Abstract：The existing seismic inversion methodsare mainly model-driven and sensitive to noise, and the resolution of the inversion results is often low.In addition, they usually only use the amplitude information of seismic dataand fail to fully utilize the phase information.The results show that the correlation between wave impedance and seismic amplitude and phase is strong, so the joint application of amplitude spectrum and phase spectrum can effectively alleviate the multi-solution problem of inversion results. Therefore, this paper starts from the data-driven perspective to achieve intelligent wave impedance inversion based on frequency-phase fusion by combining deep learning with frequency （amplitude spectrum） and phase （phase spectrum） information of seismic data, which effectively improves the resolution and accuracy of inversion.The steps are as follows：①extracting the frequency and phase information of seismic trace based on the inversion of high-resolution time-frequency analysis； ②applying image processing technology to fuse frequency and phase information； ③combi-ning frequency and phase information and formation wave impedance to make data label pairs and using the data label pairs to train the preferred deep network；④extracting the frequency and phase information of the three-dimensional seismic data to be retrieved into the trained deep network, so as to obtain the high-resolution wave impedance inversion results.Compared with the existing data-driven deep learning seismic inversion methods, the innovation of the proposed method is as follows： the frequency and phase information are applied simultaneously in seismic inversion for the first time.The model test results show that the joint application of frequency and phase information can effectively alleviate the multi-solution problem of inversion results and improve the inversion accuracy.The application results in the Dalujia area of Shengli Oilfield show that compared with the traditional sparse pulse inversion, the proposed inversion method has significantly improved the longitudinal resolution, which is beneficial to further promotion and application.
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