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The 2019 Mw 7.0 Banten, Indonesia, intraslab earthquake: investigation of the coseismic slip, tsunami modelling and Coulomb stress change

The 2019 Mw 7.0 Banten, Indonesia, intraslab earthquake: investigation of the coseismic slip,... The 2019 Mw 7.0 Banten, Indonesia, earthquake occurred at a 49 km depth in a relatively unknown region, where the geological structure did not clearly show the fault. In this study, we use the Global Navigation Satellite System data to analyse the fault source of the earthquake. Following the earthquake’s focal mechanism, we modelled a total of four fault models using two possible fault strikes, with each of the fault strikes investigated for shallow top depth and deeper top depth. This study also utilises the tide gauge data to confirm the tsunami waveform, modelled using the estimated coseismic slip. We present evidence of the shallow rupture of the 2019 Mw 7.0 Banten, Indonesia, intraslab earthquake from an ENE-WSW fault direction. The tsunami modelling of a shallow top depth of an ENE-WSW fault direction is a better fit in predicting the tide gauge waveform. We also present evidence that the 2019 Banten intra- slab earthquake generated very few aftershocks for a magnitude 7-class earthquake. The stress transfer of a shallow rupture ENE- WSW fault model was able to explain the relocated two weeks of aftershocks. Keywords: Coseismic slip, 2019 Banten intraslab earthquake, GNSS, Tsunami, Aftershocks, Coulomb stress change Introduction earthquake suggest two possible fault source directions. On 2 August 2019, 12:03 UTC at a ~ 50  km distance off The first possible fault was an ENE-WSW fault direction the coast of Banten, Indonesia, a damaging Mw 7.0 earth- with a strike of 69° and dip to the south. The other pos - quake occurred as a result of tectonic activity between sible fault was dipping to the west of an NEN-SWS fault the Australian Plate and the Sunda Block in this particu- direction with a strike of 201°. The subsurface seismic pro - lar region (Fig.  1; Bock et  al. 2003; DeMets et  al. 2010). file obtained by seismic data did not clearly show the fault Following the earthquake, four people died and more that could have ruptured during the event in this particu- than 200 houses were damaged or destroyed, as reported lar region (Susilohadi et al. 2009). Thus, the fault respon - by the Indonesian National Board for Disaster Manage- sible for the 2019 Banten earthquake is not well-known. ment (BNPB). Shaking was felt up to southern Sumatra In order to address this issue, this study investigated and western Java. four fault models responsible for the 2019 Banten The United States Geological Survey (USGS) reported earthquake using two possible fault strikes, with each that the 2019 Banten earthquake epicentre was rela- of the fault strikes investigated for shallow top depth tively deep at 49  km depth. The focal mechanisms of the and deeper top depth. The distribution of the subsur - face coseismic slip during the 2019 Banten earthquake was inferred using Global Navigation Satellite Sys- *Correspondence: endra.gunawan@itb.ac.id tem (GNSS) data available from the Indonesian Con- Global Geophysics Research Group, Faculty of Mining and Petroleum tinuously Operating Reference Stations (Ina-CORS) Engineering, Bandung Institute of Technology, Bandung, Indonesia Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 2 of 12 Fig. 1 Tectonic setting of this study. The red star indicates the location of the 2019 Banten earthquake, with focal mechanism from USGS. The light red colour triangles show the locations of GNSS stations, while the yellow squares indicate the location of the tide gauge. Dashed lines denote depth of slab. Brown lines remarks active faults obtained from Gunawan (2021). Bathymetry and topography are taken from SRTM15 + ( Tozer et al. 2019). The inset shows the larger regional setting network maintained by the Indonesian Geospatial Materials and methods Information Agency (BIG). We also modelled the tsu- In this study, we utilise GNSS data, tidal observa- nami waveform using the estimated coseismic slip of tion data, and aftershocks data to investigate the fault four models and compared it with the tide gauge data responsible for the 2019 Banten earthquake. The infor - available along the coast of southern Sumatra and west- mation of this data and the methodology used in this ern Java (Fig.  1). Finally, we also investigated the Cou- study are described below. lomb stress transfer and compared it with the relocated aftershocks from the Indonesian Agency for Meteorol- GNSS ogy, Climatology and Geophysics (BMKG) network. The GNSS data used by this study was obtained from the Indonesia Continuously Operating Reference Station Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 3 of 12 Fig. 2 Coseismic slip inversion results for a Model 1A; b Model 1B. The black vectors indicate the GNSS data used in the inversion, while the red vectors show the displacement models. The solid black line delineates the top of the fault (Ina-CORS) (Gunawan et  al. 2019; Gunawan and Widi- (Bertiger et al. 2020) and GAMIT software (Herring et al. yantoro 2019), which was available during the 2019 2010), described below. Banten earthquake event. The GNSS antennae of the Ina- During daily solution estimation using GipsyX, we CORS station are located embedded on top of a concrete conducted static solutions in the precise point position- structure. The GNSS data, which is recorded with a 30-s ing mode. We employed fiducial-free with five iterations sampling interval, was processed using GipsyX software and JPL’s reanalysis final set of the International GNSS Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 4 of 12 Fig. 