Compared with first-order surface-related multiples from marine data,the onshore internal multiples are weaker and are always combined with a hazy and occasionally strong interference pattern.It is usually difficult t...Compared with first-order surface-related multiples from marine data,the onshore internal multiples are weaker and are always combined with a hazy and occasionally strong interference pattern.It is usually difficult to discriminate these events from complex targets and highly scattering overburdens,especially when the primary energy from deep layers is weaker than that from shallow layers.The internal multiple elimination is even more challenging due to the fact that the velocity and energy difference between primary reflections and internal multiples is tiny.In this study,we propose an improved method which formulates the elimination of the internal multiples as an optimization problem and develops a convolution factor T.The generated internal multiples at all interfaces are obtained using the convolution factor T through iterative inversion of the initial multiple model.The predicted internal multiples are removed from seismic data through subtraction.Finally,several synthetic experiments are conducted to validate the effectiveness of our approach.The results of our study indicate that compared with the traditional virtual events method,the improved method simplifies the multiple prediction process in which internal multiples generated from each interface are built through iterative inversion,thus reducing the calculation cost,improving the accuracy,and enhancing the adaptability of field data.展开更多
基金the National Natural Science Foundation of China under Grant Nos.41974116 and 41930431Local Universities Reformation and Development Personnel Training Supporting Project from Central Authorities under Grant No.140119001 for supporting this work
文摘Compared with first-order surface-related multiples from marine data,the onshore internal multiples are weaker and are always combined with a hazy and occasionally strong interference pattern.It is usually difficult to discriminate these events from complex targets and highly scattering overburdens,especially when the primary energy from deep layers is weaker than that from shallow layers.The internal multiple elimination is even more challenging due to the fact that the velocity and energy difference between primary reflections and internal multiples is tiny.In this study,we propose an improved method which formulates the elimination of the internal multiples as an optimization problem and develops a convolution factor T.The generated internal multiples at all interfaces are obtained using the convolution factor T through iterative inversion of the initial multiple model.The predicted internal multiples are removed from seismic data through subtraction.Finally,several synthetic experiments are conducted to validate the effectiveness of our approach.The results of our study indicate that compared with the traditional virtual events method,the improved method simplifies the multiple prediction process in which internal multiples generated from each interface are built through iterative inversion,thus reducing the calculation cost,improving the accuracy,and enhancing the adaptability of field data.
