Sparse time-frequency analysis of seismic data via convolutional neural network

Time-frequency (TF) analysis is commonly used to reveal the local properties of seismic signals, such as frequency and spectral contents varying with time/depth. Aiming to realize a highly localized TF representation of seismic signals, researchers treated the TF analysis as an inverse problem, and the regularization is adopted in the objective functions. Traditionally, the TF sparse inversion process is solved by the Lasso regression. It has been proven that the Lasso regression needs a large number of iterations to reach a high accurate solution for the convex problem. Recently, convolutional neural network (CNN) has been successfully used to solve the convex problem due to their high computational efficiency and strong nonlinear characterization ability. We use CNN to solve the sparse TF inversion, and our method is called STFA-CNN. The objective function in the neural network architecture consists of two portions. The first one is to minimize the difference between the local forward and backward Fourier transform of seismic signals. The second is minimizing the regularization (lp norm) of TF results. To demonstrate the effectiveness of our method, we apply it to synthetic and real seismic data. We further use the calculated TF spectra to compute the attenuation of seismic waveforms and apply the attenuation attribute to predict the hydrocarbons of a seismic survey acquired over the Ordos Basin, northwest of China.