Multiscale Sparse Dictionary Learning With Rate Constraint for Seismic Data Compression

Xin Tian

doi:10.1109/access.2019.2925535

Abstract

Seismic survey is one of the most effective tools for oil and gas exploration. To date, there has been an exponential growth in the size of seismic data required for large-scale seismic survey. For transmission and storage purposes, we propose a novel seismic compression method. First, a multiscale sparse dictionary learning model with rate constraint is presented. By combining the advantages of multiscale decomposition and dictionary learning, the seismic data could be effectively represented as a sparse matrix. Rate constraints are used to obtain the sparse coefficients that are properly tailored to the compression objective. To solve the optimization problem, the alternating direction method of multipliers is adopted. Furthermore, a seismic compression scheme based on the learned dictionary is introduced. Finally, public seismic datasets are used to verify the efficiency of different seismic data compression methods. The experimental results indicate that the proposed method achieves the best seismic compression performance, including rate-distortion tradeoff and visual quality.

Highlights

A seismic survey, which collects a significant amount of seismic data from a field, is a primary strategic tool that is utilized in oil and gas exploration [1]
The peak signal to noise ratio (PSNR) and the structural similarity index (SSIM) [42], which are two quality metrics used for reconstruction performance comparison, are exploited and defined as follows: PSNR
In this report, we propose a novel seismic compression method based on multiscale and rate-constrained dictionary learning

Summary

Introduction

A seismic survey, which collects a significant amount of seismic data from a field, is a primary strategic tool that is utilized in oil and gas exploration [1]. A typical seismic survey may generate tens of terabytes of raw seismic data on a daily basis [2] This results in extreme challenges in terms of wireless gathering of the seismic data from the field, given that the bandwidth of a wireless system is inherently limited. Once collected, this massive amount of data requires very large storage capacities at data centers. Lossless compression [3] is a kind of data compression algorithm that allows the original data to be perfectly reconstructed from the compressed data but results in limited data reduction This may not be suitable for the compression of a massive amount of seismic data, especially when the seismic signal samples are represented by float

Methods

Results

Conclusion