3 Coseismic slip inversion results for a Model 2A; b Model 2B. (See Fig. 2 for a detailed description of the figure legend) Service 2014 (IGS14) orbit and clock product. Ocean observations; the orbit and earth-orientation parameters loading parameters were obtained from the Onsala Space were fixed. Second, these positions and their covariance Observatory (http:// holt. oso. chalm ers. se/ loadi ng/) using with global GNSS solutions, computed as part of Mas- the GOT4.8 model. In addition, we also set an elevation sachusetts Institute of Technology’s processing for the angle cut-off with 15°. International GNSS Service (IGS), were combined. Third, Meanwhile, our daily solution estimation used GAMIT the clarified position time series were estimated. In both incorporating processing steps as used by Gunawan et al. the second and third steps, the loosely constrained solu- (2021). First, the daily position was estimated with atmos- tion was mapped onto a well-constrained reference frame pherically used, loose-constraint, prior GNSS phase by minimising the position and velocity differences of Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 5 of 12 Fig. 4 Tide gauge data record and waveform model of fault Model 1A at each tide gauge station. The data after filtering using a de-tiding and moving average process are shown by the black line. The waveform model of fault Model 1A is shown by the red line. The tide gauge stations are: a KRUI, b BKNT, c KTAG, d SBSI, e BNTN, f SERA, g BINU, and h PRTU selected stations with respect to a priori values defined processed GNSS data. In our search for the fault model by the IGS14 realisation of the International Terrestrial of the 2019 Banten earthquake, we modelled using two Reference Frame (ITRF) 2014 reference frame. possible fault strikes based on the earthquake focal Using the processed daily solutions GNSS data, we mechanism as reported by the USGS. The first fault strike extracted the coseismic displacements by subtracting the model, named Model 1, dips to the south of an ENE- velocity data for five days after the earthquake to five days WSW fault direction with a strike of 69°. The second fault before the earthquake. For each coseismic displacement strike model, named Model 2, dips to the west of a NEN- analysis process obtained from GipsyX and GAMIT, we SWS fault direction with a strike of 201°. The dip angle took the average value and used it as final coseismic dis - for Model 1 is 54°, while the dip angle for Model 2 is 49°. placements of the 2019 Banten earthquake. In both models, the length of the fault is 40  km, which is estimated using an earthquake scaling relationship for dip-slip faulting system (Gunawan 2021). We also divided Coseismic slip inversion the main fault into sub-faults with a length and width of The coseismic slip inversion was calculated using 5 km. the final coseismic displacements obtained from the Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 6 of 12 Fig. 5 The sea surface height maximum during simulation running Fig. 6 The sea surface height maximum during simulation running for 2 h for a fault Model 1A for 2 h for a fault Model 2A In our inversion, we utilised sdm2013 (Wang et  al. Tsunami 2011; 2013) to estimate the coseismic slip distribution. In this study, we modelled the tsunami waveform using This process follows the objective function as follows: the estimated coseismic slip of those four models (Mod- 2 2 F(m) =�Gm − d� + α �Hτ� . In this function, G is els 1A, 1B, 2A and 2B) and compared it with the tide the Green’s functions obtained from an elastic half-space gauge data available along the coast of southern Sumatra model (Okada 1992), m is a coseismic slip of each sub- and western Java. The tidal observation data was obtained fault, d is coseismic displacements, α is the smoothing from the national tide gauge stations network operated factor, which controlled by the model roughness and by BIG. Eight tide gauge stations located off the coasts of data misfit, H is the finite difference approximation of western Java and southern Sumatra were used to under- the Laplacian operator, and τ is the shear stress drop. stand the possible tsunami waveform as recorded by the For every fault model, we investigated using a shallow tide gauge. The location of these tide gauge stations is top fault depth of 1 km, hereinafter referred to as Model shown in Fig. 1. 1A and Model 2A, and a deep top fault depth of 25  km, To extract the probable tsunami waveform recorded by hereinafter referred to as Model 1B and Model 2B. All of the tide gauge, first, we conducted a de-tiding process to the four models were constructed with a bottom depth of separate the data from its tidal components. In this pro- 50 km. cess, we used a bandpass filter FFT (Fast Fourier Trans - formation) with a period of 3 to 30 min (Rabinovich 1997; Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 7 of 12 450 m, and the simulation time for this model was 7200 s or 2 h. Aftershocks Aftershocks obtained from the Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) net- work were used to understand the possibility that the mainshock raised stress in the surrounding region and triggered these aftershocks. We relocated aftershocks using a Double-Difference (DD) method (Waldhauser and Ellsworth 2000). In this method, when the hypo- centre distribution distance between two earthquakes is very small compared to the distance between the source station, then the ray-path and waveform of the two earthquakes can be considered to be approximately the same. With this assumption, the difference in travel time between the two earthquakes recorded at the same sta- tion can be considered only as a function of the distance between the two hypocentres. Pesicek et  