Abstract: | 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. |