Physics-Driven Neural Network for Interval Q Inversion

Quality factor (Q) estimation is critical for the processing of nonstationary seismic data and is an important indicator of oil and gas. Traditional methods for Q value estimation require the identification of the top and bottom of each constant Q layer, which can be challenging in the processing of field seismic data. Deep-learning (DL)-based Q inversion methods leverage the powerful nonlinear fitting capabilities of deep network to automatically obtain interval Q estimates directly from the input seismic data. However, these methods possess so-called “black box” characteristics and lack interpretability, thereby limiting their practical application. To address these issues, this study proposes a physics-driven neural network (PDNN) that integrates physical knowledge with deep neural networks, embedding the frequency-shift method for Q value calculation into the computational layers of the network. Our approach uses nonstationary seismic signals and their corresponding logarithmic time-frequency amplitude spectrum (LTFAS) as input. The neural network decouples the dynamic wavelets and reflection coefficients to obtain the LTFAS of dynamic wavelets. Furthermore, a network layer is designed based on the frequency-shift method to generate the interval Q curve. Experiments on both synthetic and field data demonstrate that the neural network constrained by physical knowledge can alleviate the instability in interval Q calculations, yielding more stable Q estimates. Additionally, this approach enhances the interpretability and generalization capabilities of DL methods, offering significant practical value.