Model-constrained and data-driven double-supervision acoustic impedance inversion

Seismic impedance inversion is an important technique for structure identification and reservoir prediction. Model-based and data-driven impedance inversion are the commonly used inversion methods. In practice, the geophysical inversion problem is essentially an ill-posedness problem, which means that there are many solutions corresponding to the same seismic data. Therefore, regularization schemes, which can provide stable and unique inversion results to some extent, have been introduced into the objective function as constrain terms. Among them, given a low-frequency initial impedance model is the most commonly used regularization method, which can provide a smooth and stable solution. However, this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data, which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions. Therefore, we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network, which regards seismic data as time-series rather than image-like patches. Compared with the model-based inversion method, the data-driven approach provides higher resolution inversion results, which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components. However, judging from the inversion results for characterization the spatial distribution of thin-layer sands, the accuracy of high-frequency components is difficult to guarantee. Therefore, we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes. First, constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network, which provides the predicted impedance with higher resolution. Then, convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results, which makes the synthetic seismic data obtained from the inversion result consistent with the input data. Finally, we test the proposed scheme based on the synthetic and field seismic data. Compared to model-based and purely data-driven impedance inversion methods, the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity. The inversion results accurately characterize the spatial distribution relationship of thin sands. The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data. Moreover, tests on the oil field data indicate the practicality and adaptability of the proposed method.