Reverse Time Migration with Lossy and Lossless Wavefield Compression

  • Carlos HS Barbosa UFRJ
  • Alvaro LGA Coutinho UFRJ


Seismic imaging techniques like Reverse Time Migration (RTM) are time-consuming and data-intensive activities in the field of geophysical exploration. The computational cost associated with the stability and dispersion conditions in the discrete two-way wave equation makes RTM time-consuming. Additionally, RTM is data-intensive due to the need to manage a considerable amount of information, such as the forward propagated wavefields (source wavefield), to build the final migrated seismic image according to an imaging condition. In this context, we introduce lossy and lossless wavefield compression for parallel multi-core and GPU-based RTM to alleviate the data transfer between processor and disk. We use OpenACC for enabling GPU parallelism and the ZFP library aligned to decimation based on the Nyquist sampling theorem to reduce storage. We study experimentally the effects of wavefield compression for both GPU-based and optimized OpenMP+vectorization RTM versions. Multi-core and GPU-based RTM have been linked to the ZFP library to compress the source wavefield on-the-fly once it has been decimated according to the Nyquist sampling theorem to calculate the imaging condition. This approach can reduce drastically the persistent storage required by the technique. However, it is essential to understand the impact of using compressed wavefields on the migration process that builds the seismic image. In this context, we show how much storage can be reduced without compromising the seismic image's accuracy and quality.
Palavras-chave: Seismic imaging, High-performance Computing, Reverse Time Migration, OpenMP/OpenACC, Compression
BARBOSA, Carlos HS; COUTINHO, Alvaro LGA. Reverse Time Migration with Lossy and Lossless Wavefield Compression. In: INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD), 35. , 2023, Porto Alegre/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 192-201.