Abstract: A seismic time-frequency analysis method based on generalized Chirplet transform with time-synchronized extraction, which has higher level of energy aggregation in the time direction and can better describe and characterize the local characteristics of seismic signals, and is applicable to the time-frequency characteristic representation of both harmonic signals and pulse signals, comprising the steps of processing generalized Chirplet transform with time-synchronized extraction for each seismic signal to obtain a time spectrum by: carrying out generalized Chirplet transform, calculating group delay operator and carrying out time-synchronized extraction on seismic signals, thereby the boundary and heterogeneity structure of the rock slice are more accurately and clearly shown and subsequence seismic analysis and interpretation are facilitated.

Abstract: Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

Abstract: Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

Abstract: A seismic time-frequency analysis method based on generalized Chirplet transform with time-synchronized extraction, which has higher level of energy aggregation in the time direction and can better describe and characterize the local characteristics of seismic signals, and is applicable to the time-frequency characteristic representation of both harmonic signals and pulse signals, comprising the steps of processing generalized Chirplet transform with time-synchronized extraction for each seismic signal to obtain a time spectrum by: carrying out generalized Chirplet transform, calculating group delay operator and carrying out time-synchronized extraction on seismic signals, thereby the boundary and heterogeneity structure of the rock slice are more accurately and clearly shown and subsequence seismic analysis and interpretation are facilitated.