Adaptive Integration of Active-Passive Seismic Data for Robust Velocity Inversion With Deep Learning

The pivotal role of seismic velocity inversion in oil and gas exploration and geological research has been widely acknowledged. However, conventional methods face challenges such as strong reliance on initial models and high computational costs. Based on the mode of seismic event generation, seismic data can be classified into active seismic data and passive seismic data, which collectively constitute the multisource data discussed in this article. Velocity inversion based on deep learning primarily relies on active seismic data, training neural networks to learn the mapping between seismic records and subsurface velocities. In contrast, signals in passive seismic data typically originate from noise at certain depths within the Earth, encompassing valuable information about deep subsurface structures that is crucial for velocity inversion, thus presenting a potential complement to active seismic data. This study proposes a seismic velocity inversion method that combines active and passive seismic data, utilizing deep learning techniques to adaptively integrate data from both sources, enabling joint inversion. The proposed neural network architecture combines transformer and convolutional neural network (CNN), enhancing the accuracy and robustness of velocity inversion.