2021InterpretationRequires access

High-resolution fixed-point seismic inversion

Song Pei, Xingyao Yin, Zhaoyun Zong, Kun Li

Open publisher page 4 citations

Abstract

Resolution improvement always presents as the crucial task in geologic inversion. Band-limited characteristics of seismic data and noise make seismic inversion complicated. Specifically, geologic inversion suffers from the deficiency of low- and high-frequency components. We have developed the fixed-point seismic inversion method to alleviate these issues. The problem of solving the objective function is transformed into the problem of finding the fixed point of the objective function. Concretely, a recursive formula between seismic signal and reflection coefficient is established, which is characterized by good convergence and verified by model examples. The error between the model value and the inverted value is reduced to approximately zero after a few iterations. The model examples show that in either case, that is, the seismic traces are noise-free or with a little noise, the model value can almost be duplicated. Even if the seismic trace is accompanied by moderate noise, optimal inverted results can still be obtained with our method. The initial model constraint is further introduced into the objective function to increase the low-frequency component of the inverted results by adding prior information into the target function. The singular value decomposition method is applied to the inversion framework, thus making a high improvement of antinoise ability. Finally, the synthetic models and seismic data are investigated following our method. The inverted results obtained from the fixed-point seismic inversion are compared with those obtained from the conventional seismic inversion, and it is found that the former has a higher resolution than the latter.

About this research paper

What this paper is about

Resolution improvement always presents as the crucial task in geologic inversion. Band-limited characteristics of seismic data and noise make seismic inversion complicated. Specifically, geologic inversion suffers from the deficiency of low- and high-frequency components. We have developed the fixed-point seismic inversion method to alleviate these issues. The problem of solving the objective function is transformed into the problem of finding the fixed point of the objective function. Concretely, a recursive formula between seismic signal and reflection coefficient is established, which is characterized by good convergence and verified by model examples. The error between the model value and the inverted value is reduced to approximately zero after a few iterations. The model examples show that in either case, that is, the seismic traces are noise-free or with a little noise, the model value can almost be duplicated. Even if the seismic trace is accompanied by moderate noise, optimal inverted results can still be obtained with our method. The initial model constraint is further introduced into the objective function to increase the low-frequency component of the inverted results by adding prior information into the target function. The singular value decomposition method is applied to the inversion framework, thus making a high improvement of antinoise ability. Finally, the synthetic models and seismic data are investigated following our method. The inverted results obtained from the fixed-point seismic inversion are compared with those obtained from the conventional seismic inversion, and it is found that the former has a higher resolution than the latter.

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Available abstract

Resolution improvement always presents as the crucial task in geologic inversion. Band-limited characteristics of seismic data and noise make seismic inversion complicated. Specifically, geologic inversion suffers from the deficiency of low- and high-frequency components. We have developed the fixed-point seismic inversion method to alleviate these issues. The problem of solving the objective function is transformed into the problem of finding the fixed point of the objective function. Concretely, a recursive formula between seismic signal and reflection coefficient is established, which is characterized by good convergence and verified by model examples. The error between the model value and the inverted value is reduced to approximately zero after a few iterations. The model examples show that in either case, that is, the seismic traces are noise-free or with a little noise, the model value can almost be duplicated. Even if the seismic trace is accompanied by moderate noise, optimal inverted results can still be obtained with our method. The initial model constraint is further introduced into the objective function to increase the low-frequency component of the inverted results by adding prior information into the target function. The singular value decomposition method is applied to the inversion framework, thus making a high improvement of antinoise ability. Finally, the synthetic models and seismic data are investigated following our method. The inverted results obtained from the fixed-point seismic inversion are compared with those obtained from the conventional seismic inversion, and it is found that the former has a higher resolution than the latter.

Key concepts: Inversion (geology), Seismic inversion, Singular value decomposition, Geology, Algorithm, Seismology, Mathematical optimization, Computer science

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