应用Squeeze算法实现地震数据高效压缩

Applying Squeeze algorithm to achieve efficient compression of seismic data

为了提高海量地震数据的存储和处理能力,需要采用有损压缩方法对地震数据进行高效压缩。目前常用的地震有损压缩方法主要利用了地震数据的稀疏性来实现压缩,代码实现较为复杂,实际应用需要使用者对相应的压缩方法原理具有比较好地了解。另一种压缩实现原理是利用地震数据相邻道之间的关系实现压缩,该类方法原理简单,易于理解。这里将近年最新发展的Squeeze算法应用到地震数据压缩问题中,该算法通过使用多维和多层预测模型,基于邻近点信息预测当前点波场值,然后对实际波场值和预测值之间的差异,进行自适应量化编码和熵编码实现数据的有效压缩。在地震波数值模拟算法中实现了Squeeze压缩方法,并通过二维SEG/EAGE盐丘模型合成道集数据、实际三维道集数据以及三维Marmousi正演波场数据测试Squeeze压缩效果,结果表明,Squeeze算法对于地震数据具有比较好的压缩性能,同时代码实现和使用十分方便。

In order to improve the storage and processing capacity of massive seismic data,it's necessary to use lossy compression methods to compress seismic data efficiently.The commonly used methods are mainly employ the seismic data sparsity to achieve compression,and the code implementations are also complicated,which requires the users to have a better understanding of the corresponding methods.Another compression implementation principle is to use the information of adjacent points of the seismic data to achieve compression.And the methods based on this are easy to be understood and applied.In this paper,we implement the newly developed algorithm,the Squeeze compression algorithm in seismic data compression.It employs a multi-layer and multi-dimensional prediction model for the relationship between adjacent points,and then performs adaptive quantization encoding for the difference between the real value and predicted value,and entropy encoding.We have implemented the Squeeze compression in the seismic forward modeling.And through the compression tests of 2D SEG-EAGE salt model post-stack data,real 3D seismic gather data,and the wavefield data of 3D Marmousi forward modeling,we show that the Squeeze algorithm has a good compression performance on seismic data,and its implementation and application are quite convenient.