Impedance inversion is an ill-posed and nonlinear problem, and limited by wavelet estimation and data frequency band. In this work, the bi-directional long short-term memory recurrent neural network (Bi-directional LSTM RNN) is applied to the inverse problem of P-impedance (Ip) calculation. Logging data and borehole-side traces are used to generate the training dataset. Results show that this network successfully predicts impedances without known the wavelet. The Ip profiles maintain good lateral continuity due to the LSTM model application. The network has shown great promise in predicting a high-frequency impedance result from a low-frequency seismic signal, given appropriate training data.


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