by reducing the number of shots and receivers. Among various interpolation methods, the Matching Pursuit method can be applied to any sampling type which is regular or irregular. However, in case of using sinusoidal basis function, this method has a limitation in spatial aliasing. Therefore, in this study, we have developed wavelet based Matching Pursuit method that uses wavelet instead of sinusoidal function for the improvement of dealiasing performance. In addition, we have improved interpolation speed by using inner product instead of L-2 norm.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.62-76
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• 2023

Recently, machine learning (ML) techniques have been actively applied for seismic trace interpolation. However, because most research is based on training-inference strategies that treat missing trace gather data as a 2D image with a blank area, a sufficient number of fully sampled data are required for training. This study proposes trace interpolation using ML, which uses only irregularly sampled field data, both in training and inference, by modifying the training-inference strategies of trace-based interpolation techniques. In this study, we describe a method for constructing networks that vary depending on the maximum number of consecutive gaps in seismic field data and the training method. To verify the applicability of the proposed method to field data, we applied our method to time-migrated seismic data acquired from the Vincent oilfield in the Exmouth Sub-basin area of Western Australia and compared the results with those of the conventional trace interpolation method. Both methods showed high interpolation performance, as confirmed by quantitative indicators, and the interpolation performance was uniformly good at all frequencies.

• Geophysics and Geophysical Exploration
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• v.20 no.2
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• pp.78-87
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• 2017

For efficient data processing, trace interpolation and regularization techniques should be antecedently applied to the seismic data which were irregularly sampled with missing traces. Among many interpolation techniques, MWNI (Minimum Weighted Norm Interpolation) technique is one of the most versatile techniques and widely used to regularize seismic data because of easy extension to the high-order module and low computational cost. However, since it is difficult to interpolate spatially aliased data using this technique, model-constrained MWNI was suggested to compensate for this problem. In this paper, conventional MWNI and model-constrained MWNI modules have been developed in order to analyze their performance using synthetic data and validate the applicability to the field data. The result by using model-constrained MWNI was better in spatially aliased data. In order to verify the applicability to the field data, interpolation and regularization were performed for two field data sets, respectively. Firstly, the seismic data acquired in Ulleung Basin gas hydrate field was interpolated. Even though the data has very chaotic feature and complex structure due to the chimney, the developed module showed fairly good interpolation result. Secondly, very irregularly sampled and widely missing seismic data was regularized and the connectivity of events was quite improved. According to these experiments, we can confirm that the developed module can successfully interpolate and regularize the irregularly sampled field data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.139-142
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• 1996

움직임 보상형 프레임간 내삽기법은 다른 동영상 압축기법과 함께 사용함으로써, 그 압축효과를 더욱 향상시킬 수 있는 장점을 가지고 있다. 그러나 블록에 기반한 기존의 움직임 보상형 프레임간 내삽기법을 움직이는 물체의 경계에서 심한 블록효과를 보인다. 본 논문에서는 Quad-tree 기법을 이용해 화소단위의 움직임을 추정하고, 이를 이용해 드러난 영역과 가리워지는 영역을 정확 히 예측해 보상함으로써 움직이는 물체의 경계에서 나타나는 블럭효과를 제거했다. 그리고 제안된 방법에서는 순방향이나 역방향 움직임추정 시 송신측으로 부터 보내지는 움직임 벡터를 이용함으로써 움직임 추정을 위해 소요되는 계산량을 최소화하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.543-550
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• 2011

The gravity anomaly is a basic geophysical data applied in various fields such as geophysics, geodesy and national defense. In general, the gravity anomaly is used through a interpolation process based on the constructed database. The gravity variation, however, is appeared in various shapes depending on the topography and the density of the underground structures. Therefore, the interpolation could lead to a large differences if the gravity fields do not satisfy the assumptions on the signal behavior like linear or a certain degree polynomials. Furthermore, the interpolation does not reflect the physical characteristics of the gravity such as the harmonic condition. In this study, the gravity modeling using the plane Fourier series and radial basis functions are performed to overcome the problems in the usual interpolation. The results of the modeling is analyzed for the case of the gravity referenced navigation focused on the signal characteristics. Based on the study, it was found that the results from modeling are not much different to that from the interpolation in a smoothly varied area. In case of the highly varied area, however, a large differences are appeared among the three methods. Especially, the Fourier series shows the most smooth variations in the modeled gravity values while the highest variations appeared in the interpolation. Applying to the gravity referenced navigation, it was found that the modeling is more effective in calculation cost. It is considered that the results from this study provides a basis on effective modeling of the gravity fields in terms of the signal characteristics and resolution for various application fields.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.907-912
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• 1993

In this Paper we have proposed a new method to implement the interpolation of the functions, using a neural network. The architecture of neural network is a three-layer perceptron and the training algorithm is a modified error back propagation algorithm adding neurons to hidden layer. The interpolated functions are sin(7 X), 3rd order polynomial 0.5$\times$3_2$\times$2+X+2.5 and rectangular pulse 0.99 U (X-0.2) -0.99 U(X-0.8) +0.01, where U(X) is the unit step. The root mean squred errors of the interpolated functions are 0.00258, 0.00164 and 0.00116 respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.334-334
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• 2017

Representative particle sizes(RPS) are commonly used for particle size distribution of heterogeneous sediment such as bed material. RPS can provide not only information of mean characteristics of sediment, but also other properties like sorting, skewness, kurtosis. For sediment including sand and clay material, RPS is estimated through two steps. The first is experimental step for calculating weight of each size class, the next is interpolation step to get RPS by using the graph plot. At the second step, graph method known as direct reading of value along the interpolation line in the graph plot is commonly used. This method is often time-consuming job. In this study we developed a new program to get RPS by using MS Excel. Simple linear and semi-log interpolation are used. When compared with conventional graph method(direct reading), simple linear shows 5.31%, while semi-log 1.29% of relative difference. We developed MS Excel program for estimation of RPS automatically.