As the intelligent application of oil and gas exploration and development matures and application scenarios increase，large-scale application is drawing nearer. As a result，the distributed storage，efficient collection，and parallel com? puting of samples have become urgent requirements of the intelligent application of oil and gas exploration and development. The intelligent application of seismic exploration is an important part of that of oil and gas exploration and development. In view of the large amount of single file data in and the unstructured characteristic of seismic exploration data，this paper analyzes the collection requirements for seismic exploration big data samples，proposes a solution of large file segmentation and merging based on the Hadoop distributed file system（HDFS），and implements redundant storage of seismic exploration data in three dimensions to improve the efficiency of seismic exploration sample collection. The experimental results show that the HDFS-based triple redundant storage solution can effectively improve the efficiency in collecting seismic exploration big data samples under rapid growth in data amount and therefore meet the requirements for intelligent application of seismic exploration.