Proceedings of the 12th International INQUA meeting on paleoseismology, active tectonic and archaeoseismology

160 PATA Days 2024 Fig. 1: Seismicity map of Bolivia in its regional frame (1913-2021). M E T H O D For the purposes of this study, a protocol was devised that combines manual picking and fully automated deep-learning methods (Zhu and Beroza, 2019) followed by quality control approaches. Upon data collection from our network, seismic waveforms undergo processing through Seisan (Havskov, et al., 2020) for P and S phase picking, phase association, phase amplitude measurements, and hypocentral locations with Hypo71 (Lee and Lahr, 1972). The 1D velocity model we extracted for the Cochabamba region, as referenced in Ryan et al. (2016), exhibits Vp and Vs velocities comparable to those documented in the literature for the eastern front of the Andes in northwestern Argentina, as reported by Ammirati et al. (2015) and Venerdini et al. (2020). To assess potential biases arising from the 1D velocity model, we employed the joint hypocenter, velocity model, and station coeJcient determination method of VELEST (Kissling et al., 1994), along with the Joint Hypocenter Determination (JHD) method (Pujol et al., 1992). To identify the seismic sources of earthquakes in the region, we conduct full waveform inversion based on the methodology outlined by Delouis (2014). This algorithm will provide parameters such as strike, dip, rake, moment magnitude, and a best-Gt solution for depth.