jrfapp: A Python Package for Joint Inversion of Apparent S-Wave Velocity and Receiver Function Time Series

Receiver function (RF) inversion is a well-established method to quantify a horizontally layered approximation of the S-wave velocity structure beneath a seismic station. It is well-known that the RF inverse problem is highly non-unique, and various tools such as joint inversion with other seismological observations exist that may overcome this problem. We present a joint inversion framework along with a Python package that implements the joint inversion of RF and the apparent S-wave velocity ( V S,app ). Our implementation includes a pseudo-initial model estimation, which helps address the inherent non-uniqueness of the joint inversion of RFs and V S,app . This implementation enhances the resolving power, enabling estimation of S-wave velocities with resolution approaching that of deep controlled source seismic methods. As an illustration, we showcase an example from a permanent station in the Makran subduction zone southeast of the Iranian Plateau and two other stations in the supplementary material. We compare our joint inversion results with several S-wave velocity models obtained through a deep seismic sounding profile and joint inversion of surface wave dispersion and RFs. This comparison shows that although we note a slightly lower sensitivity of our proposed method at greater depths (beyond 50 km), the method yields much better results for shallow structures. Our inversion code provides a powerful, accessible software package that has superior resolving power at shallow depth compared to RFs-surface wave inversion codes. Furthermore, the fact that only one data-derivative is used, makes this inversion code extremely easy to use, without the need for complementary datasets.