Dynamic Convolution-based Misfit Function for Time Domain Full Waveform Inversion

Under the same propagation operator, the precision of the seismic wavelet determines whether synthetic data can match field data accurately. By constructing the convolution wavefield objective function, the source difference between simulated and observed data is ignored, thus avoiding the source wavelet estimation. Theoretically, this process has no restriction on the accuracy of the wavelet, and a multi-scale inversion strategy can be implemented by using a source wavelet with different dominant frequencies. When the propagation operator used does not conform with reality, even if both the wavelet and the parameter mode are accurate, it is impossible to simulate the full waveforms of the observed data. Meanwhile, the difference in the Green function brings new discrepancy to the convolution wavefield objective function and affects the final inversion results. The method presented in this paper is based on the convolution wavefield objective function. On the basis of an original single reference trace, we discuss the performance of the convolution wavefield-type objective function under multiple reference traces selected from different offsets. After introducing the changed wavefield information, the objective function has the ability to adapt to different types of data. The analysis shows that nonlinearity is significantly increased after introducing the different wavefield information and also increases with inversion frequency. Even for anisotropic data, it is still possible to give a relatively accurate structure at the low-frequency stage, which shows that the wavefield information from different offsets can help to weaken the artifacts introduced by operator mismatch, thus providing more possibilities for the application of acoustic wave equation inversion.