High-resolution time-frequency decomposition with adaptive filter
عنوان دوره: نوزدهمین کنفرانس ژئوفیزیک ایران
کد مقاله : 1791-NIGS
نویسندگان
1دانشگاه فردوسی مشهد
2لرزه شناسی - موسسه ژئوفیزیک دانشگاه تهران
چکیده
The success of seismic attributes analysis depends on the resolution of calculated attributes and robustness of the method against noise-contaminated data. Conventional methods are sensitive to errors in data, which demands the filtering process. Conventional filtering methods try to increase the SNR at the cost of losing spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time-frequency decomposition which is stabilized for random noise normal distribution. The procedure begins by using Sparsity-based, adaptive S-transform to regularize abrupt variations in the frequency content of the non-stationary signals. An adaptive filter is then utilized to the previously sparsified time-frequency spectrum. The proposed zero adaptive filter enhances the high amplitude frequency components while suppressing the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in the estimation of instantaneous attributes through studying synthetic signals. Seismic attributes estimated by the proposed method are superior to the conventional ones in terms of robustness and high-resolution viewpoint.
کلیدواژه ها
Title
High-resolution time-frequency decomposition with adaptive filter
Authors
Mohsen Kazemnia Kakhki, Kamal Aghazade, Peyman Poor Moghadam
Abstract
The success of seismic attributes analysis depends on the resolution of calculated attributes and robustness of the method against noise-contaminated data. Conventional methods are sensitive to errors in data, which demands the filtering process. Conventional filtering methods try to increase the SNR at the cost of losing spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time-frequency decomposition which is stabilized for random noise normal distribution. The procedure begins by using Sparsity-based, adaptive S-transform to regularize abrupt variations in the frequency content of the non-stationary signals. An adaptive filter is then utilized to the previously sparsified time-frequency spectrum. The proposed zero adaptive filter enhances the high amplitude frequency components while suppressing the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in the estimation of instantaneous attributes through studying synthetic signals. Seismic attributes estimated by the proposed method are superior to the conventional ones in terms of robustness and high-resolution viewpoint.
Keywords
Seismic attributes analysis, Time-frequency decomposition, zero adaptive filter