Research on Collaborative Integration Mechanism of Seismic Processing and Interpretation Algorithms Based on Microservice Architecture

This paper addresses the challenges of multi-language and multi-framework algorithm integration in the digital transformation of seismic processing and interpretation. A microservice architecture tailored for seismic data analysis is proposed to standardize heterogeneous algorithm integration, meeting industry demands for ultra-large-scale data processing, elastic computing resources, and efficient algorithm management on distributed computing platforms (e.g., local clusters or cloud environments). The architecture integrates a four-layer framework comprising a data layer (using data lake technology for unified abstraction), an algorithm service layer (containerized deployment of Python and C algorithms), an orchestration layer (Kubernetes-based adaptive resource scheduling), and an application layer (APIs and web interfaces). Real-time request-response and asynchronous messaging mechanisms are implemented to handle both instantaneous interactions and long-running computational tasks. Experimental results demonstrate successful integration of multi-language algorithms, achieving a 35% reduction in response time and 40% improvement in resource efficiency under standard test scenarios, with strong horizontal scalability. This study resolves key issues in traditional approaches, including lengthy deployment cycles, data heterogeneity, and runtime environment conflicts, validating the feasibility of data lake-driven and container-orchestrated technical solutions for seismic processing workflows.