al. (2010) developed the DD method for teleseismic cases by adapting the P wave beam track- ing method for the case of spherical earth (Koketsu and Sekine 1998). We used the DD method for the teleseis- mic distance, named teletomoDD, which uses a nested regional-global 3D velocity model (Widiyantoro and van der Hilst 1997). For regional models, the 3D velocity model is used and for the global model, the AK135 veloc- ity model is used (Kennett et al. 1995). Results and discussion Coseismic slip Our estimation of the final coseismic displacements of the 2019 Banten earthquake suggests that the GNSS sta- Fig. 7 The sea surface height maximum during simulation running tions located closest to the epicentre, CUJK, experience for 2 h for a fault Model 2B coseismic displacements of ~ 5  mm. Meanwhile, the far- thest GNSS stations, BAKO, experience ~ 3  mm. The estimated coseismic displacements of the 2019 Banten Heidarzadeh and Satake 2013). Then, although the tide earthquake at GNSS stations in western Java are shown component was removed, the recorded data may suffer in Figs. 2 and 3. interference from noise. To eliminate this type of noise, Following our coseismic slip inversion, Model 1A and the tide gauge data was then filtered using the moving Model 1B suggest that the high slip is located in the shal- averages process. In this study, a moving average was car- lower part of the design fault geometry, where ~ 2.0  m ried out for 15 pieces of data, or for tide gauge data with a slip of Model 1A is located at 11  km depth and ~ 2.4  m 1-min sampling rate of 15 min. slip of Model 1B is located at 30  km depth (Fig.  2). We The tsunami modelling was performed with the use of calculated a misfit between GNSS data displacement and TSUNAMI-N3. The model data and setup were as fol - model displacement using mean absolute error as fol- lows. First, the numerical domain for this study was in lows: Our investiga- MAE = 1/n (data − model ). the boundary of geographic coordinates in longitude i i i=1 tion for the Model 1A indicated that the misfit is 2 mm, between 103°E and 107°E, and latitude between 5°S while in the Model 1B it is 3 mm. Meanwhile, high coseis- and 8°S. The geometric data used GEBCO Compilation mic slip of Model 2A is ~ 1.5  m at 24  km depth, while Group (2020) combined with a navy chart provided by Model 2B is ~ 2.0 at 30  km depth (Fig.  3). Misfit from the BNPB. The grid data used in this study was set to be Model 2A and Model 2B are ~ 4 mm, which is higher than Model 1A and Model 1B. Using 30 GPa as rigidity, Model Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 8 of 12 Fig. 8 a Epicenter shifts of the DD locations relative to the BMKG catalog; b Rose diagram showing the dominant direction of relocation shifts 1A and Model 1B yield geodetic moment of Tsunami modelling 19 19 4.22 × 10   N  m and 3.95 × 10   N  m, which are both Figure  4 shows the tide gauge record after being filtered equivalent with Mw 7.0. Meanwhile, Model 2A and using the de-tiding and moving average process. Tsunami Model 2B yield a geodetic moment of 1.90 × 10   N  m waveforms at the tide gauges are simulated using each of and of 2.00 × 10   N  m, which are both equivalent with the estimated coseismic slip models. The simulated tsu - Mw 6.80. nami waveforms are then compared with the observed Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 9 of 12 Fig. 9 Histograms of relative residuals of event pairs. a Before relocation; b After relocation using the DD method Coulomb stress change one to find the best earthquake source model. The ver - Using the BMKG network, we recorded only one tical surface displacement for the tsunami simulation is earthquake event with magnitude 4.2, which occurred calculated from the estimated coseismic slip model using on 3 August 2019 at 22 km of depth, and three magni- the equations in Mansinha and Smylie (1971). The Model tude 3-class earthquakes, which occurred on 6 August 1A generated highest sea surface height (SSH) maximum 2019. Between 6 and 15 August 2019, no aftershock compared to the other three models (Fig.  5). Our results was recorded. Then, another aftershock with Mw 3.9 also suggest that the Model 1B did not generate a tsu- occurred on 16 August 2019. Figures  8 and 9 shows nami, while the sea surface heights of Model 2A (Fig.  6) epicenter shifts of the DD locations relative to the and Model 2B (Fig. 7) were very small and did not reach BMKG catalog and the histograms of relative residu- the tide gauge stations. Our tsunami modelling of these als of event pairs. Thus, during the two weeks after the four fault models indicates that a significant tsunami mainshock, only five aftershocks of the 2019 Banten occurs when the fault is ruptured at a shallow depth of earthquake were recorded. This is fewer than after the the fault. 2006 Mw 7.8 Java tsunami earthquake which occurred Comparing the tsunami waveform from each fault in the shallow interplate or forearc region (Bilek and model and the tide gauge data, we found that tsunami Engdahl 2007). waveform of fault Model 1A is comparable and better fits the tide gauge data than the other model, although it is Using the relocated aftershocks, we investigated important to note that the waveform amplitude is very the possibility that the mainshock raised stress in small, only ~ 2 cm. With such a small amplitude, a notice the surrounding region and triggered aftershocks. We calculated the Coulomb stress change as follows: able comparison of the tsunami ~ 1 h after the mainshock �CFF = �τ + µ′�σ , where �τ is the shear stress change was detected at SBSI and BNTN. At some tide gauge on a given fault plane (positive in the direction of receiver stations however, such as SERA and BINU, early 30 min fault slip), �σ is the fault normal stress change (positive tide gauge data still contained noise which could not be for fault unclamping) and μ′ is the effective fault friction removed. This most likely happened because these sta - coefficient on the receiver fault (Toda et  al. 1998, 2011; tions are located at a pier, harbour, or bay, with a rela- Gunawan et  al. 2018). Positive values of ΔCFF indicate tively shallow depth of < 15 m, thus data is influenced by the beach morphology. Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 10 of 12 Fig. 10 Relocated aftershocks distribution from 3—16 August 2019 and Coulomb stress change analysis correspond with fault a, b Model 1A, c, d Model 1B. a, c Distribution of the relocated aftershocks are shown by yellow circles; b, d The vertical view of calculated Coulomb stress change along profile AA’ that the stress in a region is acting to promote slip, nega- fault structure responsible for the 2019 Banten intraslab tive values suggest opposition to slip. earthquake. Using µ′= 0.8 , we found that the correlation between Our findings emphasise the need for further positive values of ΔCFF with aftershocks location fit improvement of the geophysical data collection well for fault Model 1A (Fig.  10b). For the other mod- through marine geophysical survey, additional con- els, aftershocks were located in the negative values of tinuous GNSS and seismic stations in Java, especially ΔCFF (Figs.  10d, 11b, d). The ΔCFF investigation sup - towards a better understanding of any future potential ports the preferences fault model from the minimum earthquakes that may occur in Java. Widiyantoro et al. misfit of GNSS data displacement and model displace - (2020) proposed a locking megathrust in southern ment obtained by the fault Model 1A and the tsunami Java with an estimated magnitude of Mw 9.1 if all seg- modelling. ments in Java are ruptured. Utilising comprehensive Our investigation suggests that the 2019 Banten earth- data would be very useful to identify the earthquake quake occurred on an ENE-WSW fault direction, where source in Java, especially in performing disaster miti- the fault ruptured at a shallow depth. The GNSS data gation to reduce risk in this most populated island in inversion, stress transfer analysis and tsunami model- Indonesia. ling support this hypothesis. The geophysical survey conducted in this particular region, however, did not Conclusion identify an ENE-WSW structure, which was responsible We investigated the 2 August 2019 Mw 7.0 Ban- for the 2019 Banten earthquake (Susilohadi et  al. 2009). ten intraslab earthquake using GNSS data available This happened because the geophysical survey line (SO in western Java. We estimated the coseismic slip of 137-27 in Fig. 2 of Susilohadi et al. 2009) is parallel to the fault models with the direction of ENE-WSW and Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 11 of 12 Fig. 11 Relocated aftershocks distribution from 3—16 August 2019 and Coulomb stress change analysis correspond with fault a, b Model 2A, c, d Model 2B. (See Fig. 10 for a detailed description of the figure legend) NEN-SWS. In every fault direction, two fault mod- the most populated island in Indonesia and prone to els were investigated with shallow top depth and future potential megathrust earthquakes. deeper top depth. We show that the minimum mis- fit between GNSS data displacement and model dis- Abbreviations placement was obtained from our modelling using GNSS: Global Navigation Satellite System; ENE-WSW: East North East-West an ENE-WSW fault direction. The fault models with South West; BNPB: Indonesian National Board for Disaster Management; USGS: United States Geological Survey; NEN-SWS: North East North-South West the direction of NEN-SWS poorly predict displace- South; Ina-CORS: Indonesian Continuously Operating Reference Stations; BIG: ment nearest to the epicentre. Using the coseismic Indonesian Geospatial Information Agency; BMKG: Indonesian Agency for slip estimation, we conducted tsunami modelling on Meteorology, Climatology and Geophysics; IGS: International GNSS Service; ITRF: International Terrestrial Reference Frame; FFT: Fast Fourier Transformation; each of the model and compared the result with the DD: Double-Difference; MAE: Mean Absolute Error. tide gauge data. We showed that tsunami modelling of a shallow top depth of an ENE-WSW fault direc- Acknowledgements The authors would like to thank the Editor, Christopher Gomez, and two tion is a better fit in predicting the tide gauge wave- anonymous reviewers for the thoughtful comments, which help improve form. Finally, we also found that the stress transfer of the quality of this manuscript. We also thank BMKG and BIG for providing the a shallow rupture ENE-WSW fault model was able to earthquake data and GNSS data used in this study. Figures were generated using Generic Mapping Tools (GMT ) software ( Wessel et al. 2019). explain the relocated two weeks of aftershocks. Our investigation suggests that the 2019 Mw 7.0 Banten, Author contributions Indonesia, intraslab earthquake ruptured on a shallow EG: conceptualization, investigation, writing, methodology, discussion, review and editing, WK and ARG: tsunami analysis, writing, methodology and discus- portion of the fault with an ENE-WSW direction. Per- sion, MK: GNSS processing, BTW: tide gauge processing, SW and PS: seismic forming disaster mitigation through identification of analysis, writing, methodology and editing, NRH, IMA and CP: conceptualiza- earthquake source is very crucial, considering Java is tion and discussion. All authors read and approved the final manuscript. Gunawan et al. 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The 2019 Mw 7.0 Banten, Indonesia, intraslab earthquake: investigation of the coseismic slip, tsunami modelling and Coulomb stress change

