A paradigm shift in seismic monitoring: from hardware-driven to algorithm-defined intelligent systems

Abstract

The technology of seismic monitoring is undergoing a profound paradigm shift from systems centered on hardware performance to architectures defined by algorithmic intelligence. Traditionally, hardware has served as the core of seismic monitoring, undertaking essential tasks such as signal acquisition, noise suppression, and feature extraction; however, its performance improvement is inherently constrained by cost, power consumption, and physical limits. In contrast, the rapid development of artificial intelligence and edge computing has opened new avenues in which algorithms are no longer ancillary tools assisting hardware but have become the central mechanism that compensates for hardware deficiencies and defines system intelligence. This paper examines three representative hardware challenges in seismic monitoring, including sensor drift, ultra-low signal-to-noise ratio (SNR), and limited edge-computing resources, and systematically analyzes the roles of adaptive calibration, deep learning-based denoising, and lightweight intelligent models in addressing these issues. By comparing traditional hardware-oriented optimization with algorithm-driven strategies, this paper highlights that future seismic monitoring systems will achieve breakthroughs through deep algorithm–hardware integration, forming an intelligent network that is predictive, physically constrained, and self-evolving. This trend signifies the emergence of a more intelligent, sustainable, and resilient next-generation seismic monitoring paradigm.