APPLICATION OF ARTIFICIAL INTELLIGENCE PHASENET AND GAMMA, AND NONLINLOC PROGRAM IN OPTIMIZATION OF REGIONAL TECTONIC EARTHQUAKE IDENTIFICATION ON THE ISLAND OF JAVA
The condition and amount of earthquake data in the world continues to increase along with the increasing capacity and coverage of the global seismic network. Therefore, there is a need for technology that can accommodate the increasing amount of data and process it quickly to help improve data pr...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/78931 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The condition and amount of earthquake data in the world continues to increase
along with the increasing capacity and coverage of the global seismic network.
Therefore, there is a need for technology that can accommodate the increasing
amount of data and process it quickly to help improve data processing efficiency in
identifying seismic wave phases. This can help the development of more advanced
systems, such as disaster early warning systems in Indonesia. In this study, we apply
artificial intelligence, namely PhaseNet and GaMMA, and apply probabilistic
location determination with NonLinLoc in optimizing the identification of regional
tectonic earthquakes in Indonesia, especially in the Island of Java.
In this study, seismogram data from 50 regional tectonic earthquakes on Java
Island, Indonesia in 2020 with a magnitude range of 3,28 to 5,49 were used. The
results of data processing using PhaseNet produced P-wave and S-wave picks from
the seismogram data with 189 out of 569 (33%) PhaseNet picks compared to the
BMKG catalog having a time difference below 3 seconds, which is considered a
good result. GaMMa generated a total of 44 event associations from a total of 2207
picks out of 50 events (88%) generated by PhaseNet. The arrival times of 20 events
were passed to NonLinLoc after filtering based on the Wadati diagram. Of the 20
input events, 17 events had their hypocenters estimated using NonLinLoc. The
comparison between NonLinLoc and the BMKG catalog shows that the minimum,
average, and maximum differences for determining the hypocenter location
respectively are 3.914213631 km; 206.2701712 km; and 57.36959936 km, with
58% of the events having differences in the range of 20-35 km. Overall, with good
quality input data, the application of PhaseNet, GaMMa, and NonLinLoc can
optimize the identification of regional tectonic earthquakes on Java Island.
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