Abstract
The seawater column is typically taken as a homogeneous velocity layer in wide-angle crustal seismic surveys in marine environments. However, heterogeneities in salinity and temperature throughout the seawater layer result insignificant lateral variations in its seismic velocity, especially in deep marine environments. Failure to compensate for these velocity inhomogeneities will introduce significant artifacts in constructing crustal velocity models using seismic tomography. In this study, we conduct numerical experiments to investigate the impact of heterogeneous seismic velocities in seawater on tomographic inversion for crustal velocity models. Experiments that include lateral variation in seawater velocity demonstrated that the modeled crustal velocities were contaminated by artifacts from tomographic inversions when assuming a homogeneous water layer. To suppress such artifacts, we propose two strategies: 1) simultaneous inversion of water velocities and the crustal velocities; 2) layer-strip** inversion during which to first invert for seawater velocity and then correct the travel times before inverting for crustal velocities. The layer-strip** inversion significantly improves the modeling of variation in seawater velocity when preformed with seismic sensors deployed on the ocean bottom and in the water column. Such strategies improve crustal modeling via wide-angle seismic surveys in deep-marine environment.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 41230318), the Natural Science Foundation of Shandong Province (No. ZR2014DM006), the China Postdoctoral Science Foundation (No. 2015M582138), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education. The final publication is available at Springer via https://doi.org/10.1007/s12583-017-0816-7.
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Zou, Z., Zhou, HW., Gurrola, H. et al. Impact and Solutions of Seawater Heterogeneity on Wide-Angle Tomographic Inversion of Crustal Velocities in Deep Marine Environments—Numerical Studies. J. Earth Sci. 29, 1380–1389 (2018). https://doi.org/10.1007/s12583-017-0816-7
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DOI: https://doi.org/10.1007/s12583-017-0816-7