Diagnosing and Breaking Amplitude Suppression in Seismic Phase Picking Through Adversarial Shape Learning

Deep learning has revolutionized seismic phase picking, yet a paradox persists: high signal-to-noise S-wave predictions consistently fail to cross detection thresholds, oscillating at suppressed amplitudes. We identify this previously unexplained phenomenon as amplitude suppression, which we diagnose through analyzing training histories and loss landscapes. Three interacting factors emerge: S-wave onsets exhibit high temporal uncertainty relative to high-amplitude boundaries; CNN's bias toward sharp amplitude changes anchors predictions to these boundaries rather than subtle onsets; and point-wise Binary Cross-Entropy (BCE) loss lacks lateral corrective forces, providing only vertical gradients that suppress amplitude while temporal gaps persist. This geometric trap points to a shape-then-align solution where stable geometric templates must precede temporal alignment. We implement this through a conditional GAN framework by augmenting conventional BCE training with a discriminator that enforces shape constraints. Training for 10,000 steps, this achieves a 64% increase in effective S-phase detections. Our framework autonomously discovers target geometry without a priori assumptions, offering a generalizable solution for segmentation tasks requiring precise alignment of subtle features near dominant structures.

MAX: Masked Autoencoder for X-ray Fluorescence in Geological Investigation

Pre-training foundation models has become the de-facto procedure for deep learning approaches, yet its application remains limited in the geological studies, where in needs of the model transferability to break the shackle of data scarcity. Here we target on the X-ray fluorescence (XRF) scanning data, a standard high-resolution measurement in extensive scientific drilling projects. We propose a scalable self-supervised learner, masked autoencoders on XRF spectra (MAX), to pre-train a foundation model covering geological records from multiple regions of the Pacific and Southern Ocean. In pre-training, we find that masking a high proportion of the input spectrum (50\%) yields a nontrivial and meaningful self-supervisory task. For downstream tasks, we select the quantification of XRF spectra into two costly geochemical measurements, CaCO$_3$ and total organic carbon, due to their importance in understanding the paleo-oceanic carbon system. Our results show that MAX, requiring only one-third of the data, outperforms models without pre-training in terms of quantification accuracy. Additionally, the model's generalizability improves by more than 60\% in zero-shot tests on new materials, with explainability further ensuring its robustness. Thus, our approach offers a promising pathway to overcome data scarcity in geological discovery by leveraging the self-supervised foundation model and fast-acquired XRF scanning data.