Seismic waves are mechanical waves generated by earthquakes that travel through the Earth. Body waves consist of fast, compressional primary (P) waves and slower, shear secondary (S) waves. With large datasets of seismogram recordings, researchers train machine learning models to automatically pinpoint P‑ and S‑wave arrival times. This is essential for real‑time seismic monitoring and early warning systems.
Our Green Learning framework streamlines this process while boosting interpretability. We begin by slicing raw seismic recordings into overlapping three‑channel windows and assigning each a continuous pseudo‑label (ranging from 0 to 1) that reflects how accurately it is aligned to a P‑ or S‑wave onset. Treating these windows as 3‑channel images, we extract multi‑scale features via multiple Saab transform layers and select the most powerful features at each scale using Relevant Feature Test (RFT) modules. An XGBoost regressor then produces a continuous output signal, from which P‑ and S‑wave arrivals are simply recovered by peak detection. Compared to the SotA deep learning model EQTransformer, this model uses far fewer parameters,
