$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

What can machine learning do for seismic data processing? An interpolation application

Geophysics, v.82 no.3, 2017년, pp.V163 - V177  

Jia, Yongna (Harbin Institute of Technology, Department of Mathematics, Harbin, China..) ,  Ma, Jianwei (Harbin Institute of Technology, Department of Mathematics, Harbin, China..)

Abstract AI-Helper 아이콘AI-Helper

Machine learning (ML) systems can automatically mine data sets for hidden features or relationships. Recently, ML methods have become increasingly used within many scientific fields. We have evaluated common applications of ML, and then we developed a novel method based on the classic ML method of ...

참고문헌 (52)

  1. Androutsopoulos, I., G. Paliouras, V. Karkaletsis, G. Sakkis, C. D. Spyropoulos, and P. Stamatopoulos, 2000, Learning to filter spam e-mail: A comparison of a naive Bayesian and a memory-based approach: Proceedings of the Workshop on Machine Learning and Textual Information Access, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, 1-13. 

  2. Banerjee, T.P., Das, S.. Multi-sensor data fusion using support vector machine for motor fault detection. Information sciences, vol.217, 96-107.

  3. Bobadilla, J., Ortega, F., Hernando, A., Gutierrez, A.. Recommender systems survey. Knowledge-based systems, vol.46, 109-132.

  4. Bonar, David, Sacchi, Mauricio. Denoising seismic data using the nonlocal means algorithm. Geophysics, vol.77, no.1, A5-A8.

  5. 10.1145/312129.312241 

  6. Burges, Christopher J.C.. A Tutorial on Support Vector Machines for Pattern Recognition. Data mining and knowledge discovery, vol.2, no.2, 121-167.

  7. Cai, J.F., Ji, H., Shen, Z., Ye, G.B.. Data-driven tight frame construction and image denoising. Applied and computational harmonic analysis, vol.37, no.1, 89-105.

  8. Chang, C.C., and C.J. Lin, 2001, Libsvm: A library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm, accessed 28 February 2017. 

  9. Chaplot, Sandeep, Patnaik, L.M., Jagannathan, N.R.. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical signal processing and control, vol.1, no.1, 86-92.

  10. Chiu, Stephen K.. Multidimensional interpolation using a model-constrained minimum weighted norm interpolation. Geophysics, vol.79, no.5, V191-V199.

  11. Earth soundings analysis: Processing versus inversion Claerbout J. F. 1992 

  12. 1049-5258 Advances in Neural Information Processing Systems Drucker H. 9 1997 

  13. DUTTON, DAVID M., CONROY, GERARD V.. A review of machine learning. The Knowledge engineering review, vol.12, no.4, 341-367.

  14. Gao, Jianjun, Sacchi, Mauricio D., Chen, Xiaohong. A fast reduced-rank interpolation method for prestack seismic volumes that depend on four spatial dimensions. Geophysics, vol.78, no.1, V21-V30.

  15. Gülünay, Necati. Seismic trace interpolation in the Fourier transform domain. Geophysics, vol.68, no.1, 355-369.

  16. Guzella, T.S., Caminhas, W.M.. A review of machine learning approaches to Spam filtering. Expert systems with applications, vol.36, no.7, 10206-10222.

  17. Hajj Hassan, Ali, Lambert-Lacroix, Sophie, Pasqualini, Francois. Real-Time Fault Detection in Semiconductor Using One-Class Support Vector Machines. International journal of computer theory and engineering, vol.7, no.3, 191-196.

  18. Haykin, S., 2004, Neural networks: A comprehensive foundation, 2nd ed.: Prentice Hall. 

  19. Helmy, T., Fatai, A., Faisal, K.. Hybrid computational models for the characterization of oil and gas reservoirs. Expert systems with applications, vol.37, no.7, 5353-5363.

  20. 10.1111/gji.2008.173.issue-1 

  21. Huang, Cheng-Lung, Chen, Mu-Chen, Wang, Chieh-Jen. Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, vol.33, no.4, 847-856.

  22. Huang, W., Nakamori, Y., Wang, S.-Y.. Forecasting stock market movement direction with support vector machine. Computers & operations research, vol.32, no.10, 2513-2522.

