Accurate earthquake source parameters (e.g., magnitude, source location, and focal mechanism) are of key importance in seismic source studies and seismic hazard assessments. The routine workflow of source parameters estimation consists of two steps: source location inversion and focal mechanism inversion. Separate inversion of source parameters is subject to the cumulative uncertainties of both two steps inversion processes. Markov Chain Monte Carlo (MCMC), as global optimization, has been adopted in many nonlinear inversion problems to reduce cumulative errors and provide uncertainty assessment, but the application of MCMC is strongly subject to prior information. In this study, we present a new Python package MCMTpy. MCMTpy exploits the Cut-And-Paste (CAP) algorithm and Bayesian inference, using Markov Chain to implement the source location inversion and focal mechanism inversion in one inversion workflow. The new approach can effectively reduce the prior model dependence, and is closely integrated into the current seismological programming ecosystem. To demonstrate the effectiveness of the new package, we applied the MCMTpy to the 2021 Ms 6.4 Yangbi earthquake, Yunnan, China, and 2008 Mw 5.2 Mt. Carmel Earthquake, Illinois. A comparison between our results and other catalogs (e.g., Global Centroid Moment Tensor and U.S. Geological Survey W-phase) solutions illustrates that both double-couple and moment tensor solutions can be reliably recovered. The robustness and limitations of our approach are demonstrated by an experiment with 30 different initial models and an experiment with the grid-search method.

Distributed Acoustic Sensing (DAS) can transform existing urban standard telecommunication cables into dense broadband seismic observation systems. The use of urban communication optical cables for active/passive surface wave imaging provides a new way for urban near-surface imaging and underground space detection, and has broad application prospects. A large number of recent studies have shown that making full use of higher-mode surface wave information can significantly improve the near-surface imaging effect of surface wave. In order to explore the feasibility of extraction and utilization of higher-mode surface wave in DAS records, in this paper, we derive the MF-J transformation formula suitable for extracting multi-mode surface wave dispersion curves in DAS seismic records, and successfully apply them to the Baijia Tuan DAS experiment. Based on the active source observation data, the multi-mode dispersion curve was extracted and inverted to construct a high-resolution, high-precision shear wave velocity structure profile in the study area. The research results show that the MF-J method can effectively extract the high-mode dispersion curves from the DAS active source record, also could avoid the cross artifact in the traditional F-J method. The velocity structure resolution and detection depth based on the inversion of the high-mode dispersion curves are significantly better than the inversion using only the fundamental mode. The research results of this paper reveal the possibility of using urban telecommunication cables to realize low-cost and high-resolution underground structure detection.