Dissertation > Excellent graduate degree dissertation topics show

High-order Statistics Based Realization of Seismic Relecticity and Wavelet

Author: GaoWei
Tutor: LiuXiWu
School: Ocean University of China
Course: Earth Exploration and Information Technology
Keywords: wavelet estimation seismic deconvolution relecticity high-order statistics independent component analysis
CLC: P631.4
Type: Master's thesis
Year: 2008
Downloads: 281
Quote: 2
Read: Download Dissertation

Abstract


Both the wavelet estimation and the seismic deconvolution are studied in this paper. The wavelet estimation and seismic deconvolution are always based on the assumpsition of Guassality and whiten noise to relecticity and minimum phase to the seismic wavelet. It commonly has a good effect in the practical application, but it can’t be sure that these assumpsitions are always accurate. However, the wavelet estimation based on the High-order statistic can eliminate the assumpsition of Guassality and whiten noise to relecticity and minimum phase to the seismic wavelet. Furthermore, the relecticity can be separated. Then the seismic deconvolution can be achieved. The paper studies the non-minimum phase seismic wavelet estimation and seismic deconvolution based on the production of fore people. At the same time, applying independent component anaysis to the blind deconvolution of seismic data in a creative way. Completed primarily below work:1. Neglecting noise, achieves the minimum phase seismic wavelet estimation and seismic deconvolution.2. Neglecting noise, applying bispectrum to recover the no minimum wavelet, and then applying the homomorphic deconvolution method to realize the deconvolution to obtain the relecticity.3. Neglecting noise, making use of time lagged version matrix of convolved signal and seismic wavelet banded convolving mixture matrix to construct a basic ICA model. By applying FastICA algorithm, and combining the banded property as a prior information, giving rised to a banded ICA algorithm(B-ICA), more reflectivity series are produces as many as the dimension of the seismic wavelet filter, and finally one best independent component can be extracted from the candinate solutions by additional information from the seismic convolution model.4. Neglecting noise, changing the seismic record from time realm to complex cepstrum realm to transform the common seismic model to the basic ICA model. By applying the FastICA algorithm, separating the the seismic wavelet and reflectivity and changing the result back to the time realm.The model and real seismic data mumerical examples all shows that the stasticstical deconvolution based on ICA can inverse blindly the wavelet and the reflectivity at the same time with no assumpsition of Guassality and whiten noise to relecticity, and no minimum phase to seismic wavelet. The algorithms based on ICA refered here can slove the seismic signals blind deconvolution effectively and worth doing more researchs.

Related Dissertations

  1. Paradigm Design and Algorithm Research for P300-based BCI,TP334.7
  2. Based on odor analysis equipment malfunction detection method,TB17
  3. Based on digital visual spelling - Listen combined stimulation ERP Study,R318.0
  4. Based Cognitive Brain Auditory Attention - Computer Interface,R318.0
  5. A Study on Spatial Filtering and Feature Extraction Methods in Multi-Task Brain-Computer Interfaces,TP11
  6. Research on Analysis & Recognition Method for Multi-motor ERD/ERS Signal in BCI System,TN911.6
  7. Research on Seismic Blind De-convolution Method Based on BSS,P631.4
  8. Independent Component Analysis Theory and It’s Application,P631.443
  9. Practical Online Brain-Computer Interface System Based on Motion-onset Visual Responses,R318.0
  10. Research on Classification Algorithms and BCI Based on the Left and Right Motor Imagery,TP11
  11. Non-stationary Vibration Signal Analysis of Rotating Machinery Based on Blind Source Separation,TH165.3
  12. Development and Application of Near-infrared Spectroscopy,TP391.41
  13. Group Independent Component Analysis of Brain Functional Networks,R318
  14. Blind Sources Separation and Digital Image Processing,TP391.41
  15. The Applications of Independent Component Analysis in Music Signal Processing,TN912.3
  16. Blind Source Separation Based on Neural Networks,TN911.7
  17. Denoising method based on wavelet transform and independent component analysis study,TN911.4
  18. Independent Component Analysis and Its Application to Vehicle Condition Monitoring,TP274
  19. Batch Process Monitoring Based on Multi-way Independent Principal Component Analysis Methods,TP277
  20. Research on Block Matching Motion Estimation Algorithm in Video Compression,TP391.41
  21. The FECG Detection Algorithm and Implementation Based on BSS,TN911.23

CLC: > Astronomy,Earth Sciences > Geology > Geology, mineral prospecting and exploration > Geophysical exploration > Seismic exploration
© 2012 www.DissertationTopic.Net  Mobile