Abstract-Duplicates in data streams may often be observed by the projection on a subspace and/or multiple recordings of objects. Without the uniqueness assumption on observed data elements, many conventional aggregates computation problems need to be further investigated due to their duplication sensitive nature. In this paper, we present novel, space-efficient, one-scan algorithms to continuously maintain duplicate insensitive order sketches so that rank-based queries can be approximately processed with a relative rank error guarantee ǫ in the presence of data duplicates. Besides the space efficiency, the proposed algorithms are time-efficient and highly accurate. Moreover, our techniques may be immediately applied to the heavy hitter problem against distinct elements and to the existing fault-tolerant distributed communication techniques. A comprehensive performance study demonstrates that our algorithms can support real-time computation against high speed data streams.
In seismic data acquisition, data loss can occur, particularly with the use of streamer systems in marine seismic exploration. These systems often cause spatial aliasing problems by having close inline intervals and wide crossline intervals to maximize the exploration range. To improve the resolution of seismic data in the crossline direction, various machine learning techniques have been employed for crossline data reconstruction. In this study, we introduce a 3D cWGAN (conditional Wasserstein generative adversarial network) for interpolating 3D seismic data. We evaluate the model’s performance by comparing it with 2D cWGAN and 3D U-Net. In this study, two interpolation strategies are employed to reconstruct missing data in the crossline direction. The first strategy uses a 2D network, which trains a model using inline data and applies the trained model to the crossline direction via 2D cWGAN. The second strategy employs a 3D network, which uses the 3D volume of the seismic data directly via 3D cWGAN and 3D U-Net. We demonstrate the effectiveness of the proposed method using the Sleipner CO2 4D seismic survey dataset. Our results show that the 3D cWGAN is more efficient in enhancing resolution and computation compared to the 2D cWGAN or 3D U-Net.
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