Qianlong Ding

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows.

The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and PS reflection coefficients at different incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with angle (AVA) inversion based on the Zoeppritz equation has limitations regarding the accuracy. The AVA neglects transmission losses and the effects of multiple reflections during seismic wave propagation, resulting in reduced resolution. In contrast, the propagation matrix theory offers a comprehensive range of reflection coefficients for P- and S-waves in multilayered media at arbitrary incidence angles, thereby theoretically enhancing the inversion accuracy. However, the seismic responses obtained using this method exist in the slowness–frequency domain and require constant slowness for consistency along a profile. This assumption is violated when variations in the P-wave velocity occur within the subsurface, affecting the incidence angle of propagating seismic waves. This study modifies the propagation matrix theory to compute AVA seismic responses and applies it to pre-stack multiparameter inversion. The effectiveness of the modified method was validated by deriving theoretical AVA seismic responses and comparing them to solutions from a typical layered media model. The modified theory was also employed for seismic pre-stack inversion. Numerical simulations and field data tests demonstrated that the new propagation matrix method offers a high accuracy and stability.