GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 1.1 GNGTS 2023 Fig. 3E shows the RMS time residuals after the tomography at each iteration, related to the four cases, and Table 1 shows the corresponding final depth positions of the events considered. Based on this plot, we can observe a significant drop of the residuals in cases A and B after the first iteration, followed by small changes of the residuals in the next iterations. Otherwise, in cases C and D, where the origin time is known, there is a smoothed decrease in the residuals before the 5 th and 6 th iterations. However, when we use the residual analysis within the iterative procedure, we observe a decrease in the time residuals, which is small in case D, but significant when the origin time is unknown (case B). The same considerations can be made for the depth positions of the localized events (Tab. 1). If we know the origin time, the difference from the true positions is very small. Otherwise, if we do not know the origin time, the improvement is significant when we apply the time residual analysis. Conclusions In this work, we present a method to improve earthquake location and the definition of the associated velocity model. The method consists of using the time residual analysis within the iterative procedure “location + travel time tomography”. For each event, the time residuals (difference between observed and computed travel times) obtained after each tomographic step from all the connected stations are converted into depth (by using the local velocity of the current model), which is then used to improve earthquake locations. To validate this approach, we tested the method using a synthetic model in four cases: with and without applying the method, and with and without considering known the origin time. The results show a significant improvement in resolution for both the earthquake positions and the velocity model, although the unknown origin time remains a critical aspect of this inversion problem. References Aghamiry H., Gholami A. and Operto S.; 2020: Wavefield inversion for microseismic imaging , in SEG Technical Program Expanded Abstracts 2020, pp. 2130–2134, Society of Exploration Geophysicists. Crosson R.S.; 1976: Crustal structure modeling of earthquake data, 1, Simultaneous least squares estimation of hypocenter and velocity parameters . J. Geophys. Res., 81, 3036-3046. Kamei R. and Lumley D.; 2014: Passive seismic imaging and velocity inversion using full wavefield methods, in SEG Technical Program Expanded Abstracts 2014, pp. 2273–2277. Kissling E., Ellsworth W.L., Eberhart-Phillips D., Kradolfer U. (1994). Initial reference models in local earthquake tomography. J. Geophys. Res. 99: 19635–19646. Lomax A., Virieux J., Volant P. and Berge C.; 2000: Probabilistic earthquake location in 3D and layered models: Introduction of a Metropolis-Gibbs method and comparison with linear locations ,