The Sumatra subduction zone is a highly active seismic region prone to large and destructive earthquakes. Understanding the earthquake potential along this zone is crucial for preparedness and mitigation efforts. The key factors contributing to high earthquake potential can be described as follows:

The subduction Zone Activity, in which the Indian-Australian Plate is subducted beneath the Sunda Plate along the Sumatra coast, causes significant stress build-up. This tectonic activity releases energy in the form of earthquakes. Sumatra has a history of major earthquakes, including the devastating 2004 Indian Ocean earthquake and tsunami, highlighting the region’s vulnerability. The subduction zone can generate megathrust earthquakes, which can have magnitudes exceeding 8.0 and cause widespread destruction and tsunamis. While it is impossible to predict future earthquakes’ exact timing and location, scientists and seismologists continuously monitor the region for signs of increased seismic activity. Some areas along the Sumatra subduction zone are considered at higher risk due to their specific tectonic characteristics and historical earthquake patterns. Thus, by understanding the earthquake potential along the Sumatra subduction zone and taking proactive measures, it is possible to mitigate the impact of future earthquakes and safeguard lives and property. Shearer and Burgmann’s (2010) analysis of the 2004 Sumatra-Andaman earthquake provides crucial insights into the complexities of large-scale seismic events. While the earthquake exhibited many characteristics of other major earthquakes, it also revealed significant uncertainties and long-term consequences. Following the suggestion of Shearer and Burgmann (2010), the implications and future research directions to reduce the uncertainties in estimating the potential of slip distribution for seismic hazard assessment could be done by combining seismic and geodetic data, which can provide a more comprehensive understanding of earthquake rupture processes and improve seismic hazard assessments and advanced seismic imaging techniques to constrain slip distribution better and assess the potential for future large earthquakes. Numerous regional hazard models have been developed to characterize slip along the Sumatran megathrust. Early models, such as the Global Seismic Hazard Assessment Program, highlighted the Sumatra Subduction Zone and the Sumatran Fault as major sources. Subsequent studies expanded assessments and refined hazard maps using updated earthquake catalogs (Petersen et al., 2004; Petersen et al., 2007; Megawati and Pan, 2009); these early studies lacked integrated geologic, seismic, and geodetic data.

Triyoso et al. (2020) addressed a significant gap in seismic hazard assessment along Sumatra’s Padang and Bengkulu coasts by incorporating GPS-derived surface strain rates to estimate seismic moment rates, a crucial factor in determining earthquake magnitude. Their approach combined a smoothed seismicity rate with a model of pre-earthquake surface strain derived primarily from GPS data from several previous studies (Prawirodirdjo et al., 2010; Bradley et al., 2017). For the Andaman and Nicobar Islands, a pre-earthquake crustal motion model was developed using forward modeling methods, incorporating existing seismic data and assuming a plate convergence rate of 14 mm/yr (Triyoso et al., 2020). Finally, they constructed a model to characterize fault parameters in the Sumatran subduction zone, utilizing forward modeling and GPS data from the coastal region as a reference.

Triyoso and Sahara (2021) studied the seismic hazard function mapping using estimated horizontal crustal strain off the west coast of northern Sumatra. Their study aims to understand and assess the seismic hazard potential in the region off the west coast of Northern Sumatra by utilizing estimated horizontal crustal strain data. This approach provides a valuable tool for evaluating the likelihood of future large earthquakes and their potential impacts. In their study, the horizontal crustal strain quantifies the deformation of the Earth’s crust, providing insights into the accumulation of stress along fault lines. The Seismic Hazard Function (SHF) represents the probability of exceeding a specific level of ground motion within a given time frame. Pre-seismic and co-seismic GPS data are crucial for estimating crustal strain and understanding tectonic processes. Historical earthquake data provide valuable information about past seismic activity and recurrence intervals. Various modeling techniques, such as the deterministic and probabilistic approaches, are employed to assess seismic hazard. Their study suggests that by understanding the seismic hazard potential in the region, policymakers, engineers, and communities can take proactive measures to mitigate the risks associated with future earthquakes.

Taroni and Akinci (2021) emphasize the importance of accurate spatial earthquake forecasting for hazard analysis. While historical earthquake records, analyzed through zone division or seismicity smoothing methods (Frankel, 1995; Helmstetter et al., 2007), are commonly used for predicting future earthquake locations, they propose that incorporating foreshocks and aftershocks utilizing the methodology developed by Marzocchi and Taroni (2014) can enhance forecasting accuracy.

Nishimura (2022) emphasizes that geodetic data is valuable because it captures the permanent deformation caused by earthquakes, as explained by Reid’s (1910) elastic rebound theory (Rhoades et al., 2011). Ward (1994); Ward (1998) highlighted the importance of geodetic data in understanding the earthquake potential of poorly understood faults, especially in regions like southern California. The success of earthquake forecasting models utilizing GNSS data at global and regional scales further underscores this point (e.g., Bird and Kreemer, 2015). Building upon the need to reassess earthquake risk and enhance early warning systems, Regional Earthquake Likelihood Models (RELMs) play a crucial role in understanding and mitigating earthquake hazards (Nishimura, 2022). While many RELMs traditionally rely on active fault data or historical seismicity records to estimate long-term earthquake rates (e.g., Zechar et al., 2010), incorporating geodetic data, particularly from GNSS networks, can significantly improve their accuracy (Nishimura, 2022). This approach aligns with the need for more robust and comprehensive earthquake hazard assessment strategies.

