Nature’s intelligence: Hybrid bio-inspired method yields more accurate seismic locations of geomorphic events

How do we pinpoint fleeting geomorphic surface events in the planet's remotest corners, where no witness observes and classical methods falter? Processes like landslides, debris flows, avalanches, and rockfalls not only sculpt the Earth's dynamic landscape but also pose significant hazards in remote and populated areas alike. As environmental changes intensify, closing the gap of elusive detection holds profound implications for disaster response, hazard prediction, and geomorphic theory advancement. The difficulty lies in the concealed, stochastic nature of these processes and the challenges of direct observation. Continuous high-resolution seismic sensing offers unique potential to detect and locate geomorphic sources that evade other tools. However, surface processes generate chaotic, site-specific waveforms with rapid, nonlinear energy release, often in noisy, inaccessible settings. Existing, rigid location techniques are ill-equipped for this challenge, failing to match known details of historic geomorphic sources. We introduce a hybrid, nature-inspired seismic event location approach that fuses physical and biological principles to overcome longstanding obstacles in monitoring geomorphic processes.

Our method synergizes deterministic and heuristic elements into a robust, self-adaptive framework. The source location is approximated first by a hybrid of grid search, modified gradient descent, and full waveform inversion. A bio-inspired procedure then iteratively refines this output to near-optimal solutions. Our method autonomously picks arrival times through a multi-layered structure, leveraging dynamic time warping, Bayesian inference, and SNR optimization. Composite misfit metrics from synthetic and observed waveforms guide location estimation in a dynamic solution landscape. This search space self-adjusts to instrument network layout and landscape complexity using Voronoi tessellation, convex hulls, and velocity-refined grids.

The cornerstone of our approach is a biomimicry component, inspired by the adaptive, collaborative behaviors of diverse animal species. We leverage over ten animal behaviors mathematically encoded as optimization agents. Each species epitomizes niche strategies based on their specific strengths. For instance, elephants’ memory and herding guide global searches, fireflies’ light-attraction principles refine locally, and whales’ spiral foraging navigates complex search spaces. Guided by evolutionary mechanisms, predator-prey dynamics, and interagent communication, collective intelligence and a recursive memory are built, and global exploration is seamlessly integrated with local information, balancing far-field searches with near-field precision.

As a benchmark we will use a seismic dataset of 290 geomorphic events, spanning diverse types, scales, and complexities, worldwide. Preliminary results show a 47–200% reduction in location misfit compared to brute-force methods, which mislocate events by 11–20 km. Biomimicry achieves relocation precision of 2.6 km, reducing misfits by up to five orders of magnitude. Improvements are achieved within 150 iterations across varying noise levels, with location standard deviations as low as 1–2 km. Additionally, the method isolates subsurface anomalies, estimates source depth, provides a pathway to track process propagation, and can eventually integrate into real-time early warning systems.

By bridging geomorphology, biology, and seismology, our work elevates the capacity to detect surface processes with accuracy, adaptability, and scalability. Intelligent, resilient, and inspired by nature itself, it lays a foundation for applications ranging from hazard monitoring to planetary exploration.