  23. Jia, Y., Yu, S., Liu, L., Ma, J.. A fast rank-reduction algorithm for three-dimensional seismic data interpolation. Journal of applied geophysics, vol.132, 137-145.

  24. Keys, R.. Cubic convolution interpolation for digital image processing. IEEE transactions on acoustics, speech, and signal processing, vol.29, no.6, 1153-1160.

  25. Kreimer, Nadia, Sacchi, Mauricio D.. A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation. Geophysics, vol.77, no.3, V113-V122.

  26. Kreimer, Nadia, Stanton, Aaron, Sacchi, Mauricio D.. Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction. Geophysics, vol.78, no.6, V273-V284.

  27. 10.1190/segam2013-1165.1 

  28. Kwiatkowska, E.J., Fargion, G.S.. Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, vol.41, no.12, 2844-2860.

  29. Liang, Jingwei, Ma, Jianwei, Zhang, Xiaoqun. Seismic data restoration via data-driven tight frame. Geophysics, vol.79, no.3, V65-V74.

  30. Lim, Jong-Se. Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. Journal of petroleum science & engineering, vol.49, no.3, 182-192.

  31. Liu, Bin, Sacchi, Mauricio D.. Minimum weighted norm interpolation of seismic records. Geophysics, vol.69, no.6, 1560-1568.

  32. Ma, Jianwei. Three-dimensional irregular seismic data reconstruction via low-rank matrix completion. Geophysics, vol.78, no.5, V181-V192.

  33. Mohandes, M.A., Halawani, T.O., Rehman, S., Hussain, Ahmed A.. Support vector machines for wind speed prediction. Renewable energy, vol.29, no.6, 939-947.

  34. Murthy, Sreerama K.. Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data mining and knowledge discovery, vol.2, no.4, 345-389.

  35. Naghizadeh, Mostafa, Sacchi, Mauricio D.. Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data. Geophysics, vol.75, no.6, WB189-WB202.

  36. Naghizadeh, Mostafa, Sacchi, Mauricio. Multidimensional de-aliased Cadzow reconstruction of seismic records. Geophysics, vol.78, no.1, A1-A5.

  37. Ni, Karl S., Nguyen, Truong Q.. Image Superresolution Using Support Vector Regression. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.16, no.6, 1596-1610.

  38. Oropeza, Vicente, Sacchi, Mauricio. Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics, vol.76, no.3, V25-V32.

  39. Ravisankar, P., Ravi, V., Raghava Rao, G., Bose, I.. Detection of financial statement fraud and feature selection using data mining techniques. Decision support systems, vol.50, no.2, 491-500.

  40. Reinartz, Thomas. A Unifying View on Instance Selection. Data mining and knowledge discovery, vol.6, no.2, 191-210.

  41. Sacchi, M.D., Ulrych, T.J., Walker, C.J.. Interpolation and extrapolation using a high-resolution discrete Fourier transform. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.46, no.1, 31-38.

  42. Santamaría-Bonfil, G., Reyes-Ballesteros, A., Gershenson, C.. Wind speed forecasting for wind farms: A method based on support vector regression. Renewable energy, vol.85, 790-809.

  43. Smola, Alex J., Schölkopf, Bernhard. A tutorial on support vector regression. Statistics and computing, vol.14, no.3, 199-222.

  44. Spitz, S.. Seismic trace interpolation in theF-Xdomain. Geophysics, vol.56, no.6, 785-794.

  45. Trad, Daniel. Five-dimensional interpolation: Recovering from acquisition constraints. Geophysics, vol.74, no.6, V123-V132.

  46. 10.1190/1.3513645 

  47. 10.1007/978-1-4757-2440-0 

  48. Wu, Chun-Hsin, Ho, Jan-Ming, Lee, D.T.. Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.5, no.4, 276-281.

  49. Xu, Sheng, Zhang, Yu, Lambaré, Gilles. Antileakage Fourier transform for seismic data regularization in higher dimensions. Geophysics, vol.75, no.6, WB113-WB120.

  50. 10.3934/ipi 

  51. Yu, Siwei, Ma, Jianwei, Zhang, Xiaoqun, Sacchi, Mauricio D.. Interpolation and denoising of high-dimensional seismic data by learning a tight frame. Geophysics, vol.80, no.5, V119-V132.

  52. 10.3997/2214-4609.20141500 

관련 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로