Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration. The main cost of seismic exploration is acquiring seismic data, which can be significantly reduced through compressed sensing (CS) techniques. Traditional and deep learning (DL) CS methods offer unprecedented opportunities for cost optimization while maintaining data fidelity. However, CS methods rely on random acquisition, which performs poorly when the seismic data are not randomly acquired. This manuscript proposes a novel physics-informed neural network (PINN) framework for reconstructing 3D seismic data acquired via down-sampling from Ocean Bottom Seismometer (OBS) observation systems. The compressed sensing acquisition system of seismic data contains two types of sparsity: 1) 2D random missing traces, 2) Dual random missing of source lines and source points. The proposed method employed move-out (MO) transformations with multiple constant velocities to mitigate aliasing artifacts and improve reconstruction accuracy. Then, a pre-interpolation process is utilized for the MO-transformed seismic data groups. Additionally, a semblance evaluation mechanism dynamically assigns weights to each MO dataset, generating optimized, pre-interpolated seismic profiles. Finally, the PINN architecture integrates physical constraints to refine the reconstructed data. The experimental results demonstrate the superior reconstruction performance and computational efficiency of the proposed method compared with the state-of-the-art.

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method (CM), the proposed tracking method (TM), and the variational method (VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.