Optimum suppression of coherent signals with linear moveout in seismic data

Geophysics ◽  
1984 ◽  
Vol 49 (3) ◽  
pp. 215-226 ◽  
Author(s):  
M. Simaan ◽  
P. L. Love

This paper presents a new technique for suppression of coherent signals with linear moveout in seismic data. This technique is implemented in two schemes which take an array of traces as input and produce either one single trace or another array of traces as output. Two seismic applications are discussed in detail. The first is in the area of separation of upward and downward travelling signals in vertical array seismology, and the second is concerned with the attenuation of ground roll in conventional land data and cable noise in marine data, respectively. In each case, an example of real seismic data is presented to illustrate the effectiveness of this technique.

Author(s):  
Raed M. Shubair ◽  
Abdulrahman S. Goian ◽  
Mohamed I. AlHajri ◽  
Ahmed R. Kulaib

2001 ◽  
Vol 2001 (1) ◽  
pp. 1-4
Author(s):  
Mu Luo ◽  
Brian J. Evans

2002 ◽  
Author(s):  
Gianni Matteucci ◽  
Linda J. Zimmerman ◽  
Michael M. Deal

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. A1-A6 ◽  
Author(s):  
Ming Ma ◽  
Rui Zhang ◽  
Sanyi Yuan

We have upgraded the conventional nonconvex optimization algorithm and addressed a new technique to estimate the acoustic impedance (AI) for attenuated seismic data based on the modified alternating direction method of multipliers (ADMM). To eliminate the discontinuity in the inverted AI profiles with the existing single-trace processing strategy, we construct a multichannel framework and a dimensional reduction operation is used with a brief matrix manipulation in the designed forward-simulation model. The [Formula: see text] norm constraint in the inversion function can assist to search a global optimal AI solution. In the promoted ADMM solving procedure, we invoke the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm and the generalized iterated-shrinkage algorithm to solve the relative suboptimization problems. Our new technique could recover an absolute AI from the nonstationary seismic data directly, even though this inversion problem is seriously ill-posed and nonlinear. The inverted AI profile will be satisfied with the assumption that the calculated reflectivity is comparatively sparse corresponding to its minimum [Formula: see text]-norm. Appending this adequate constraint term to the objective function is crucially significant in reducing the number of estimated AI. Due to the consideration of seismic attenuation in the equation, the inversion approach is deployed into the nonstationary reflection data avoiding the drawbacks brought by the energy-compensation processing (e.g., inverse [Formula: see text]-filtering). Using synthetic and field data, we determine the performance of our method.


2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S543-S543
Author(s):  
Satoshi Kimura ◽  
Keigo Matsumoto ◽  
Yoshio Imahori ◽  
Katsuyoshi Mineura ◽  
Toshiyuki Itoh

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