A Seismic Blind Deconvolution Algorithm Based on Bayesian Compressive Sensing

Compressive sensing in seismic signal processing is a construction of the unknown reflectivity sequence from the incoherent measurements of the seismic records. Blind seismic deconvolution is the recovery of reflectivity sequence from the seismic records, when the seismic wavelet is unknown. In this paper, a seismic blind deconvolution algorithm based on Bayesian compressive sensing is proposed. The proposed algorithm combines compressive sensing and blind seismic deconvolution to get the reflectivity sequence and the unknown seismic wavelet through the compressive sensing measurements of the seismic records. Hierarchical Bayesian model and optimization method are used to estimate the unknown reflectivity sequence, the seismic wavelet, and the unknown parameters (hyperparameters). The estimated result by the proposed algorithm shows the better agreement with the real value on both simulation and field-data experiments.

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SEISMIC SIGNAL PROCESSING FOR TRAJECTOR ASSESSMENT OF A MOVING VEHICLE

Автометрия ◽

10.15372/aut20180304 ◽

2018 ◽

Keyword(s):

Signal Processing ◽

Seismic Signal ◽

Moving Vehicle ◽

Seismic Signal Processing

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Research of Blind Deconvolution Algorithm Based on High-Order Statistics and Quantum Inspired GA

Lecture Notes in Computer Science - Advances in Natural Computation ◽

10.1007/11539117_67 ◽

2005 ◽

pp. 461-467 ◽

Cited By ~ 1

Author(s):

Jun-an Yang ◽

Bin Zhao ◽

Zhongfu Ye

Keyword(s):

Order Statistics ◽

Blind Deconvolution ◽

High Order ◽

Deconvolution Algorithm ◽

High Order Statistics

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Bayesian feature learning for seismic compressive sensing and denoising

Geophysics ◽

10.1190/geo2016-0373.1 ◽

2017 ◽

Vol 82 (6) ◽

pp. O91-O104 ◽

Cited By ~ 7

Author(s):

Georgios Pilikos ◽

A. C. Faul

Keyword(s):

Compressive Sensing ◽

State Of The Art ◽

Feature Learning ◽

Seismic Signal ◽

Basis Functions ◽

Sparse Signals ◽

Seismic Signals ◽

Process Factor ◽

Learned Features ◽

Fixed Basis

Extracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations. Compressive sensing (CS) is a framework that allows reconstruction of sparse signals from fewer measurements than conventional sampling rates. In seismic CS, the use of sparse transforms has some success; however, defining fixed basis functions is not trivial given the plethora of possibilities. Furthermore, the assumption that every instance of a seismic signal is sparse in any acquisition domain under the same transformation is limiting. We use beta process factor analysis (BPFA) to learn sparse transforms for seismic signals in the time slice and shot record domains from available data, and we use them as dictionaries for CS and denoising. Algorithms that use predefined basis functions are compared against BPFA, with BPFA obtaining state-of-the-art reconstructions, illustrating the importance of decomposing seismic signals into learned features.

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Blind deconvolution algorithm for restoration OCT images with diffraction limited resolution

10.1117/12.889731 ◽

2011 ◽

Author(s):

Alexander A. Moiseev ◽

Grigory V. Gelikonov ◽

Pavel A. Shilyagin ◽

Valentine M. Gelikonov

Keyword(s):

Blind Deconvolution ◽

Deconvolution Algorithm

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SPATIAL-TEMPORAL ANALYSIS OF SEISMIC WAVE-FIELD FROM THE DISTRIBUTED MINING EXPLOSION

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2019-4-1-141-148 ◽

2019 ◽

Vol 4 (1) ◽

pp. 141-148

Author(s):

Sergey Efimov

Keyword(s):

Wave Field ◽

Seismic Wave ◽

Temporal Analysis ◽

Seismic Signal ◽

Environmental Risks ◽

Time Analysis ◽

Seismic Recording ◽

Seismic Records ◽

Seismic Wave Field ◽

Spatial Temporal Analysis

The article presents a space-time analysis of the seismic wave from a distributed career explosion. The method of direct measurement based on the dynamic model of the wave field is used to form an image of the seismic wave field in the field of quarry explosions. The efficiency of the proposed method is shown by the example of experimental seismic recording processing. The program "Nelumbo" is used to visualize the seismic field, which on the basis of experimental seismic records allows to form an image of the wave field in the space of the hemisphere, the center of which corresponds to the position of the seismometer (registration point). The algorithm of the program "Nelumbo" is based on the Huygens – Fresnel principle and Kirchhoff's theorem. The algorithm of the program allows to allocate from record of a seismic signal frames of a certain duration and to form the image of a wave field in space of a solid angle dimension π. This approach can be used as a tool to analyze the nature of the development of disturbances in the environment and the analysis of environmental risks in the production of blasting.

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