Domain adaptation-based sparse time-frequency analysis and its application on seismic attenuation estimation

Time-frequency (TF) transform is a commonly used tool for geologic structure interpretation and attenuation estimation, mainly including the linear and nonlinear methods. The former consists of short-time Fourier transform, continuous wavelet transform, S-transform, etc., which can be efficiently implemented. However, they suffer from the Gabor uncertainty principle, which limits their TF readability. The latter can effectively enhance TF readability; nevertheless, it has several unavoidable drawbacks, such as low computational efficiency and parameter selection. For seismic sparse TF (STF) analysis (STFA), we develop a domain adaptation-based STFA (DASTFA) model. This model mainly contains two steps. First, we suggest an STF network (STFNet) for mapping a 1D seismic signal to a 2D STF spectrum, which is pretrained using synthetic traces and STF labels. Second, based on the pretrained STFNet, we design a DASTFA model for transferring to field data. Note that the synthetic traces are generated using well-log data and horizon at the study area, located in the Ordos Basin, China, which can reduce the gap between synthetic and field data. Afterward, we use synthetic traces with their STF labels for model pretraining in the first step and field data without STF labels for model transferring in the second step. Here, model transferring indicates transferring the pretrained model parameters based on field traces. Finally, to test the validity and effectiveness of DASTFA, we apply it to synthetic and field data. Moreover, we test the availability of seismic attenuation estimation by adopting the suggested model for 3D poststack field data volume, which achieved encouraging results.