Focal Mechanism and Moment Tensor Determination for Tsunami Early Warning Systems: A Literature Study (2018–2024)

Focal mechanism and moment tensor are critical parameters for seismology required to be calculated promptly after the location and magnitude of earthquake have been determined. The calculations are needed to identify fault geometry, interpret forces acting on the fault, and evaluate potential damage in affected areas. These processes are complex and time-consuming, requiring significant effort to produce accurate results. Therefore, this review aims to present a systematic overview of recent studies and trends related to both concepts. The process was achieved by collecting quality studies from academic databases and digital libraries focusing on 2018 to 2024, using keywords such as earthquake, focal mechanism, inversion, polarity, moment tensor, and tsunami. A total of 113 relevant studies were selected and the results showed attention was mostly on wide usage of conventional classical methods. However, the methods require more calculation time, are subjective, have limitations in analyzing complex data, and challenging to apply in emergency situations demanding rapid decision-making due to high operator control. Several studies proposed improving objectivity and accuracy through artificial intelligence (AI), including machine learning and deep learning. AI methods could offer novel opportunities in seismology considering the potential to enhance analysis efficiency and accuracy of focal mechanism and moment tensor, providing additional insights into earthquake characteristics quickly, accurately, and automatically. This review aimed to guide the selection of appropriate methods for determining focal mechanism and moment tensor in Tsunami Early Warning Systems (TEWS) while identifying challenges and potential future study directions.