Palu Earthquake Research Articles

Large Scale Simulation of 3D Fault Rupture Subjected to Far‐Field Loading With PDS‐FEM: Application to the 2018 Palu Earthquake

AbstractSimulating dynamic rupture of fault systems can be computationally demanding, as it requires reproducing complex fault geometry and accurately capturing waves propagating away from the rupture front. It is in particular challenging to predict an initial stress state consistent with fault geometries, heterogeneous distribution of surrounding materials, and far‐field tectonic loading. While standard techniques such as contact analysis and Lagrangian multipliers can be used to model the fault, it can lead to significant computational overhead in FEM. We extended Particle Discretization Scheme‐FEM, which provides numerically efficient crack treatment, without requiring contact analysis, to simulate dynamic fault rupture as a frictional crack propagating along a pre‐existing shear crack surface. Initial stress, which is consistent with initial frictional forces, material distribution and fault geometry, is derived using Coulomb friction and far‐field boundary conditions. The study first demonstrates the ability of the numerical method to reproduce a 2D ideal supershear scenario, and the underlying Burridge‐Andrew rupture mechanism. The methodology is then applied to the large scale simulation of the 2018 Palu earthquake on the Palu‐Koro fault. The simulation successfully reproduces the early and sustained supershear rupture which was observed for the Palu earthquake. Also, it indicates that the presence of an off‐fault damage zone can contribute to the low rupture velocity measured during the earthquake. Unlike sub‐Rayleigh earthquakes, the shockwave propagation was observed to lead to significant amplitudes of the ground motion even far from the fault.

Triggered and recurrent slow slip in North Sulawesi, Indonesia

Nearby faults interact with each other through the exchange of stress. However, the extent of fault interaction is poorly understood. In particular, interactions may lead to slow-slip activity, resulting in episodes of transient surface motion. Our study concentrates on Northwest Sulawesi (Indonesia), which hosts two fault zones with potential for major earthquakes and tsunamis: the strike-slip Palu-Koro fault and the Minahassa subduction zone. Thanks to a 20-year-long effort in geodetic monitoring, we are able to identify multiple periods during which surface velocities deviate from their interseismic trend. We use a Bayesian methodology with forward predictions of slip on the two fault interfaces to match the observations following the 2018 Mw7.5 Palu earthquake, and infer that both deep afterslip on the Palu-Koro fault and slow slip on the Minahassa subduction interface have caused the observed transient surface motion. This finding represents the first recording of a slow slip event on the Minahassa subduction interface. We also infer that the subduction interface and the strike-slip fault are likely interacting on a regular basis.

Investigation of Liquefaction in Balaroa, Petobo, and Jonooge (Central Sulawesi, Indonesia) Caused by the 2018 Palu Earthquake Sequence

The liquefaction that occurred in the city of Palu on September 28, 2018, was caused by a series of significant earthquakes that took place in a relatively short time around 25 minutes after the main earthquake of magnitude 7.5. This event was followed by aftershocks of magnitudes 6.4, 6.2, and 6.1. The magnitude 6.2 aftershock occurred at 10.16 UTC, while the magnitude 6.1 aftershock occurred at 10.25 UTC. These were both located very close to the liquefaction locations in Balaroa, Petobo, and Jono Oge. We investigated the mainshock and the three aftershocks using the NCEER method based on Vs30 measurements and data from the drill liquefaction locations at Balaroa, Petobo, and Jono Oge. We found that the liquefaction was not only caused by the main earthquake but also by the subsequent aftershocks that occurred within 25 minutes after the mainshock.

Exploring tsunami generation and propagation: A case study of the 2018 Palu earthquake and tsunami

