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Indonesia is a region that experiences frequent earthquakes, and therefore is highly prone to earthquake hazards. Elevated seismicity in Indonesia means that building models to understand and predict earthquake characteristics and their associated hazards is important. The goal of this research is to develop a supervised learning artificial neural network (ANN) application that can predict the magnitude of earthquake wave propagation (bar) and the direction of propagation of earthquake wave using some selected earthquake data (Mw 5-8) happened in Indonesia from 1996 to 2019. The data was taken from the United States Geological Survey (USGS) database and the Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) database. The earthquake data used in the artificial neural network application consists of a hidden layer with four neurons and seven input neurons that contain earthquake parameters, including longitude, latitude, magnitude, depth, strike, dip, and rake, as well as one output neuron. The magnitude and direction of energy propagation of the earthquakes were successfully predicted using the ANN program. Excellent agreement between the results of ANN and those of Coulomb 3.3 software strongly indicates that the ANN program can be used as an alternative to the existing Coulomb 3.3 software. The ANN model can also be further applied to other earthquake data around the world. The results are expected to contribute to the development of earthquake detection software tools. With artificial intelligence, earthquake prediction software will be more effective and can reduce the risks of failure in predicting the magnitude and direction of earthquake wave propagation. [ABSTRACT FROM AUTHOR]

Mamasa, located in West Sulawesi, Indonesia, is a populous dense area with a low seismic hazard. In this area, the question of fault reactivation was raised due to the 804 reported earthquakes from November 2018 to February 2019. This series does not show an apparently major earthquake or behaviors, such as swarms. One month earlier, on 28 September 2018, the 7.6 Mw Palu earthquake occurred at a distance of approximately 230 km north of Mamasa. It affected the occurrence of this swarm since Mamasa has many hydrothermal manifestations, which have been linked with seismic activity. To investigate the roles of these two factors, the series of earthquakes and their possible linked faults must be characterized. Specifically, the double-difference hypocenter relocation method was used to relocate the earthquakes and conduct a spatiotemporal analysis of this swarm. Furthermore, the FHD (first horizontal derivative) gravity data were used to illuminate the possible linked fault. To assess the impact of the Palu earthquake, the Coulomb stress was calculated, and it was reported that it was very small. Therefore, it played an insignificant role in triggering the swarm. In contrast, the relocated earthquakes are concentrated in an area surrounded by surface hydrothermal manifestations, and the swarm feature is consistent with the interpretation of associated seismicity. Therefore, hydrothermal activities play a critical role in triggering the swarm. [ABSTRACT FROM AUTHOR]