Thin-layer weak signal enhancement based on cyclic spectrum enhancement technology

With the continuous growth of global energy demand, subtle reservoirs such as thin beds have become an important target for exploration and development. However, the identification accuracy of thin-layer seismic weak signals is limited by the physical resolution limit of traditional seismic exploration methods due to its small thickness, strong heterogeneity and significant interlayer interference effect. In order to solve the problem of weak signal recognition in thin layer, a method of weak signal recognition in thin layer based on cyclic spectrum enhancement technology is proposed in this paper, improving the sensitivity and accuracy of weak signal detection. The method preprocesses the original seismic data by Gauss filtering to suppress random noise while accurately preserving the main features of the signal, and then introduces even-order derivative operation, the high-frequency details of the weak signal in the thin layer are enhanced, and the reflection difference between the layers is highlighted. To mitigate the high-frequency artifacts inherently generated by high-order derivatives, a Butterworth low-pass filter is employed for directional spectral conditioning, facilitating the high-fidelity separation of the effective signal from noise. Finally, the optimal derivative order is dynamically determined through an adaptive termination criterion to prevent over-enhancement or under-enhancement. Numerical simulation and field seismic data test showed that the proposed method significantly improves the resolution of thin-layer horizons and enhances the detectability of subtle seismic signals.