The Role of Data Storage and Management in Seismic Data Processing : Best Practices for Efficient Data Handling and Integrity

The exponential growth of seismic data volumes driven by high-resolution acquisition and continuous monitoring has underscored the critical role of robust data storage and management systems in seismic data processing workflows. Efficient handling of terabytes to petabytes of raw and processed seismic records demands scalable storage architectures, rigorous metadata cataloguing, and integrity-preserving mechanisms. This review examines state-of-the-art storage technologies—including disk arrays, object storage, and cloud platforms—and evaluates data management practices such as automated ingestion pipelines, metadata standards, and provenance tracking. We analyze integrity assurance techniques, from checksum validation to backup and disaster-recovery strategies, that safeguard data fidelity throughout processing stages. Challenges related to performance bottlenecks, security, and evolving hardware-software ecosystems are discussed, along with best-practice guidelines for optimizing throughput and minimizing data loss. Finally, emerging trends—such as AI-driven data placement, hybrid on-premises/cloud frameworks, and advanced compression algorithms—are highlighted to inform future developments. By synthesizing current methodologies and proposing practical recommendations, this paper aims to guide geoscience teams in constructing resilient, high-performance data infrastructures that underpin accurate seismic interpretation and reliable reservoir characterization.