The tsunami disaster in Palu, Indonesia, on September 28, 2018, caused many casualties and damage along Palu Bay, Indonesia. Prior to the tsunami, an earthquake was recorded 25 km northeast of Donggala. The analysis suggests that the tsunami was triggered by a landslide beneath the waters of Palu Bay. The investigation then focused on the earthquake's impact on the tsunami. This study conducts simulations to discern the dominant causes of tsunamis, comparing the effects of landslides with earthquakes. Three distinct tsunami sources were identified: earthquake-only (T-E), earthquake and submarine landslide (T-EL), and submarine landslide-only (T-L). Interpretation of tsunami source parameters for underwater landslides is carried out accurately. A nesting grid numerical method was employed to increase resolution and accuracy in the simulations. This study accurately interprets tsunami source parameters using data from the Bathymetry National Data-Geospatial Information Agency (185 m resolution) and the field survey data from Baruna Jaya IV-BPPT Research Vessel Indonesia (25 m resolution), thereby contributing to the creation of a tsunami inundation model map. This map serves to inform preparedness efforts and guide recommendations for infrastructure development in the Palu Bay area. The tsunami inundation model map is verified with field data and estimated using tide gauge monitoring data. The characteristics of the tsunami waves on September 28, 2018, reflect a T-EL-type tsunami source, while the T-L type source produces tsunami waves with longer periods compared to earthquake-generated tsunamis (T-EL). The T-E type, located on land, had minimal influence in causing the tsunami, despite the destructive potential of the shallow-depth M7.5 earthquake.

Seismic Deformation Analysis of the 28th September 2018 Palu Earthquake (7.5 Mw) Using InaCORS Station Data and Okada Model

This study employs the Okada Method to analyze the horizontal seismic deformation of the Palu earthquake on September 28, 2018, with a magnitude of 7.5 Mw. Data from InaCORS stations (WATP, CPRE, CPAL, TOBP, and CMLI) strategically positioned near the earthquake epicenter were processed using Gfortran software, and deformation was mapped using GMT software. The analysis focuses on the 100 Days of Year (DOY) period from August 6 to November 28, 2018. Results indicate that during the co-seismic phase (DOY 272), InaCORS stations experienced deformations ranging from 477.130 mm to 7.7852 mm. The magnitude of deformation varied based on station proximity to the epicenter, with the largest displacement observed at TOBP and the smallest at CPRE. Station movements were divergent, with northern stations shifting northward and southern stations moving southward. Subsurface slip reached 1449.23 mm, affecting an area measuring 145 km by 76 km at a depth of 8 km, dip of 65˚, strike of 351˚, and rake of -46˚. These findings contribute valuable insights into the seismic impact on the Earth's crust, aiding seismic hazard assessments in the region

Analysis of historical cases of liquefaction (sand boils) case study: Kelapa Gading Residence, Sigi Regency, Central Sulawesi Province

The Palu earthquake on September 28th, 2018, caused liquefaction in several locations in Central Sulawesi. According to The National Disaster Management Authority of Indonesia data, five prominent areas with massive impacts experienced liquefaction in flow liquefaction: Balaroa, Petobo, Jono Oge, Lolu, and Sibalaya. Meanwhile, the locations that experienced liquefaction in the form of sand boils needed to be recorded optimally. This research aims to see the parameters of soil layer behavior in areas that experience liquefaction in the form of sand boils. The study was conducted in Kelapa Gading housing, Sigi Regency, Central Sulawesi. Based on the initial survey, this location experienced sand boils in several areas. The research used the CPT-based liquefaction triggering procedure method and soil behavior index method. The result showed that the liquefaction potential was found at a depth of 2.5-3.3 meters for PGA 0.34 and -2.5-(-3.5) for PGA 0.68. When receiving cyclic load, the soil layer with an Ic value > 2.60 with a thickness of 1.6 m at the upper layer can withstand liquefaction with a thickness of 0.8 meters in the form of quicksand on the surface and only causes sand boils. Sand boils would not happen in the Kelapa Gading area if the excess pore water were redirected to discharge wells.

2.5D Time Series of Postseismic Deformation Following the 2018 Mw 7.5 Palu Earthquake Using Sentinel-1 InSAR

