Deep Learning for Seismic Data Compression
摘要
The seismic industry has experienced a significant increase in data volume in recent years, primarily attributed to advances in digital technology and seismic data acquisition methods. This explosive growth poses considerable challenges, notably in data storage, transmission, and processing. Addressing these challenges accelerates the development of an efficient data compression algorithm tailored to leverage the inherent sparsity and redundancy in seismic data. This approach facilitates a more compact representation of the data by effectively reducing its dimensions.