A Machine-learning-based Method for Integrating Seismic Data from Heterogeneous Sources

Summary In marine seismic exploration, various types of seismic sources are employed to visualize geological structures beneath the seafloor, depending on survey objectives. Airgun sources, which generate large amounts of energy by releasing compressed air underwater, are typically used for imaging deep area; however, they have limited vertical resolution due to their low peak frequencies. In contrast, sparker sources generate wavelets with high peak frequencies using bubbles produced by discharging electrical energy to vaporize water, resulting in high vertical resolution. Sparker sources are useful for the detailed imaging of shallow strata but have a shallow penetration depth due to their low source energy. This paper proposes a method to integrate airgun and sparker data to broaden the frequency bandwidth and thus achieve more accurate geological interpretations. The study used small-scale airgun data and sparker data acquired in Yeongil Bay, Pohang, South Korea. A machine-learning-based shaping filter model was developed along with synthetic training data representing the airgun and sparker source wavelet characteristics, and the trained models were applied to regularize these source wavelets. Subsequently, time-variant spectral whitening (TVSW) and weighted integration were performed to yield the flattened broadband frequency spectrum. The integrated data have enhanced penetration depth and vertical resolution compared with the original single-source datasets, thus overcoming the interpretational limitations imposed by their limited frequency bandwidth and penetration depth and enhancing the reliability of associated geological interpretations.