Joint Inversion and Hyperbolic Median Filtering for Simultaneous Source Data Separation

Author(s):  
A. Tarasov ◽  
A. Shuvalov ◽  
V. Ignatev ◽  
A. Konkov ◽  
B. Kashtan ◽  
...  
Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. WA147-WA158 ◽  
Author(s):  
Ivan Vasconcelos ◽  
James Rickett

Estimation of seismic velocities and subsurface reservoir properties in deep, complex, geologic environments calls for the coordination of new acquisition technology with novel depth-domain inversion methods. Here, we propose a method for inversion of subsurface reflectivity image gathers that jointly relies on broadband data acquired by multimeasurement methods (vector-acoustic data; pressure and its gradients) from monopole (pressure) and dipole (e.g., gradient) sources. Our inversion retrieves depth-domain extended images (EIs), which represent the full nonlinear reflectivity operators with pseudosources and pseudoreceivers within the subsurface, as a function of time or frequency. Based on recent advances in interferometry by multidimensional deconvolution (MDD), we present two MDD-based imaging conditions for an EI inversion. One imaging condition consists of deconvolving correlation-based EIs with the so-called joint point-spread function (JPSF). These methods can, in principle, account for imaging primaries as well as internal and free-surface multiples. Because it is based on MDD, our JPSF approach can fully account for blended/simultaneous-source data in imaging with no need to deblend/separate the simultaneous-source data prior to imaging. Using dual-source vector-acoustic data, we describe how the EI JPSF system is constructed by separating source and receiver wavefields from receiver-side upgoing and ghost data, from pressure and gradient sources. With numerical examples, we demonstrate how the method successfully inverts for EIs representing subsurface reflectivity, while benefiting from the increase in temporal and spatial bandwidth brought on by the joint use of dual-source vector-acoustic data. Although here we focus on broadband streamer seismic applications, our joint wavefield inversion approach provides a framework for jointly imaging data from multiple experiments of any kind (e.g., surface and borehole, active and passive) with acoustic, elastic, and electromagnetic fields.


Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. A9-A12 ◽  
Author(s):  
Kees Wapenaar ◽  
Joost van der Neut ◽  
Jan Thorbecke

Deblending of simultaneous-source data is usually considered to be an underdetermined inverse problem, which can be solved by an iterative procedure, assuming additional constraints like sparsity and coherency. By exploiting the fact that seismic data are spatially band-limited, deblending of densely sampled sources can be carried out as a direct inversion process without imposing these constraints. We applied the method with numerically modeled data and it suppressed the crosstalk well, when the blended data consisted of responses to adjacent, densely sampled sources.


Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. V1-V11 ◽  
Author(s):  
Amr Ibrahim ◽  
Mauricio D. Sacchi

We adopted the robust Radon transform to eliminate erratic incoherent noise that arises in common receiver gathers when simultaneous source data are acquired. The proposed robust Radon transform was posed as an inverse problem using an [Formula: see text] misfit that is not sensitive to erratic noise. The latter permitted us to design Radon algorithms that are capable of eliminating incoherent noise in common receiver gathers. We also compared nonrobust and robust Radon transforms that are implemented via a quadratic ([Formula: see text]) or a sparse ([Formula: see text]) penalty term in the cost function. The results demonstrated the importance of incorporating a robust misfit functional in the Radon transform to cope with simultaneous source interferences. Synthetic and real data examples proved that the robust Radon transform produces more accurate data estimates than least-squares and sparse Radon transforms.


Geophysics ◽  
2021 ◽  
pp. 1-56
Author(s):  
Breno Bahia ◽  
Rongzhi Lin ◽  
Mauricio Sacchi

Denoisers can help solve inverse problems via a recently proposed framework known as regularization by denoising (RED). The RED approach defines the regularization term of the inverse problem via explicit denoising engines. Simultaneous source separation techniques, being themselves a combination of inversion and denoising methods, provide a formidable field to explore RED. We investigate the applicability of RED to simultaneous-source data processing and introduce a deblending algorithm named REDeblending (RDB). The formulation permits developing deblending algorithms where the user can select any denoising engine that satisfies RED conditions. Two popular denoisers are tested, but the method is not limited to them: frequency-wavenumber thresholding and singular spectrum analysis. We offer numerical blended data examples to showcase the performance of RDB via numerical experiments.


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