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Abstract

The 2019 Mw 7.0 Banten, Indonesia, earthquake occurred at a 49 km depth in a relatively unknown region, where the geological structure did not clearly show the fault. In this study, we use the Global Navigation Satellite System data to analyse the fault source of the earthquake. Following the earthquake’s focal mechanism, we modelled a total of four fault models using two possible fault strikes, with each of the fault strikes investigated for shallow top depth and deeper top depth. This study also utilises the tide gauge data to confirm the tsunami waveform, modelled using the estimated coseismic slip. We present evidence of the shallow rupture of the 2019 Mw 7.0 Banten, Indonesia, intraslab earthquake from an ENE-WSW fault direction. The tsunami modelling of a shallow top depth of an ENE-WSW fault direction is a better fit in predicting the tide gauge waveform. We also present evidence that the 2019 Banten intra- slab earthquake generated very few aftershocks for a magnitude 7-class earthquake. The stress transfer of a shallow rupture ENE- WSW fault model was able to explain the relocated two weeks of aftershocks. Keywords: Coseismic slip, 2019 Banten intraslab earthquake, GNSS, Tsunami, Aftershocks, Coulomb stress change Introduction earthquake suggest two possible fault source directions. On 2 August 2019, 12:03 UTC at a ~ 50  km distance off The first possible fault was an ENE-WSW fault direction the coast of Banten, Indonesia, a damaging Mw 7.0 earth- with a strike of 69° and dip to the south. The other pos - quake occurred as a result of tectonic activity between sible fault was dipping to the west of an NEN-SWS fault the Australian Plate and the Sunda Block in this particu- direction with a strike of 201°. The subsurface seismic pro - lar region (Fig.  1; Bock et  al. 2003; DeMets et  al. 2010). file obtained by seismic data did not clearly show the fault Following the earthquake, four people died and more that could have ruptured during the event in this particu- than 200 houses were damaged or destroyed, as reported lar region (Susilohadi et al. 2009). Thus, the fault respon - by the Indonesian National Board for Disaster Manage- sible for the 2019 Banten earthquake is not well-known. ment (BNPB). Shaking was felt up to southern Sumatra In order to address this issue, this study investigated and western Java. four fault models responsible for the 2019 Banten The United States Geological Survey (USGS) reported earthquake using two possible fault strikes, with each that the 2019 Banten earthquake epicentre was rela- of the fault strikes investigated for shallow top depth tively deep at 49  km depth. The focal mechanisms of the and deeper top depth. The distribution of the subsur - face coseismic slip during the 2019 Banten earthquake was inferred using Global Navigation Satellite Sys- *Correspondence: endra.gunawan@itb.ac.id tem (GNSS) data available from the Indonesian Con- Global Geophysics Research Group, Faculty of Mining and Petroleum tinuously Operating Reference Stations (Ina-CORS) Engineering, Bandung Institute of Technology, Bandung, Indonesia Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 2 of 12 Fig. 1 Tectonic setting of this study. The red star indicates the location of the 2019 Banten earthquake, with focal mechanism from USGS. The light red colour triangles show the locations of GNSS stations, while the yellow squares indicate the location of the tide gauge. Dashed lines denote depth of slab. Brown lines remarks active faults obtained from Gunawan (2021). Bathymetry and topography are taken from SRTM15 + ( Tozer et al. 2019). The inset shows the larger regional setting network maintained by the Indonesian Geospatial Materials and methods Information Agency (BIG). We also modelled the tsu- In this study, we utilise GNSS data, tidal observa- nami waveform using the estimated coseismic slip of tion data, and aftershocks data to investigate the fault four models and compared it with the tide gauge data responsible for the 2019 Banten earthquake. The infor - available along the coast of southern Sumatra and west- mation of this data and the methodology used in this ern Java (Fig.  1). Finally, we also investigated the Cou- study are described below. lomb stress transfer and compared it with the relocated aftershocks from the Indonesian Agency for Meteorol- GNSS ogy, Climatology and Geophysics (BMKG) network. The GNSS data used by this study was obtained from the Indonesia Continuously Operating Reference Station Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 3 of 12 Fig. 2 Coseismic slip inversion results for a Model 1A; b Model 1B. The black vectors indicate the GNSS data used in the inversion, while the red vectors show the displacement models. The solid black line delineates the top of the fault (Ina-CORS) (Gunawan et  al. 2019; Gunawan and Widi- (Bertiger et al. 2020) and GAMIT software (Herring et al. yantoro 2019), which was available during the 2019 2010), described below. Banten earthquake event. The GNSS antennae of the Ina- During daily solution estimation using GipsyX, we CORS station are located embedded on top of a concrete conducted static solutions in the precise point position- structure. The GNSS data, which is recorded with a 30-s ing mode. We employed fiducial-free with five iterations sampling interval, was processed using GipsyX software and JPL’s reanalysis final set of the International GNSS Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 4 of 12 Fig. 3 Coseismic slip inversion results for a Model 2A; b Model 2B. (See Fig. 2 for a detailed description of the figure legend) Service 2014 (IGS14) orbit and clock product. Ocean