Modeling and Sparsity Promoting Separation of Wind Turbine Noise in Common-shot Gathers

In land seismic acquisition, the quality of common-shot gathers is severely degraded by Wind Turbine Noise (WTN) when wind turbines are operating continuously in surveys. The high-amplitude WTN overlap or even completely submerge the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are observed: 1) it is periodic with nearly constant frequencies over time; 2) it is coherent but exhibits different apparent velocities in space; 3) it has relatively narrow bands with varying central frequencies. The first feature enables WTN to distort signals from shallow to deep, while the latter two features make traditional methods that separate noise and signals based on velocity and frequency differences less effective. To suppress the WTN, we first analyze its formation and propagation mechanism, and then propose a WTN simulation model to validate the presented mechanism. Based on our analysis of WTN and signals, we consider common-shot gathers as the linear superpositions of periodic WTN and relatively broadband signals (referred to as low-oscillatory signals). This additive mixture aligns with the feasibility premise of Morphological Component Analysis (MCA). Finally, based on MCA theory, we propose a sparsity-promoting separation method to suppress WTN in common-shot gathers. To implement our separation method, we construct two dictionaries using the Tunable Q-factor Wavelet Transform (TQWT) and the Discrete Cosine Transform (DCT). TQWT and DCT can sparsely represent oscillating waves (signals) and periodic waves (WTN), respectively. This work contributes to the existing knowledge of WTN separation by modeling the periodicity of WTN and the low-oscillatory behavior of signal, rather than relying on velocity or frequency differences. The proposed method has been tested on both synthetic and field data, and both tests demonstrate its effectiveness in separating WTN and preserving signals.