Ground vibrations as an untapped energy resource: Evaluation of energy harvesting potential using earthquakes, mining induced seismicity and anthropogenic sources

The assessment of ground motion vibrational energy harvesting (VEH) potential remains largely unexplored. Unlike mature renewable resources such as solar or wind energy, no standardized frameworks currently exist to quantify how much energy can be realistically harvested from real ground vibrations. This knowledge gap limits the strategic deployment of VEH technologies and constrains their integration into self-powered sensing systems. This study introduces a novel, systematic methodology to evaluate the theoretical harvesting potential of electromagnetic energy harvesters (EMEHs) subjected to real seismic excitations. The proposed workflow comprises six stages: data acquisition, signal preprocessing, event identification (qualification, characterization and classification), device selection, energy harvesting simulation, and potential power estimation. Both natural and anthropogenic vibration sources, including earthquakes, microseisms, mining blasts, and heavy vehicle traffic, are explicitly considered. The methodology is applied to a mining environment in northern Chile using continuous seismic records. Distinct yet partially overlapping power ranges are identified across different event classes, highlighting the strong dependence of harvestable energy on local vibrational characteristics. Spatial energy density maps further reveal preferential deployment zones for EMEHs devices, underscoring the strong spatial dependence of the energy resource and the importance of characterizing local vibrational features when evaluating harvesting feasibility and robustness. By shifting the focus from device-level optimization to resource-level assessment, this work establishes a replicable foundation for evaluating seismic vibrations as an energy resource. The framework represents an initial step toward unlocking the potential of ground motion VEH, including natural events, and enabling its future integration into sustainable, self-powered monitoring infrastructures.