Characterizing the interior structure of the Jakarta Basin, Indonesia, is important for the improvement of seismic hazard assessment there. A dense-portable seismic broad-band network, comprising 96 stations, has been operated between October 2013 and February 2014 covering the city of Jakarta. The seismic network sampled broad-band seismic noise mostly originating from ocean waves and anthropogenic activity. We used horizontal-to-vertical spectral ratio (HVSR) measurements of the ambient seismic noise to estimate fundamental-mode Rayleigh wave ellipticity curves, which were used to infer the seismic velocity structure of the Jakarta Basin. By mapping and modelling the spatial variation of low-frequency (0.124-0.249 Hz) HVSR peaks, this study reveals variations in the depth to the Miocene basement. These variations include a sudden change of basement depth from 500 to 1000 m along N-S profile through the centre of the city, with an otherwise gentle increase in basin depth from south to north. Higher frequency (2-4 Hz) HVSR peaks appear to reflect complicated structure in the top 100 m of the soil profile, possibly related to the sediment compaction and transitions among different sedimentary sequences. In order to map these velocity profiles of unknown complexity, we employ a trans-dimensional Bayesian framework for the inversion of HVSR curves for 1-D profiles of velocity and density beneath each station. Results show that very low-velocity sediments (<240 m s −1) up to 100 m in depth cover the city in the northern to central part, where alluvial fan material is deposited. These low seismic velocities and the very thick sediments in the Jakarta Basin will potentially contribute to seismic amplification and basin resonance, especially during giant megathrust earthquakes or large earthquakes with epicentres close to Jakarta. Results have shown good correlation with previous ambient seismic noise tomography and microtremor studies. We use the 1-D profiles to create a pseudo-3-D model of the basin structure which can be used for earthquake hazard analyses of Jakarta, a megacity in which highly variable construction practices may give rise to high vulnerability. The methodology discussed can be applied to any other populated city situated in a thick sedimentary basin.

A systematic pattern of destruction was observed caused by the Mw 7.5 Central Sulawesi Earthquake. In brief, the quake ruptured 180 km of Palu–Koro Fault and led to massive destruction of residential buildings in Palu city, Donggala and Sigi Regencies. In Palu city, the damage was concentrated only in Balaroa and Petobo neighbourhoods where at least 930 and 1255 houses, respectively, collapsed. Microtremor time series recorded prior to the earthquake were used to analyse the subsurface structure of Balaroa and surroundings. Surprisingly, inversion of horizontal-to-vertical spectral ratio curves was able to locate the subsurface fault crossing the Balaroa and its dipping direction. Furthermore, the velocity profile deduced from this inversion points out the importance of local geology in the massive destruction in Balaroa. The existence of a subsurface pond led to an extremely water-saturated sandy soil underneath Balaroa. A combination of high level of water saturation and high peak ground acceleration (0.5–0.7 g) was strongly suspected to be the cause of the mega-liquefaction. The high degree of ground motion (>1.54 g) of spectral acceleration 0.2 s resulted in severe damage in elevated areas in Donggala, while in the Palu Basin, ground motion as high as 1.30g of spectral acceleration 0.5 s led to the destruction of four-storey or higher buildings.

Palu City is an active seismic area in Indonesia due to the very active Palu-Koro fault system. The development of the city area, therefore, must consider the risks induced by the seismic activities. The risk assessment has to be supported by information on subsurface characteristics. The aim of this study is to investigate the characteristics of the subsurface of the area by considering the value of V s30 (top 30 m shear-wave velocity). This parameter has been related to the estimation of the site's ground shaking during the occurrence of an earthquake. The measurements taken in the deep soil sediment include the microtremor array, using the spatial auto correlations (SPAC) method, as well as the site's dominant period measurement, using the horizontal-to-vertical spectral ratio (HVSR) method. All these parameters were local site parameters, which could be subsequently related to a description of the potential impact in an area near to the epicenter. The measurement of V s30 was conducted in collaboration between the Indonesian Agency for Meteorology, Climatology, and Geophysics (Badan Meteorologi, Klimatologi, dan Geofisika) (BMKG) and the University of Indonesia (Universitas Indonesia) (UI); the overall surveys included V s30 measurements at 44 sites, microtremor array surveys at 10 sites, and the dominant period measurements at 74 sites. The overall results indicated that there is a good correlation between V s30 and the dominant period. In general, Palu City is predominantly a class-D site, but the northwest part of the Palu area is a class-C site.

No abstract

In 2018, Lombok Island was hit by a major earthquake sequence. The Indonesia Meteorological, Climatological, and Geophysics Agency (BMKG) reported that the Lombok Island earthquake sequence started with an Mw 6.4 foreshock, followed by an Mw 6.8 main shock, aftershocks of Mw 5.8 and Mw 6.2, and a second mainshock of Mw 6.9 in the eastern part of Lombok. This study presents an investigation of strong motion characteristics using the Indonesia National Strong Motion Network (INSMN) data from two accelerometer stations, the MASE station (at Praya Lombok International Airport, Lombok Island, Vs30 = 770 m/s, SB site class) and TWSI station (in Sumbawa Island, Vs30 = 1152 m/s, SB site class). Signal analysis techniques using a power spectrum via fast Fourier transform, wavelet transform and horizontal-to-vertical spectral ratio (HVSR) have been applied in this study. There are significant differences in the results (e.g., predominant frequencies, wavelets, H/V ratios, and frequencies at peak H/V ratio) for the MASE and TWSI stations, highlighting the importance of actual Vs30 profiles and the limitation of the site class system in providing necessary predictive information. The variation of the peak ground acceleration (PGA) values and the spectral amplitudes could only be explained by hypothesizing the effect of the volcanic structure of Mount Rinjani on the strong motion waveforms.