observations; the orbit and earth-orientation parameters loading parameters were obtained from the Onsala Space were fixed. Second, these positions and their covariance Observatory (http:// holt. oso. chalm ers. se/ loadi ng/) using with global GNSS solutions, computed as part of Mas- the GOT4.8 model. In addition, we also set an elevation sachusetts Institute of Technology’s processing for the angle cut-off with 15°. International GNSS Service (IGS), were combined. Third, Meanwhile, our daily solution estimation used GAMIT the clarified position time series were estimated. In both incorporating processing steps as used by Gunawan et al. the second and third steps, the loosely constrained solu- (2021). First, the daily position was estimated with atmos- tion was mapped onto a well-constrained reference frame pherically used, loose-constraint, prior GNSS phase by minimising the position and velocity differences of Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 5 of 12 Fig. 4 Tide gauge data record and waveform model of fault Model 1A at each tide gauge station. The data after filtering using a de-tiding and moving average process are shown by the black line. The waveform model of fault Model 1A is shown by the red line. The tide gauge stations are: a KRUI, b BKNT, c KTAG, d SBSI, e BNTN, f SERA, g BINU, and h PRTU selected stations with respect to a priori values defined processed GNSS data. In our search for the fault model by the IGS14 realisation of the International Terrestrial of the 2019 Banten earthquake, we modelled using two Reference Frame (ITRF) 2014 reference frame. possible fault strikes based on the earthquake focal Using the processed daily solutions GNSS data, we mechanism as reported by the USGS. The first fault strike extracted the coseismic displacements by subtracting the model, named Model 1, dips to the south of an ENE- velocity data for five days after the earthquake to five days WSW fault direction with a strike of 69°. The second fault before the earthquake. For each coseismic displacement strike model, named Model 2, dips to the west of a NEN- analysis process obtained from GipsyX and GAMIT, we SWS fault direction with a strike of 201°. The dip angle took the average value and used it as final coseismic dis - for Model 1 is 54°, while the dip angle for Model 2 is 49°. placements of the 2019 Banten earthquake. In both models, the length of the fault is 40  km, which is estimated using an earthquake scaling relationship for dip-slip faulting system (Gunawan 2021). We also divided Coseismic slip inversion the main fault into sub-faults with a length and width of The coseismic slip inversion was calculated using 5 km. the final coseismic displacements obtained from the Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 6 of 12 Fig. 5 The sea surface height maximum during simulation running Fig. 6 The sea surface height maximum during simulation running for 2 h for a fault Model 1A for 2 h for a fault Model 2A In our inversion, we utilised sdm2013 (Wang et  al. Tsunami 2011; 2013) to estimate the coseismic slip distribution. In this study, we modelled the tsunami waveform using This process follows the objective function as follows: the estimated coseismic slip of those four models (Mod- 2 2 F(m) =�Gm − d� + α �Hτ� . In this function, G is els 1A, 1B, 2A and 2B) and compared it with the tide the Green’s functions obtained from an elastic half-space gauge data available along the coast of southern Sumatra model (Okada 1992), m is a coseismic slip of each sub- and western Java. The tidal observation data was obtained fault, d is coseismic displacements, α is the smoothing from the national tide gauge stations network operated factor, which controlled by the model roughness and by BIG. Eight tide gauge stations located off the coasts of data misfit, H is the finite difference approximation of western Java and southern Sumatra were used to under- the Laplacian operator, and τ is the shear stress drop. stand the possible tsunami waveform as recorded by the For every fault model, we investigated using a shallow tide gauge. The location of these tide gauge stations is top fault depth of 1 km, hereinafter referred to as Model shown in Fig. 1. 1A and Model 2A, and a deep top fault depth of 25  km, To extract the probable tsunami waveform recorded by hereinafter referred to as Model 1B and Model 2B. All of the tide gauge, first, we conducted a de-tiding process to the four models were constructed with a bottom depth of separate the data from its tidal components. In this pro- 50 km. cess, we used a bandpass filter FFT (Fast Fourier Trans - formation) with a period of 3 to 30 min (Rabinovich 1997; Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 7 of 12 450 m, and the simulation time for this model was 7200 s or 2 h. Aftershocks Aftershocks obtained from the Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) net- work were used to understand the possibility that the mainshock raised stress in the surrounding region and triggered these aftershocks. We relocated aftershocks using a Double-Difference (DD) method (Waldhauser and Ellsworth 2000). In this method, when the hypo- centre distribution distance between two earthquakes is very small compared to the distance between the source station, then the ray-path and waveform of the two earthquakes can be considered to be approximately the same. With this assumption, the difference in travel time between the two earthquakes recorded at the same sta- tion can be considered only as a function of the distance between the two hypocentres. Pesicek et  al. (2010) developed the DD method for teleseismic cases by adapting the P wave beam track- ing method for the case of spherical earth (Koketsu and Sekine 1998). We used the