This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground-motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.

MyShake is a crowdsourcing approach to earthquake early warning that harnesses the sensors in private smartphones.

We consider the nth eigenvalue as a function on the space of self-adjoint regular Sturm Liouville problems with positive leading coefficient and weight functions. The discontinuity of the nth eigenvalue is completely characterized. Academic Press

New oscillation criteria are established for the equation py q qy s 0 that are different from most known ones in the sense that they are based on the informaw . tion only on a sequence of subintervals of t , ϱ , rather than on the whole 0 half-line. Our results are more natural according to the Sturm Separation Theorem and sharper than some previous results, and can be applied to extreme cases such ϱ Ž . as H q t dt s yϱ. ᮊ

The eigenvalues of Sturm Liouville (SL) problems depend not only continuously but smoothly on the problem. An expression for the derivative of an eigenvalue with respect to a given parameter: an endpoint, a boundary condition, a coefficient or the weight function, is found.