Considerations for computing and dataflows in seismic data processing

Kai Zhuang, Daniel O. Trad

Modern seismic imaging and inversion rely on algorithms whose computational demands exceed the capabilities of naive implementations. Practical performance depends not only on mathematical complexity but on how computation interacts with memory hierarchies, data movement, and parallel execution models. This paper outlines the principles of computation-aware scientific programming, examines how processor architecture shapes algorithm design, and demonstrates through geophysical examples why optimization must be integrated into research rather than deferred to production engineering. Scalable implementations require aligning algorithmic structure with hardware constraints across CPUs, GPUs, and distributed systems. Emerging hardware trends will further influence the next generation of seismic algorithms, increasing the need for algorithmic designs that explicitly account for locality, concurrency, and memory behavior.