Abstract Stress drop, defined as the difference in shear stress on a fault before and after an earthquake, is a key parameter for characterizing earthquake source processes and for understanding fault strength, rupture dynamics, and seismic hazard. We present SDpy (stress drop in Python), an open-source Python package for estimating earthquake stress drop based on the spectral fitting and spectral ratio methods. SDpy supports analysis of P waves, S waves, and coda waves with either single- or multi-window approaches, and incorporates multiple theoretical source models, including the Brune and Boatwright models. The package is modular, extensible, and user-friendly, accommodating a variety of processing scenarios, such as single- versus three-component recordings, single- versus multi-station data, and single versus multiple empirical Green’s functions. This flexibility facilitates rapid processing and comparative studies of earthquake source characteristics, providing a practical tool for seismologists investigating rupture behavior in diverse tectonic environments. Such versatility makes SDpy suitable for a wide range of research and operational applications in observational seismology. Application of SDpy to a repeating earthquake sequence targeted by the San Andreas Fault Observatory at Depth yields stress-drop estimates consistent with the previous study, demonstrating the tool’s robustness and practical reliability.
Zhang et al. (Fri,) studied this question.