Seismic-ZFP: Fast and Efficient Compression and Decompression of Seismic Data

We present our open-source seismic data compression library, built on top of a state-of-the-art floating-point compression algorithm, and motivated by the demands of Machine Learning and cloud computing.

Fast arbitrary reading is achieved by using two key observations, namely that regularity may be preserved by using fixed-rate compression, and that storage hardware may be efficiently utilized by packing disk blocks with data which is frequently accessed together.

We also demonstrate the quality of reproduction of the input data, with the claim that it is suitable for the purposes of Machine Learning.