Integrated Algorithm for High-Resolution Crustal-Scale Imaging Using Complementary OBS and Streamer Data

We present an integrated algorithm for high-resolution crustal-scale imaging utilizing long-offset wide-angle ocean-bottom seismometer (OBS) data and short-offset multichannel streamer (MCS) data. The algorithm adopts a two-step imaging strategy, initially using the OBS data to enhance deep structure imaging and capture long-wavelength features of the migration velocity. Subsequently, the MCS data are migrated to recover detailed short-wavelength components and shallow structures using the migration velocity model modified by the OBS result. Both steps employ the least-squares reverse time migration (LSRTM) method with appropriate regularization techniques. The algorithm is implemented using the Bregmanized operator splitting (BOS) approach, known for its efficiency and adaptability in handling non-smooth regularization, such as total-variation (TV) constraints. To improve computational efficiency, compressed sensing is employed to store incident wavefields at half their actual size and reconstruct them accurately when required. Convergence is expedited through the use of preconditioners and the Anderson acceleration method. The proposed algorithm is validated through large-scale numerical examples, demonstrating its robustness even with an initially imprecise velocity model. Results showcase improved resolution and accuracy achieved through the integration of OBS and MCS data. This study offers a comprehensive framework for crustal-scale imaging, addresses computational complexities, and provides enhanced imaging capabilities. We present a method for high-resolution crustal-scale imaging using a combination of long-offset wide-angle data from stationary receivers deployed on the seafloor with short-offset data from floating receivers. Our method is structured as a two-step imaging algorithm that exploits the unique strengths of each data set. In the first step, the stationary recorded data is used to recover the long-wavelength features. Subsequently, we integrate the floating receiver data with the preceding image to recover the short-wavelength components, adding details. The efficacy of our method relies on the utilization of least-squares reverse time migration, which is enhanced by incorporating appropriate regularization techniques. This enables us to address critical challenges such as memory overhead, convergence speed, and image stability. These key challenges are addressed by incorporating three advanced strategies: compressed sensing theory, Anderson acceleration, and total variation regularization. Accordingly, we enhance computational efficiency, expedite convergence, and elevate imaging quality, which helps provide accurate, high-resolution images of the subsurface that facilitate better geological interpretation of deep structures and therefore improve our understanding of the target's regional geodynamic context. Moreover, this algorithm allows imaging with less initial knowledge about the image and more robustness to the frequency range. Enhanced crustal-scale seismic imaging from academic data Ocean bottom seismometer and multi-channel streamer data integration High-resolution least-squares reverse time migration