ACADSTAFF UGM
| Title | : | Toward waveform-based earthquake source parameters determination with a minimum number of stations |
| Author | : |
ANANG SAHRONI (1) Theodosius Marwan Irnaka, S.Si., M.Sc., Dr. (2) Dr.rer.nat. Wiwit Suryanto, S.Si., M.Si. (3) |
| Date | : | 7 2021 |
| Keyword | : | inversion.,seismology,modeling inversion.,seismology,modeling |
| Abstract | : | With the growing number of earthquake data combined with the processing speed of the next generation of quantum based computers, earthquake source parameter determination using their full-waveform characteristics may be carried out in the future. The aforementioned source determination can be performed using full-waveform inversion, source imaging, or location-based on knowledge and learning. In this research, we perform a preparation study to perform earthquake source determination based on knowledge/learning. Ideally, the learning method requires a statistically sufficient dataset that can map the possible location and mechanism of the earthquake which can happen in the future. However, in real applications, we rarely have such data and information. Therefore, in this study, we generated a set of synthetic datasets, which then can be used as the input of the learning process. We focused on the Palu-Koro fault system located in Sulawesi, Indonesia. This fault system is the one that is responsible for 2018 earthquake causing 4340 casualties. The research was performed by carefully studying a possible fault model from several works of literature. We obtained the result as the following: the dip of the fault is 79 degrees, the rake of the fault is -13.05 degrees, whereas the strike of the fault depends on the segment of the fault. Afterward, we created a grid that represents the possible location of the hypocenter of the earthquake with a horizontal spacing and vertical spacing. The simulation is carried out using SPEFCEM3D Globe based with a similar with the 2018 earthquake. The results are a dataset collection that can be used as a training input for the learning-based source parameter determination method. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
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