The observed Displacement of Line of Sight (LOS) InSAR can only measure the displacement towards and away from the satellite sensor, making it difficult to provide an intuitive depiction of the deformation source. In the previous studies, research on the characteristics of postseismic deformation mechanisms was conduct by InSAR but only in the vertical component. Therefore, our study attempts to complement this deformation picture by considering both the horizontal and vertical components. It is hoped that this will provide a more comprehensive understanding of the postseismic deformation mechanism of the 2018 Palu earthquake. In this study, we attempted to estimate the 2.5-D surface deformation resulting from the 2018 Palu Earthquake using Sentinel-1 data. The data was then modeled using exponential and logarithmic functions to understand the characteristics of the postseismic deformation mechanism. The 2.5-D surface deformation exhibited variations in horizontal and vertical motions. The East-West (EW) displacement values showed a maximum value of -66.19 mm in the east direction, while the maximum value in the west direction is 75.23 mm. On the other hand, for the Up-Down (UD) displacement, there was a maximum subsidence of -73.40 mm and a maximum uplift of 67.28 mm. To study the characteristics of the transient postseismic deformation, observations were made at 14 points. Postseismic deformation was observed at all locations in the study area. During the period 2018-2021, the time series of the north and east components of the postseismic transients were analyzed using logarithmic and exponential functions. The modeling results showed that the Root Mean Square Error (RMSE) value for the exponential model is 81.80 mm, while for the logarithmic model is 81.38 mm. Therefore, the logarithmic model demonstrated a better fit, indicating that the postseismic deformation mechanism is influenced by afterslip.

Machine Learning for Landslide Susceptibility Mapping Using Phyton in Sigi Biromaru Area (Near Palu), Central Sulawesi, Indonesia

Sigi Biromaru is near Palu City; both experienced the Palu earthquake on 28 September 2018. Unlike Palu City, a flat area, Sigi Biromaru is hilly, so it experienced landslides after the big earthquake. This study performed landslide susceptibility mapping for Sigi Biromaru using a machine learning method, namely random forest. Nine parameters were used, i.e., altitude, slope, lithology, peak ground acceleration, land cover, river density, lineament density, rainfall, and aspect. The total data points were 530, half landslide samples and the other half non-landslide samples. We used 70% of the data points to train the model and the rest to evaluate the model. The landslide susceptibility map produced by random forest with no hyperparameter tuning has an area under the curve (AUC) of 0.94 for the success rate and 0.91 for the predictive rate. This study used a novel method in which the non-landslide locations represent the safe zone area, sampled randomly from the lowest class of landslide susceptibility area based on a bivariate statistical model. This study is highly recommended for the initial phase of landslide hazard mitigation.

The impact of Coulomb stress changes of the 2018 Mw 7.5 Palu earthquake, Indonesia

The impact of Coulomb stress changes of the 2018 Mw 7.5 Palu earthquake, Indonesia

Estimating tidal inundation in the aftermath of the 2018 Palu earthquake

Estimating tidal inundation in the aftermath of the 2018 Palu earthquake

Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model.

Overview of Flowslide in Petobo during liquefaction of the 2018 Palu Earthquake

Massive flowsides inducing deformation of about 1 km on gentle slope occurred in Petobo during liquefaction of the 2018 Palu Earthquake. The liquefaction mechanism attributed to this enormous lateral deformation is not fully understood yet. In fact, a comprehensive understanding of this phenomenon mechanism is crucial to mitigate similar disasters in the future. This manuscript describes the flowslide observations in Petobo based on geotechnical investigations, including trench, geophysical survey, CPT, boreholes, and laboratory testing. The investigation showed that the flowslide location consisted of a liquefaction layer with a cap layer on it. The liquefaction layer consists of two successive liquefied layers, the upper layer is finer and less permeable than that of the one below. With the upper layer less permeable, this may result in an increase in the void ratio due to void redistribution distributed in the liquefied upper layer. This study demonstrates that variations in liquefied layer conditions have a major effect on whether flowslide due to liquefaction occurs or not. The results of this study are expected to enrich our knowledge related to liquefaction-induced flowslide and can be used as a basis to evaluate flowslide potential in other locations.

