Full text loading...
Crustal velocity parameters are crucial for determining geological properties. The commonly used velocity model building method is mainly travel-time tomography, which assumes that the lateral changes of the seafloor structure are not drastic and usually only uses the first arrival wave, which severely limits its inversion accuracy. To address this problem, this paper proposes a high-precision crustal velocity modeling method that combines tomography with deep learning. First, a macro model is constructed using tomography, and then a more accurate model is constructed using U-net-SSIM on this basis. In order to address the problem of low inversion accuracy of deep learning due to the limited number of training samples, a deep learning algorithm in the cosine transform domain is introduced. The South China Sea OBS data verifies the effectiveness of this method.