PHYSINET:PHYSICS-GUIDED WEAKLY-SUPERVISED DEEP LEARNING FOR INTERPRETABLE SEISMIC EVENT CLASSIFICATION AND INVERSE PHYSICAL DISCOVERY

Deep seismic classifiers are fast but opaque and fragile under distribution shift, while classical physical discriminants are interpretable yet require manual mea- surement at deployment. We propose PhysiNet, a physics-guided weakly- supervised framework built on one principle: physical knowledge constrains training, while inference uses only three-component time–frequency images and no auxiliary physical quantity. We instantiate the framework on the three-class earthquake–explosion–collapse task with two auxiliary targets chosen for their source physics rather than their availability: the classical lg(P/S) amplitude ra- tio — converted, to our knowledge for the first time, into a weak regression tar- get — and the P–S spectral centroid. Both quantify the radiated wavefield (a dimensionless amplitude ratio and a normalized frequency statistic) rather than propagation geometry, and are delivered as masked regression targets over a shared encoder. On DiTing 2.0, PhysiNet improves three-class event-level ac- curacy from 0.9017 to 0.9178 over a matched classification-only baseline (means over ten training seeds), each target contributing about one point alone and +1.6 points jointly. Strikingly, the lg(P/S) head attains only moderate regression fidelity (R2 =0.521): the value of weak supervision lies in constraining the representa- tion, not in measurement precision. Beyond accuracy, the trained model becomes an instrument for inverse physical discovery: aggregating its auxiliary outputs over the test population reveals class-conditional radiation regimes in the lg(P/S)– centroid plane, a physically grounded ambiguity map that co-locates residual er- rors with class boundaries, and a previously unquantified within-class trade-off between P/S energy partition and characteristic radiation frequency — within each source class, records that channel relatively more energy into P also radiate lower- frequency energy. These findings illustrate a bidirectional loop in which physics guides learning and the calibrated model, in turn, quantifies source physics at cat- alog scale.