3D Geometry Design via End-To-End Optimization for Land Seismic Acquisition

Seismic acquisition is important in the exploration of the subsurface to find new petroleum fields, where a regular-spaced dense acquisition is critical to obtain high-quality seismic images. However, the acquisition costs and environmental impacts have motivated undersampled acquisition schemes, where several sensing points are removed to decrease the total seismic sources. After the undersampled measurements are acquired, a recovery algorithm reconstructs the missing seismic images (shots). The removed sources are currently selected using random sensing schemes, leading to suboptimal quality in the recovered seismic images. Thus, an optimal design of the removed sources is crucial as it determines the quality of the recovered shots. This work proposes an end-to-end optimization to jointly design undersampled seismic acquisition geometries while preserving the high-quality of the reconstructed data. The seismic acquisition geometry is modeled as a deep binary layer to learn the optimal sensing pattern, while a deep neural network is used to recover the underlying removed shots. Extensive simulations were carried out on a realistic-synthetic Foothills model. The results obtained on the reconstructed data validate that the proposed acquisition design outperforms the state-of-the-art random, uniform, and jitter sensing schemes in 4 dB.