This work presents a method for volumetric seismic data compression by coupling a 3-D convolution-based autoencoder to a generative adversarial network (GAN). The main challenge of 3-D convolutional autoencoders for data compression is how to fully exploit volumetric redundancy while keeping reasonable latent representation dimensions. Our method is based on a convolutional neural network for seismic data compression called 3DSC. Its encoder and decoder use 3-D convolutions and are connected by a latent representation with the same dimensions as its 2-D network counterparts. Our main hypothesis is that the 3DSC architecture can be improved by adversarial training. We, thus, propose a new 3-D-based seismic data compression method (3DSC-GAN) by coupling the 3DSC network to a GAN. The seismic data decoder is used as a generator of poststack data that are integrated with a discriminator module to better exploit 3-D redundancy. Results show that our method outperforms previous seismic data compression methods for very low target bit rates, increasing the peak signal-to-noise ratio (PSNR) with fairly high visual quality.

We approach the problem of 3-D poststack seismic data compression by training a model based on a deep autoencoder. Our network architecture is trained to consider the similarity between 3-D seismic sections drawn from one or multiple seismic volumes. A whole seismic volume is compressed with the latent representations of each of its composing volumetric sections. The goal is to compress the seismic data at very low bit rates with high-quality reconstruction. Our model is suitable for training general compressors from multiple seismic surveys or for specialized compression of a single seismic volume. Results show that our method can compress seismic data with extremely low bit rates, below 0.3 bits-per-voxel (bpv) while yielding peak signal-to-noise ratio (PSNR) values over 40 dB.

The parallelization of Smith-Waterman (SW) sequence comparison tools for long DNA sequences has been a big challenge over the years, requesting the use of several devices and sophisticated optimizations. Pruning is one of these optimizations, which can reduce considerably the amount of computation. This article proposes MultiBP, a sequence comparison solution in multiple GPUs with block pruning. Two MultiBP strategies are proposed. In static score-sharing, workload is statically distributed to the GPUs, and the best score is sent to neighbor GPUs to simulate a global view. In the dynamic strategy, execution is divided into cycles and workload is dynamically assigned, according to the GPUs processing rate. MultiBP was integrated to MASA-CUDAlign and tested in homogeneous and heterogeneous platforms, with different NVidia GPU architectures. The best results in our homogeneous and heterogeneous platforms were mostly obtained by the static and dynamic approaches, respectively. We also show that our decision module is able to select the best strategy in most cases. Finally, the comparison of the human and chimpanzee chromosomes 1 in a cluster with 512 V100 NVidia GPUs took 11 minutes and obtained the impressive rate of 82,822 GCUPS (Billions of Cells Updated per Second) which is, to our knowledge, the best performance for SW tools in GPUs.