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Integrating quantum clustering with unsupervised deep learning for first arrival picking in local seismic events.

Dalai B, Kumar P.

GEOPHYSICAL JOURNAL INTERNATIONAL

https://doi.org/10.1093/gji/ggaf331 

Accurate picking of first-arrival P-waves is essential for earthquake localization, subsurface imaging, and for understanding velocity structures of the Earth's interior. However, achieving reliable picks under noisy conditions remains a major challenge. To address this, the study develops an automated hybrid Quantum-AI framework that combines quantum clustering within a simulated quantum environment with an unsupervised deep learning model. The approach applies quantum principles of superposition and entanglement to enhance feature discrimination and demonstrates self-emerging intelligence—learning directly from data and achieving precise, noise-resilient results.

Fig: Continuous seismic waveform recorded at the CGM station (Jammu & Kashmir) showing automatic first-arrival detection by the proposed hybrid Quantum–AI framework.

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