Realizing an effective earthquake early warning system (EEWS) in the case of extensive regions and noisy signals is challenging, particularly in East Java, Indonesia. This letter proposes the rapid detection of the p-wave arrival and determination of the earthquake’s hypocenter and magnitude using deep learning. The Ncheck algorithm is used for noise handling for picking the p-arrival on a multistation waveform as a form of picking target window prediction (PTWP). Then, multitarget regression (MTR) with a hard-shared orthogonal optimization model is proposed for earthquake parameter determination. The data sets used contained data of earthquakes recorded at three stations from the Indonesian seismic network in East Java; 2009–2017 data were used for training and validation, and 2019 data were used for real-time testing. The results show that the PTWP for picking p-arrival has a mean absolute error (MAE) of 0.12 s, and the MTR for earthquake magnitude, longitude, latitude, depth, and origin time detection shows MAEs of 0.21 M, 9.44, 18.72, 27.81 km, and 2.78 s, respectively.

The accurate and timely estimation of earthquake magnitude is essential for effective tsunami early warning, particularly in seismically active regions such as Indonesia. Conventional seismic approaches are often hindered by magnitude saturation in significant events (Mw > 7.5), resulting in systematically underestimated magnitudes. To address this limitation, we develop a regional peak ground displacement (PGD) scaling law using high-rate GNSS (HR-GNSS) data from 21 moderate to large earthquakes in Indonesia. Based on 87 PGD observations, we construct a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) exhibit strong concordance with catalog moment magnitudes, achieving a mean absolute deviation (MAD) of 0.21 and surpassing the accuracy of previously published global models. Retrospective analyses reveal that MPGD estimates converge within 2–3 min for well-recorded events and remain robust, even for great and tsunamigenic earthquakes. These results underscore the potential of HR-GNSS data to complement conventional seismic networks, providing rapid and reliable magnitude estimates for operational tsunami early warning in Indonesia.

Rapid and accurate detection of primary waves (P-waves) using high-rate Global Navigation Satellite System (GNSS) data is essential for earthquake monitoring and tsunami early warning systems, where traditional seismic methods are less effective in noisy environments. We applied a wavelet-based method using a Mexican hat wavelet and dynamic threshold to thoroughly analyze the three-component displacement waveforms of the 2009 Padang, 2012 Simeulue, and 2018 Palu Indonesian earthquakes. Data from the Sumatran GPS Array and Indonesian Continuously Operating Reference Stations were analyzed to determine accurate displacements and P-waves. Validation with Indonesian geophysical agency seismic records indicated reliable detection of the horizontal component, with a time delay of less than 90 s, whereas the vertical component detection was inconsistent, owing to noise. Spectrogram analysis revealed P-wave energy in the pseudo-frequency range of 0.02–0.5 Hz and confirmed the method’s sensitivity to low-frequency signals. This approach illustrates the utility of GNSS data as a complement to seismic networks for the rapid characterization of earthquakes in complex tectonic regions. Improving the vertical component noise suppression might further help secure their utility in real-time early warning systems.

Accurate and timely earthquake magnitude estimation is critical for effective tsunami early warning, particularly in seismically active regions like Indonesia. Conventional seismic approaches often suffer from magnitude saturation for large events (Mw > 7.5), leading to underestimated magnitudes. To address this limitation, we propose a novel Peak Ground Displacement (PGD) Scaling Law based on regional high-rate GNSS (HR-GNSS) data from 21 moderate to large Indonesian earthquakes. Using 87 PGD measurements, we derive a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) show strong agreement with final moment magnitudes (Mw), achieving a mean absolute deviation (MAD) of 0.21—outperforming global models. Retrospective analyses demonstrate that the PGD estimates stabilize within 2–3 minutes for well-recorded events and remain robust even for great and tsunamigenic earthquakes. These findings confirm that HR-GNSS data can complement seismic networks, enabling rapid and reliable magnitude estimation in Indonesia.