Modeling and mapping the environmental impact of debris flow hazard on alluvial fans for sustainable development in Bangga and Poi Villages, Sigi, Central Sulawesi

On September 28, 2018, an Mw 7.5 Palu earthquake triggered massive landslides upstream, followed by 24 debris flood events that spread to 15 villages in Sigi from September 2018 to December 2021. Debris flow and flash floods on alluvial fans inundated lowland communities, causing severe property destruction and structural damage to bridges and roadways and resulting in an estimated 900 damaged houses. Understanding their historical occurrence is essential to sustainable fan development and minimizing their threat to infrastructure and human life due to their severe geohazard potential. Poi and Bangga Villages were affected by the disastrous debris flood in Sigi Regency, Central Sulawesi. This study aimed to create a landslide inventory map, a back-analysis model, and a damage and loss assessment (DaLAs) to evaluate the potential hazard and environmental impacts of debris flow on Sigi’s alluvial fans. The result of landslide mapping showed more than 400 mapped landslides within Bangga Village in various sizes and a massive landslide within Poi Village were digitized. Then, the back-analysis model overpredicted flow direction due to vegetation, infrastructure, and road information not covered by the digital elevation model (DEM). Finally, DaLAs shows the losses caused by damaged buildings were estimated at around 65.7 and 7.4 billion rupiah in Bangga and Poi Villages, respectively.

Axial and lateral bearing capacity assessment of bored piles on medium-dense sand and liquefiable potential based on numerical simulation

Liquefaction on bridges can cause structural failure due to loss of soil strength. The Showa Bridge that collapsed in 1964 Niigata earthquake was caused by buckling in piles as the soil layer liquefied and friction losses. In addition, there was a case of Palu IV bridge collapse in Indonesia during the recent 2018 Palu earthquake, which indicated have been affected by earthquakes and liquefaction. This study examines the reliability of the bored pile foundation of the Kretek 2 Bridge in Bantul Regency, Yogyakarta Special Region Province, for axial and lateral bearing capacity. This research method uses the soil structure interaction and 3D numerical simulation by Midas Civil and Midas GTS NX pile modeling. The pile driving analysis and lateral static loading test were also conducted, which can be compared to MIDAS results. For the foundation modeling result, there is an increase in axial and moment forces but a decrease in shear forces during liquefaction. While the lateral displacement increases significantly in the liquefaction state, foundation performance is still stable. Therefore, the foundation of the kretek 2 bridge is sufficient to support axial and lateral load both in a static state and a liquefaction state.

Laboratory earthquakes decipher control and stability of rupture speeds

Earthquakes are destructive natural hazards with damage capacity dictated by rupture speeds. Traditional dynamic rupture models predict that earthquake ruptures gradually accelerate to the Rayleigh wave speed with some of them further jumping to stable supershear speeds above the Eshelby speed (~sqrt{2} times S wave speed). However, the 2018 Mw 7.5 Palu earthquake, among several others, significantly challenges such a viewpoint. Here we generate spontaneous shear ruptures on laboratory faults to confirm that ruptures can indeed attain steady subRayleigh or supershear propagation speeds immediately following nucleation. A self-similar analysis of dynamic rupture confirms our observation, leading to a simple model where the rupture speed is uniquely dependent on a driving load. Our results reproduce and explain a number of enigmatic field observations on earthquake speeds, including the existence of stable subEshelby supershear ruptures, early onset of supershear ruptures, and the correlation between the rupture speed and the driving load.

Feasibility Study of Palu Bridge III Structure

The Palu Earthquake on September 28th, 2018, which was followed by a tsunami and liquefaction, caused massive damage to infrastructure in Palu City and its surroundings, include The Palu III Bridge as an Australian steel truss structure with a span of 2 x 60 m. The need for an evaluation of the post-earthquake bridge structure can be done by Rapid Visual Screening (RVS) method to detect the transverse and longitudinal direction of the bridge, cracks in the concrete structure, failure to steel truss joints, displacement of expansion joints, etc. The degradation of the Palu III Bridge was seen in the expansion joint, massive rust spots in the transverse and longitudinal girders of the bridge, in the area near the east support, the asphalt layer apart, slope of concrete pile foundation and supported rail deflected. The Rapid Visual Screening results of the Palu III Bridge structure can be conclude as follow, the overall condition of the bridge is still in moderate condition, several parts of the bridge should be maintaining and rehabilitating, the bridge pier and abutment area should be cleaned due to a lot of garbage. In general, Palu III Bridge able to function as initial condition.

