2018_EJRNL_PP_HANCHUANG_WANG_1.pdf
Terbatas  
» Gedung UPT Perpustakaan
Terbatas  
» Gedung UPT Perpustakaan
In recent years, the sparsity-promoting reconstruction method based on the compressed
sensing theory has been rapidly developing and applied to seismic data reconstruction. Many
achievements have been made toward providing high-quality reconstruction by using
undersampled data. However, the problem of insufficient reconstruction in null traces still hinders
us a lot in practical applications, especially for complex seismic data. Aiming to solve this
problem, we made full use of the sparsity characteristics of seismic data in multiple sparse
transform domains and jointly reconstructed seismic data to realize the complementary advantages
of multiple sparse transforms; As such, we propose a high-precision seismic data recovery method
with multi-domain sparsity constraints based on curvelet and high-resolution Radon transforms.
Numerical examples by synthetic and real data showed that the new approach can achieve a better
reconstruction result than the commonly used curvelet-based recovery method. Integrated with the
curvelet transform to develop new recovery method, the high-resolution Radon transform has
more advantages than the conventional Radon transform for overcoming the shortcomings
associated with the insufficient reconstruction of high-amplitude events. At the same time, the
method is also applicable for developing new reconstruction methods by combining other sparse
transforms depending on the characteristics of seismic data. The reconstruction method with
multi-domain sparsity constraints can easily be extended to three-dimension situation.