10:45 AM - 12:15 PM
[STT44-P08] Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
Keywords:Observation site selection, Seismic wavefield reconstruction, Process-driven approach, Sparse sensor optimization, Parameter sensitivity analysis
The development of an observation site selection method is one of the key issues for efficient data-driven modelling and data assimilation in seismology. The observation site selection method can suggest the optimal location for additional observation sites and select the set of observation sites required to estimate the seismic intensity map with sufficient accuracy in the event of a large earthquake. In the present study, the sparse sensor optimization technique, which has been recently developed in the field of fluid dynamics, is applied to seismology to propose an observation site selection method for highly accurate reconstruction of seismic wavefields.
We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments.
In the present study, a numerical experiment of the seismic wavefield reconstruction is conducted using synthetic observation data and the proposed method is verified. The effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method. Furthermore, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocenter location was obtained by investigating the characteristics of the sensitivity matrix.
We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments.
In the present study, a numerical experiment of the seismic wavefield reconstruction is conducted using synthetic observation data and the proposed method is verified. The effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method. Furthermore, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocenter location was obtained by investigating the characteristics of the sensitivity matrix.