(untitled) (Communications in Computational Physics, 2020)
The stability and efficiency, especially the stability, are generally concerned issues in Q compensated reverse time migration (Q-RTM). The instability occurs because of the exponentially boosted high frequency ambient noise during the forward or backward seismic wavefield propagation. The regularization and low-pass filtering methods are two effective strategies to control the instability of the wave propagation in Q-RTM. However, the regularization parameters are determined experimentally, and the wavefield cannot be recovered accurately. The low-pass filtering method cannot balance the selection of cutoff frequency for varying Q values, and may damage the effective signals, especially when the signal-to-noise ratio (SNR) of the seismic data is low, the Q-RTM will be a highly unstable process. In order to achieve the purpose of stability, the selection of cutoff frequency will be small enough, which can cause great damage to the effective high frequency signals. In this paper, we present a stable Q-RTM algorithm based on the excitation amplitude imaging condition, which can compensate both the amplitude attenuation and phase dispersion. Unlike the existing Q-RTM algorithms enlarging the amplitude, the exponentially attenuated seismic wavefield will be used during both the forward and backward wavefield propagation of Q-RTM. Therefore, the new Q-RTM algorithm is relative stable, even for the low SNR seismic data. In order to show the accuracy and stability of our stable Q-RTM algorithm clearly, an example based on Graben model will be illustrated. Then, a realistic BP gas chimney model further demonstrates that the proposed method enjoys good stability and anti-noise performance compared with the traditional Q-RTM with amplitude amplification. Compare the Q-RTM images of these two models to the reference images obtained by the acoustic RTM with acoustic seismic data, the new Q-RTM results match the reference images quite well. The proposed method is also tested using a field seismic data, the result shows the effectiveness of our proposed method.