Detection and elimination of unwanted spikes (artifacts) in rotational seismographic signals using artificial intelligence

Abstract

This paper presents methods for detecting and eliminating artifacts in signals recorded by the FOS6 rotational seismograph based on the Sagnac effect. A combination of classical threshold-based techniques and artificial intelligence (AI) algorithms was employed, aimed not only at detecting artifacts but also at improving the overall quality of the recorded data. Particular emphasis was placed on the deliberate use of AI – not as a direct filtering tool, but as a means of identifying regions of the signal that can be effectively smoothed or removed while preserving waveform integrity. The threshold-based algorithm mainly functioned as a source of training data for the AI models, enabling effective learning and testing of the approaches developed. Training data were obtained from the earlier FOS5 device, and verification was performed using recordings from both FOS5 and FOS6, enabling evaluation of the proposed methods under real-world conditions. To suppress artifacts, a simple linear interpolation method was proposed that preserves signal continuity and morphology while minimising distortion. The results show that this combined approach significantly increases the usability of the measurement system, enabling a more reliable analysis of seismic events and reducing the number of false alarms.