A Mw 6.1 earthquake on 25 February 2022, at around 8:39 a.m. local time, struck Pasaman Barat Regency, West Sumatra, Indonesia, and was felt in Singapore and Malaysia. The hypocenter of this earthquake was 12 km deep and preceded by an Mw 4.9 foreshock a few minutes earlier. The earthquakes originated on a blind fault and triggered a landslide at Mount Talamau. Herein, the slip distribution and asperities along the plane fault during the earthquake were examined by teleseismic inversion and the fault location was identified by Global Gravity Model plus (GGMPlus) satellite gravity data. The slip distribution was calculated from the source parameters (strike: 136°; dip: 70°; rake: 174°) by inversion techniques based on teleseismic data. Based on the slip distribution, the earthquake was generated by stress from the Sianok fault that spread to the north and reached the uncertain fault segment in the Talamau area. In addition, the results of the First Horizontal Derivative and Second Vertical Derivative from the GGMplus data revealed a straight Simple Bouguer Anomaly pattern, confirming the existence of the uncertain Talamau fault as part of the Great Sumatra Fault. This work shows the potential application of the combination of teleseismic and gravity observation for delineating the fault structure that caused the 2022 Mw 6.1 Pasaman earthquake, which can also be applied to other locations of similar geological backgrounds.

Sea level rise monitoring using tide gauge data is done continuously so that long period data is available. The objective of this research is to analyze sea level rise from long period tide gauge data. This research uses tide gauge data in Semarang and Prigi station period 1996 to 2015. Data processing is done using Matlab 2013b to plot sea level rise data and to calculate the the trend of sea level rise. Initial stage of data processing is quality control of tide gauge data by eliminating outlier data with confidence level of three sigmas and filled empty data with NaN (Not A Number). Shifting correction is done if there is reference bias at some period of data recording at a station. Processing followed by calculation of monthly MSL (Mean Sea Level) and SLR (Sea Level Rise) with linear regression to calculate trend value of sea level rise. The result of this research is at Semarang tidal station produces SLR 4,4 mm/year with SEE 0,1 mm. At Prigi station, SLR calculation is divided into three periods namely 1996 to 1999, 2002 to 2009 and 2010 to 2015 due to more than 1 year data vacuum. Data period 1996 to 1999 shows the falling in sea level about 10.2 mm/yr with SEE 0.3 mm, 2002 to 2009 data period produces sea level rise 25.5 mm/yr with SEE 0.2 mm, and 2010 to 2015 data period shows the falling in sea level about 0.07 mm/yr with SEE 0.08 mm. The conclusions of this study are the rise and fall of sea level influenced by EI Nino and La Nina phenomena and climate change.

We analyze the Global Positioning System (GPS)-derived strain rate distribution to investigate active crustal structure in Central Java, Indonesia, using ten years (2010–2019) continuous and permanent observation data. Central Java is the third-most populous province in Indonesia where postseismic deformation of devastating Yogyakarta earthquake in 2006 might influence the GPS data. The postseismic extensional response might overshadow the low contractional process due to active tectonics deformation. A decomposition method of a calculated strain rate shows a long wavelength feature with the extensional region in the vicinity of the 2006 Yogyakarta earthquake may reflect the postseismic process remain active. The short wavelength pattern is sharpening potential active tectonics dominated by oblique Northwest-Southeast dip-slip motion with East-West left-lateral sense. Our result demonstrates essential implications for assessing future seismic hazard potential within a low strain rate such as the Central Java region.

The geoid is the ideal height reference field that used as the basis of the height measurement and also transferring the GNSS height data to become the practically utilized orthometric height data. In this study, 7 Global Geopotential Models (GGMs) have been evaluated and applied for precise local geoid modelling of Java Island. The Evaluation had been done through the comparison, absolutely and relatively, with 44 co-site GPS-levelling data. Base on the evaluation results, the optimal GGM had been utilized on precise local geoid model using the standard remove-restore method and the residual geoid has been computed using the 2D-FFT method. Effect of degree and order of GGM to the local geoid have also been investigated. The comparison of The GGMs and GPS-levelling showed that the EGM2008 gave best results with the mean difference value of 0.99 and 0.701, absolutely and relatively, and also shows consistency over the distance. The effect of degree and order of EGM2008 to the accuracy of the local geoid model ranged from 0.706 to 0.596 m from degree 2190 and 750. Finally, the local geoid model derived using the full resolution of EGM2008 has been fitted to the local reference, in this case, the GPS-levelling data with an accuracy of 0.204m.