Wavelet estimation and blind deconvolution of realistic synthetic seismic data by log spectral averaging

Tria, M.; Larue, Anthony; Mars, J.I.; Baan, Mirko van der

doi:10.1190/1.2792879

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Seismic deconvolution is a standard procedure in seismic data processing in which the effects of a source wavelet are removed as much as possible [1], which also attenuates reverberations and short-period multiples. Deconvolution techniques are widely used in seismic exploration [2], [3] and seismology applications [4]- [6]. Knowledge of the source wavelet is key, so a challenging problem in seismic deconvolution is blind deconvolution where the blurring kernel, i.e., the seismic source wavelet, is unknown and must be estimated [7].…”

Section: Introductionmentioning

confidence: 99%

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

et al. 2019

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In this work, an efficient numerical scheme is presented for seismic blind deconvolution in a multichannel scenario. The proposed method iterate with two steps: first, wavelet estimation across all channels and second, refinement of the reflectivity estimate simultaneously in all channels using sparse deconvolution. The reflectivity update step is formulated as a basis pursuit denoising problem and a sparse solution is obtained with the spectral projected-gradient algorithm -faithfulness to the recorded traces is constrained by the measured noise level. Wavelet re-estimation has a closed form solution when performed in the frequency domain by finding the minimum energy wavelet common to all channels. Nothing is assumed known about the wavelet apart from its time duration. In tests with both synthetic and real data, the method yields sparse reflectivity series and stable wavelet estimates results compared to existing methods with significantly less computational effort.

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Section: Introductionmentioning

confidence: 99%

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

et al. 2019

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“…To evaluate the performance of Short-Time Homomorphic Wavelet Estimation (STHWE), we compare its results with the log-spectral averaging (LSA) (Otis & Smith 1977, Tria et al 2007) and with the true wavelet in the synthetic example and with the first arrival in the field dataset. We also include a kurtosis-based method to estimate a constant-phase wavelet (van der Baan 2008), as it gives an independent comparison based on an alternative approach using different statistical assumptions.…”

Section: Resultsmentioning

confidence: 99%

Short-time homomorphic wavelet estimation

Herrera

Baan

2012

J. Geophys. Eng.

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Successful wavelet estimation is an essential step for seismic methods like impedance inversion, analysis of amplitude variations with offset and full waveform inversion. Homomorphic deconvolution has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. Associated disadvantages like phase unwrapping and restrictions of sparsity in the reflectivity function limit its application. We explore short-time homomorphic wavelet estimation as a combination of the classical homomorphic analysis and logspectral averaging. The introduced method of log-spectral averaging using a short-term Fourier transform increases the number of sample points, thus reducing estimation variances. We apply the developed method on synthetic and real data examples and demonstrate good performance.PACS numbers: 91.30.Bi, 93.85.Rt

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“…Метод носит название Blind deconvolution [Stockham et al, 1975], и к нему проявляют значитель-ный интерес исследователи в различных областях нау-ки и техники (см., напр. : [Petropulu, Nikias, 1993;Haykin, 1994;Kundur, Hatzinakos, 1996;Sarkar et al, 1998;Kaprzak, Okazaki, 2003;Douglas et al, 2007;Tria et al, 2007;van Vossen, Trampert, 2007]). …”

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Homomorphic filtering and blind deconvolution

2015

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В области фильтрации продолжительное время развивается направление, позволяющее разделять форму наблюдаемого сигнала на мультипликативные составляющие. При такой фильтрации не ис-пользуется или минимально используется информация о природе разделяемых составляющих, а так-же не требуется полного знания о процессах, которые их формируют. В результате удается восполь-зоваться эвристическими моделями при определении формы сигнала. Кроме того, определяемые посредством фильтрации составляющие служат основой для изучения процессов, приводящих к наб-людаемым изменениям сигнала. Также в зависимости от используемых моделей эти составляющие применяются для корректировки формы сигнала или являются входными данными для решения об-ратных задач. В последнем случае можно опираться на известные теоретические решения либо на некоторые эвристические конструкции, служащие основой для изучения процессов формирования регистрируемых сигналов. Таким образом, развиваемое направление обладает большими перспекти-вами в области сейсморазведки, особенно с учетом возрастающей сложности задач и требований к повышению точности прогноза характеристик изучаемой среды. Одним из наиболее известных мето-дов, основанных на идеях гомоморфной фильтрации, является поверхностно-согласованная фильтра-ция, представляющая собой стандартную процедуру всех современных комплексов обработки сейс-мических данных. Гомоморфные преобразования, кепстральный анализ, деконволюция, мультипликативные составляющие формы сигнала, объектно-ориентированные обратные задачи HOMOMORPHIC FILTERING AND BLIND DECONVOLUTION G.М. MitrofanovTrofimuk Institute of Petroleum Geology and Geophysics SB RAS 630090, Novosibirsk, prosp. Acad. Koptyug, 3, Russia; So long time in an area of filtering researches a big stream is development. It provides to separate a form of observed signals on multiplied or convoled components. In the filtering does not use or use minimum information about separated components nature as well as don't need full knowledge about processes which its forming. All give a tripled effect. The first, heuristics models can be applied for a signal form presentation. The second, components are determined by such filtration are the bases for a studying of processes which can available these variations of observed signals form. The third, separated components take account on used models are applied to the signals form correction or entrance data for inverse problems solution. In the last case known theoretical solutions or some heuristic design which are basis for studying of processes of recorded signals formation can be used. Thus, developing the direction has great prospects in seismic exploration especially given the increasing complexity of tasks and requirements to improve the accuracy of features prediction of studied medium. One of the most well-known methods, which is based on the homomorphic filtration ideas, is the surface consistent filtering representing the standard procedure of all modern package of seismic data processing.Homomorphic transforms, cepstral an...

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Wavelet estimation and blind deconvolution of realistic synthetic seismic data by log spectral averaging

Cited by 5 publications

References 3 publications

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

Short-time homomorphic wavelet estimation

Homomorphic filtering and blind deconvolution

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