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

SUMMARY The precise picking of first arrivals in seismology is very important for characterizing and monitoring the earthquakes. Similarly, in exploration seismology, identifying the refraction phases is crucial for building accurate velocity models. However, it's identification poses significant challenges in the presence of noise, especially for smaller magnitude earthquakes. Here, we propose a method to identify the first arrival P-wave from local earthquake data, employing time-frequency mapping of raw seismograms using the Generalized S-Transform and then extracting optimal deep encoded features utilizing the convolutional neural network-based unsupervised deep learning approach without the need for labelling the data. The statistical and transformational metrics, generated from both the deep encoded features and the original waveform, are combined to create an enriched feature space. Quantum clustering is then applied to this combined feature space to identify patterns or clusters that distinguish useful waveform sections from noise. This waveform-level selective identification and segmentation facilitate the determination of first arrival times within the relevant sections. The effectiveness of this method is first validated on a suite of synthetic data contaminated with various level and types of noise, and then applied to the observed data from the STEAD global dataset and seismic stations from the Jammu and Kashmir Himalaya. The method demonstrates stable picking performance under noisy conditions when compared to STA/LTA, AIC Picker and the unsupervised deep learning with classic K-Means. It also shows a broadly similar trend to supervised models such as PhaseNet and EQTransformer, and is computationally efficient, even in low signal-to-noise ratio conditions.