Vertical deformation model on postseismic phase using exponential and logarithmic function based on InSAR

The Palu MW7.4 earthquake occurred on September 28, 2018, with the epicenter at 119.86°, 0.72°. The severe shaking caused severe damage in Central Sulawesi, especially in Palu. We conducted a postseismic deformation study to determine the deformation pattern and reduce future earthquakes' impact. Interferometric Synthetic Aperture Radar (InSAR) data were processed using LiCSBAS to get the time series. The time series data were fitted to exponential and logarithmic functions to determine the mechanism of postseismic deformation. The exponential model identified the influence of the viscoelastic mechanism, and the logarithm identified the afterslip mechanism. The Palu earthquake was fitted to logarithmic and exponential, but the logarithmic was more significant than an exponential function. Afterslip mechanism predominates, and viscoelastic mechanisms play a minor role in this postseismic deformation.

Evaluating stakeholder structure and private actor involvement on the distribution of relief goods considering a partly centralised decision

ABSTRACT Relief distribution involves numerous stakeholders with various roles and different structures regarding stakeholders’ coordination. This paper proposes a model to evaluate various stakeholder structures for distributing relief goods. Unlike previous research, this model evaluates the coordination structure, assuming no single centralised decision-maker exists. The proposed model is developed based on the decomposition-based framework, assuming a partly centralised decision. The proposed model also integrates road network restoration into the model by capturing the disrupted road network. The role of the private sector is elaborated by exploring the impact of private business involvement in food-related distribution and road network restoration. The proposed model is applied to an actual case, namely the Palu earthquake and tsunami in 2018. We found that the multifocal structure performs better in distributing relief goods. Furthermore, the involvement of the private sector succeeds in minimising the unsatisfied demand.

28th September 2018 Mw 7.5 Sulawesi Supershear Earthquake, Indonesia: Ground effects and macroseismic intensity estimation using ESI-2007 scale

The 28th September 2018 Sulawesi Supershear earthquake (MW 7.5) was one of the deadliest earthquakes in the recent history of Indonesia causing ∼4000 causalities. The earthquake caused a ∼ 177 km long surface rupture along the Palu-Karo fault. Apart from surface rupture, the earthquake caused extensive earthquake environmental effects (EEEs) around the Palu-Donggala area of Central Sulawesi, Indonesia, which includes tsunami, coastal landslide, liquefaction, ground cracks and more than 7300 landslides in hilly areas. Initial post-event analysis and reports assigned a Modified Mercalli Intensity (MMI) of VII to VIII in Palu City and the surrounding area. Building damage and ground effects caused by the earthquake suggested that seismic intensity was understated. Here we applied the EEEs information from field survey data, published reports, and remote sensing tools to determine macroseismic intensity using the Environmental Seismic Intensity (ESI-07) Scale. The ESI-07 intensity derived from the ground effects suggests the maximum intensity of X-XI, which is 3–4° higher than the traditional intensity estimated by the United States Geological Survey (USGS) and the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). ShakeMaps were generated considering the ESI-07 values. The ShakeMap was compared with the instrumentally derived ShakeMap for the Palu earthquake, which proves that the ShakeMap prepared from the instrumental data or structural damage data is underrated. We argue that proper documentation of the EEEs is necessary for such damaging earthquakes for future earthquake hazard mapping and planning in the study area and other earthquakes in Indonesia. In addition, this will help in defining the on-fault and off-fault damage zone towards reducing the seismic risk of the Palu Donggala area.

Tidal triggering of seismicity in the region of Palu, Central Sulawesi, Indonesia

This paper investigates the correlation between tidal stress and earthquakes for periods ranging from hours to months in the limited zone of the Palu region (Central Sulawesi, Indonesia). Through Schuster and binomial tests, we examined the relation between the seismicity (time density of seismic events) and tidal potential arising from the Moon and Sun, using all tidal components simultaneously and focusing on the estimation of specific terms. The results show significant correlations between the seismicity and tidal potential for S2 (0.5 d) and O1 (1.075 d) tidal components in the case of solely isolated earthquake events, particularly for shallow earthquakes. Meanwhile, there is a strong relationship between aftershocks and tidal components larger than the Mf period (13.661 d). Finally, the analysis of the temporal variation of the earthquake-tide relation reveals an optimal correlation for about six years before the 2018 great Palu earthquake. The correlation becomes insignificant afterwards.