DD method for the teleseis- mic distance, named teletomoDD, which uses a nested regional-global 3D velocity model (Widiyantoro and van der Hilst 1997). For regional models, the 3D velocity model is used and for the global model, the AK135 veloc- ity model is used (Kennett et al. 1995). Results and discussion Coseismic slip Our estimation of the final coseismic displacements of the 2019 Banten earthquake suggests that the GNSS sta- Fig. 7 The sea surface height maximum during simulation running tions located closest to the epicentre, CUJK, experience for 2 h for a fault Model 2B coseismic displacements of ~ 5  mm. Meanwhile, the far- thest GNSS stations, BAKO, experience ~ 3  mm. The estimated coseismic displacements of the 2019 Banten Heidarzadeh and Satake 2013). Then, although the tide earthquake at GNSS stations in western Java are shown component was removed, the recorded data may suffer in Figs. 2 and 3. interference from noise. To eliminate this type of noise, Following our coseismic slip inversion, Model 1A and the tide gauge data was then filtered using the moving Model 1B suggest that the high slip is located in the shal- averages process. In this study, a moving average was car- lower part of the design fault geometry, where ~ 2.0  m ried out for 15 pieces of data, or for tide gauge data with a slip of Model 1A is located at 11  km depth and ~ 2.4  m 1-min sampling rate of 15 min. slip of Model 1B is located at 30  km depth (Fig.  2). We The tsunami modelling was performed with the use of calculated a misfit between GNSS data displacement and TSUNAMI-N3. The model data and setup were as fol - model displacement using mean absolute error as fol- lows. First, the numerical domain for this study was in lows: Our investiga- MAE = 1/n (data − model ). the boundary of geographic coordinates in longitude i i i=1 tion for the Model 1A indicated that the misfit is 2 mm, between 103°E and 107°E, and latitude between 5°S while in the Model 1B it is 3 mm. Meanwhile, high coseis- and 8°S. The geometric data used GEBCO Compilation mic slip of Model 2A is ~ 1.5  m at 24  km depth, while Group (2020) combined with a navy chart provided by Model 2B is ~ 2.0 at 30  km depth (Fig.  3). Misfit from the BNPB. The grid data used in this study was set to be Model 2A and Model 2B are ~ 4 mm, which is higher than Model 1A and Model 1B. Using 30 GPa as rigidity, Model Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 8 of 12 Fig. 8 a Epicenter shifts of the DD locations relative to the BMKG catalog; b Rose diagram showing the dominant direction of relocation shifts 1A and Model 1B yield geodetic moment of Tsunami modelling 19 19 4.22 × 10   N  m and 3.95 × 10   N  m, which are both Figure  4 shows the tide gauge record after being filtered equivalent with Mw 7.0. Meanwhile, Model 2A and using the de-tiding and moving average process. Tsunami Model 2B yield a geodetic moment of 1.90 × 10   N  m waveforms at the tide gauges are simulated using each of and of 2.00 × 10   N  m, which are both equivalent with the estimated coseismic slip models. The simulated tsu - Mw 6.80. nami waveforms are then compared with the observed Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 9 of 12 Fig. 9 Histograms of relative residuals of event pairs. a Before relocation; b After relocation using the DD method Coulomb stress change one to find the best earthquake source model. The ver - Using the BMKG network, we recorded only one tical surface displacement for the tsunami simulation is earthquake event with magnitude 4.2, which occurred calculated from the estimated coseismic slip model using on 3 August 2019 at 22 km of depth, and three magni- the equations in Mansinha and Smylie (1971). The Model tude 3-class earthquakes, which occurred on 6 August 1A generated highest sea surface height (SSH) maximum 2019. Between 6 and 15 August 2019, no aftershock compared to the other three models (Fig.  5). Our results was recorded. Then, another aftershock with Mw 3.9 also suggest that the Model 1B did not generate a tsu- occurred on 16 August 2019. Figures  8 and 9 shows nami, while the sea surface heights of Model 2A (Fig.  6) epicenter shifts of the DD locations relative to the and Model 2B (Fig. 7) were very small and did not reach BMKG catalog and the histograms of relative residu- the tide gauge stations. Our tsunami modelling of these als of event pairs. Thus, during the two weeks after the four fault models indicates that a significant tsunami mainshock, only five aftershocks of the 2019 Banten occurs when the fault is ruptured at a shallow depth of earthquake were recorded. This is fewer than after the the fault. 2006 Mw 7.8 Java tsunami earthquake which occurred Comparing the tsunami waveform from each fault in the shallow interplate or forearc region (Bilek and model and the tide gauge data, we found that tsunami Engdahl 2007). waveform of fault Model 1A is comparable and better fits the tide gauge data than the other model, although it is Using the relocated aftershocks, we investigated important to note that the waveform amplitude is very the possibility that the mainshock raised stress in small, only ~ 2 cm. With such a small amplitude, a notice the surrounding region and triggered aftershocks. We calculated the Coulomb stress change as follows: able comparison of the tsunami ~ 1 h after the mainshock �CFF = �τ + µ′�σ , where �τ is the shear stress change was detected at SBSI and BNTN. At some tide gauge on a given fault plane (positive in the direction of receiver stations however, such as SERA and BINU, early 30 min fault slip), �σ is the fault normal stress change (positive tide gauge data still contained noise which could not be for fault unclamping) and μ′ is the effective fault friction removed. This most