Surface-consistent Seismic Data Amplitude Correction Via Learning from Synthetic Models Based On Waveform Modeling

We test the capability of an inverse scattering algorithm for imaging noisy seismic data. The algorithm does not require a velocity model or any other a priori information about the medium under investigation. We use three different geometries which capture different types of one-dimensional media with variable velocity. We show that the algorithm can precisely locate the interfaces and discover the correct velocity changes at those interfaces under moderate noise condition. When the signal to noise ratio is too small, the data is de-noised using a threshold filter and then imaged with excellent results. KEYWORDS: Seismic Imaging, Inversion, Amplitude Correction, Scattering Theory, Noise, Threshold Filter. 2000 MATHEMATICS SUBJECT CLASSIFICATION 86A22, 35J05, 35R30.

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Amplitude Correction in Separating P- and S-waves in Elastic Seismic Data

Near Surface 2009 - 15th EAGE European Meeting of Environmental and Engineering Geophysics ◽

10.3997/2214-4609.20147106 ◽

2009 ◽

Author(s):

R.J. Sun ◽

C.L. Yang

Keyword(s):

Seismic Data ◽

S Waves ◽

Amplitude Correction

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Sparse-promoting full-waveform inversion based on online orthonormal dictionary learning

Geophysics ◽

10.1190/geo2015-0632.1 ◽

2017 ◽

Vol 82 (2) ◽

pp. R87-R107 ◽

Cited By ~ 13

Author(s):

Lingchen Zhu ◽

Entao Liu ◽

James H. McClellan

Keyword(s):

Sparse Representation ◽

Seismic Data ◽

Waveform Inversion ◽

Full Waveform Inversion ◽

Training Method ◽

Full Waveform ◽

High Resolution Images ◽

Complex Features ◽

Adaptive Transform ◽

Synthetic Models

Full-waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between recorded and calculated seismic data. We have attacked this misfit successfully with the Gauss-Newton method and sparsity-promoting regularization based on fixed multiscale transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical transforms with predefined dictionaries to achieve sparse representation, we developed an adaptive transform called the sparse orthonormal transform (SOT), whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations in the entire model perturbation. The complexity of the training method is proportional to the cube of the number of samples in one small patch. By incorporating compressive subsampling and the adaptive SOT-based representation into the Gauss-Newton least-squares problem for each FWI iteration, the model perturbation can be recovered after an [Formula: see text]-norm sparsity constraint is applied on the SOT coefficients. Numerical experiments on synthetic models determined that the SOT-based sparsity-promoting regularization can provide robust FWI results with reduced computation.

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Estimation of Sedimentary Basin Depth Using the Hybrid Technique for Gravity Data

VNU Journal of Science Mathematics - Physics ◽

10.25073/2588-1124/vnumap.4203 ◽

2017 ◽

Vol 33 (2) ◽

Author(s):

Pham Thanh Luan ◽

Do Duc Thanh

Keyword(s):

Seismic Data ◽

Depth Distribution ◽

Sedimentary Basin ◽

Gravity Data ◽

Theoretical Models ◽

Data Interpretation ◽

Hybrid Technique ◽

Space Domain ◽

Synthetic Models ◽

Computation Speed

Abstract: In this paper we present the rapid method for determining the depth distribution of a sedimentary basin by combining the FFT-based and space domain techniques in gravity data interpretation. The method is tested on two 3D synthetic models which density contrast is constant and exponential variation with depth. Then, the method is applied to determine the depth distribution of Nam Con Son sedimentary basin in Vietnam. The obtained results coincide well with theoretical models and seismic data. The computation speed of the method is much faster than that of space domain technique.

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