Prestack reverse time migration (RTM) requires extensive data storage since it computes wavefields in forward time and accesses wavefields in reverse order. We first review several successful schemes that have been proposed to reduce data storage, but require more computational redundancies. We propose two effective strategies to reduce data storage during RTM. The first strategy is based on the Nyquist sampling theorem, which involves no extra computational cost. The fact is that the time sampling intervals required by numerical algorithms or given by field records is generally several times smaller than that satisfied by the Nyquist sampling theorem. Therefore, we can correlate the source wavefields with the receiver wavefields at the Nyquist time step, which helps decrease storage of time history. The second strategy is based on a lossless compression algorithm, which is widely used in computer science and information theory. The compression approach reduces storage significantly at a little computational cost. Numerical examples show that the two proposed strategies are effective and efficient.
[1]
Abraham Lempel,et al.
A universal algorithm for sequential data compression
,
1977,
IEEE Trans. Inf. Theory.
[2]
William W. Symes,et al.
Reverse time migration with optimal checkpointing
,
2007
.
[3]
R. Clapp.
Reverse time migration with random boundaries
,
2009
.
[4]
Yue Wang,et al.
REVERSE-TIME MIGRATION
,
1999
.
[5]
Alexey Romanenko,et al.
3D wave-packet decomposition implemented on GPUs [Электронный ресурс]
,
2011
.
[6]
Peter Moczo,et al.
Efficient Methods to Simulate Planar Free Surface in the 3D 4th-Order Staggered-Grid Finite-Difference Schemes
,
2002
.
[7]
David A. Huffman,et al.
A method for the construction of minimum-redundancy codes
,
1952,
Proceedings of the IRE.
[8]
N. Whitmore.
Iterative Depth Migration By Backward Time Propagation
,
1983
.
[9]
Giovanni Motta,et al.
Handbook of Data Compression
,
2009
.