Multiscale transform based seismic reflectivity inversion using convolutional neural network

Abstract: The Multiscale Fourier Transform of a seismic trace performs time-frequency analyses over a range of window lengths. The variation in window length captures local and global relative amplitudes between events, thereby allowing reflectivity inversion that is independent of the amplitude spectrum of the seismic wavelet. As the temporal and spatial variation of the actual seismic wavelet in seismic reflection data is poorly known, this approach has many advantages over conventional seismic reflectivity inversion. No wavelet extraction is performed. Thus, the inversion for reflectivity can be conducted without well control, seismic ties, or time-depth functions. The inversion is sparse, so no starting model is needed. Furthermore, as no wavelet is required, the inversion can be applied directly to depth migrated data. The phase of the wavelet is constrained by the assumption of sparse reflectivity and thus works best when earth impedance structure is blocky. Trace integration of the inverted reflectivity provides bandlimited impedance which compares very favorably to well-log bandlimited impedance for both synthetic and real data cases.