likely happened because these sta - coefficient on the receiver fault (Toda et  al. 1998, 2011; tions are located at a pier, harbour, or bay, with a rela- Gunawan et  al. 2018). Positive values of ΔCFF indicate tively shallow depth of < 15 m, thus data is influenced by the beach morphology. Gunawan et al. Geoenvironmental Disasters (2022) 9:14 Page 10 of 12 Fig. 10 Relocated aftershocks distribution from 3—16 August 2019 and Coulomb stress change analysis correspond with fault a, b Model 1A, c, d Model 1B. a, c Distribution of the relocated aftershocks are shown by yellow circles; b, d The vertical view of calculated Coulomb stress change along profile AA’ that the stress in a region is acting to promote slip, nega- fault structure responsible for the 2019 Banten intraslab tive values suggest opposition to slip. earthquake. Using µ′= 0.8 , we found that the correlation between Our findings emphasise the need for further positive values of ΔCFF with aftershocks location fit improvement of the geophysical data collection well for fault Model 1A (Fig.  10b). For the other mod- through marine geophysical survey, additional con- els, aftershocks were located in the negative values of tinuous GNSS and seismic stations in Java, especially ΔCFF (Figs.  10d, 11b, d). The ΔCFF investigation sup - towards a better understanding of any future potential ports the preferences fault model from the minimum earthquakes that may occur in Java. Widiyantoro et al. misfit of GNSS data displacement and model displace - (2020) proposed a locking megathrust in southern ment obtained by the fault Model 1A and the tsunami Java with an estimated magnitude of Mw 9.1 if all seg- modelling. ments in Java are ruptured. Utilising comprehensive Our investigation suggests that the 2019 Banten earth- data would be very useful to identify the earthquake quake occurred on an ENE-WSW fault direction, where source in Java, especially in performing disaster miti- the fault ruptured at a shallow depth. The GNSS data gation to reduce risk in this most populated island in inversion, stress transfer analysis and tsunami model- Indonesia. ling support this hypothesis. The geophysical survey conducted in this particular region, however, did not Conclusion identify an ENE-WSW structure, which was responsible We investigated the 2 August 2019 Mw 7.0 Ban- for the 2019 Banten earthquake (Susilohadi et  al. 2009). ten intraslab earthquake using GNSS data available This happened because the geophysical survey line (SO in western Java. We estimated the coseismic slip of 137-27 in Fig. 2 of Susilohadi et al. 2009) is parallel to the fault models with the direction of ENE-WSW and Guna wan et al. Geoenvironmental Disasters (2022) 9:14 Page 11 of 12 Fig. 11 Relocated aftershocks distribution from 3—16 August 2019 and Coulomb stress change analysis correspond with fault a, b Model 2A, c, d Model 2B. (See Fig. 10 for a detailed description of the figure legend) NEN-SWS. In every fault direction, two fault mod- the most populated island in Indonesia and prone to els were investigated with shallow top depth and future potential megathrust earthquakes. deeper top depth. We show that the minimum mis- fit between GNSS data displacement and model dis- Abbreviations placement was obtained from our modelling using GNSS: Global Navigation Satellite System; ENE-WSW: East North East-West an ENE-WSW fault direction. The fault models with South West; BNPB: Indonesian National Board for Disaster Management; USGS: United States Geological Survey; NEN-SWS: North East North-South West the direction of NEN-SWS poorly predict displace- South; Ina-CORS: Indonesian Continuously Operating Reference Stations; BIG: ment nearest to the epicentre. Using the coseismic Indonesian Geospatial Information Agency; BMKG: Indonesian Agency for slip estimation, we conducted tsunami modelling on Meteorology, Climatology and Geophysics; IGS: International GNSS Service; ITRF: International Terrestrial Reference Frame; FFT: Fast Fourier Transformation; each of the model and compared the result with the DD: Double-Difference; MAE: Mean Absolute Error. tide gauge data. We showed that tsunami modelling of a shallow top depth of an ENE-WSW fault direc- Acknowledgements The authors would like to thank the Editor, Christopher Gomez, and two tion is a better fit in predicting the tide gauge wave- anonymous reviewers for the thoughtful comments, which help improve form. Finally, we also found that the stress transfer of the quality of this manuscript. We also thank BMKG and BIG for providing the a shallow rupture ENE-WSW fault model was able to earthquake data and GNSS data used in this study. Figures were generated using Generic Mapping Tools (GMT ) software ( Wessel et al. 2019). explain the relocated two weeks of aftershocks. Our investigation suggests that the 2019 Mw 7.0 Banten, Author contributions Indonesia, intraslab earthquake ruptured on a shallow EG: conceptualization, investigation, writing, methodology, discussion, review and editing, WK and ARG: tsunami analysis, writing, methodology and discus- portion of the fault with an ENE-WSW direction. Per- sion, MK: GNSS processing, BTW: tide gauge processing, SW and PS: seismic forming disaster mitigation through identification of analysis, writing, methodology and editing, NRH, IMA and CP: conceptualiza- earthquake source is very crucial, considering Java is tion and discussion. All authors read and approved the final manuscript. Gunawan et al. 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Journal

Geoenvironmental Disasters – Springer Journals

Published: Jun 10, 2022

Keywords: Coseismic slip; 2019 Banten intraslab earthquake; GNSS; Tsunami; Aftershocks; Coulomb stress change

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