Anderson and Zaliapin (2023) recently refined the seismicity model framework that Petersen et al. (2020) developed by incorporating spatial and temporal declustering. Their goal was to compare their method’s results with those obtained using the Gardner and Knopoff window method (Gardner and Knopoff, 1974) and a modified version of this method by Anderson et al. (2021) that prioritizes retaining significant events from an engineering perspective. Declustering earthquake catalogs, which involves retaining the most significant earthquakes while excluding smaller events with negligible impact on ground shaking, is a common practice in seismicity analysis. However, Marzocchi and Taroni (2014) caution that using complete seismic catalogs, particularly those derived from instrumental recordings, without declustering can lead to a distorted perception of the future spatial distribution of seismicity rates. Despite the focus on major earthquakes, monitoring minor seismic events, including earthquake swarms that lack an apparent main shock, is crucial for identifying areas with an elevated potential for larger seismic events. Research has demonstrated the value of information on small-scale seismic events and inter-seismic strain accumulation as predictors of major earthquake locations (Strader et al., 2018; Zeng et al., 2018). Triyoso and colleagues have developed a novel approach for estimating the likelihood of earthquakes exceeding a specified magnitude threshold (Triyoso, 2023; Triyoso et al., 2023). This method weights the uniform background seismicity rate, derived from declustered earthquake catalogs, based on the normalized maximum shear strain rate or seismic moment rate inferred from pre-seismic surface strain data using a data model based on GPS. Building upon this, Triyoso (2023) developed a robust model for forecasting seismogenic earthquake potential by integrating methodologies from previous studies and analyzing earthquake catalogs spanning various timeframes.

Further research by Triyoso (2024a) proved that by building upon a foundation of research in probabilistic seismic hazard analysis (Marzocchi and Taroni, 2014; Strader et al., 2018; Zeng et al., 2018; Taroni and Akinci, 2021; Triyoso 2023; Triyoso et al., 2023), conducted a study focused on earthquake spatial forecasting in the Sumatran subduction zone. The study involved creating and comparing models based on an extensive catalog of shallow earthquakes over different observation periods. Model fit regarding the reference event was evaluated using the Akaike Information Criterion (Akaike, 1974). The study identified a smoothed seismicity approach as the most reliable for model construction. This approach incorporated data from a complete catalog of small to moderate earthquakes with seismicity smoothing applied and normalized (Triyoso, 2024a; Triyoso et al., 2024). This data was combined with the normalized seismic moment rate from the surface strain rate to produce a spatial seismicity pattern model. Seismicity rate estimation combines the spatial seismicity model with a uniform background seismicity rate model. This background model was calculated from declustered earthquake data (M_w ≥ 5.0 and depth ≤50 km from 1963 to 2016) using the Gardner and Knopoff algorithm (Gardner and Knopoff, 1974) implemented in ZMAP software (Wiemer, 2001). The study suggests that using a complete earthquake catalog, encompassing smaller magnitudes and spanning an extended observation period, contributes to developing more reliable models.

Helmstetter et al. (2007) assessed the effectiveness of earthquake forecasting models by comparing their probability gain per earthquake to a uniform density model. Their study focused on a time-independent forecast derived from smoothed seismicity data. A key finding was the significant improvement in model performance when incorporating small earthquakes (M ≥ 2) to predict larger events (M ≥ 5). It suggests a relationship between the spatial distributions of smaller and larger earthquakes. Interestingly, the model’s performance remained consistent regardless of the minimum magnitude set for target events. The model effectively predicted events with magnitudes M ≥ 3 and M ≥ 5.5, further supporting that large earthquakes share spatial patterns with smaller ones. This research highlights the value of utilizing data from smaller earthquakes to estimate the probability of larger seismic events, contributing to advancements in earthquake forecasting and hazard assessment.

Rundle et al. (2011), Rundle et al. (2021) explored two contrasting earthquake forecasting models: activation and quiescence models. Both models leverage the frequency of small earthquakes as a predictor of larger events, but their core assumptions differ. Activation models, such as The Epidemic Type Aftershocl-Sequence (ETAS; Ogata, 2005), the Branching Aftershock Sequence (BASS; Holliday et al., 2007), and Short-Term Earthquake Probability (STEP; Gerstenberger et al., 2005), propose that a surge in small earthquakes signals a heightened risk of a large earthquake. Conversely, quiescence models suggest that a lull in small earthquake activity indicates a greater likelihood of a large earthquake.

Using the Z-value method, Sukrungsri and Pailoplee (2016) analyzed seismicity rate changes preceding eight significant earthquakes in the Sunda Arc from 2004 to 2012. The analysis was conducted on a comprehensive earthquake catalog (M_w ≥ 4.4) spanning 1980 to 2015. Optimal parameters, N = 50 events and T = 2 years, were determined to effectively identify anomalous Z-values associated with these major earthquakes. Spatial analysis revealed a strong correlation between seismic quiescence and subsequent large earthquakes. Three significant seismic quiescence zones were identified by applying these parameters to recent seismicity data (2008–2013): Nicobar Islands, Western offshore Sumatra, and Western Myanmar. These findings align with previous studies using the FMD b-value and RTL score methods, suggesting a high probability of future large earthquakes in these regions. While these methods provide valuable intermediate-term forecasting, short-term prediction remains a significant challenge. To enhance short-term forecasting capabilities, exploring alternative physical precursors, such as seismic electric signals or natural time analysis, may offer promising avenues.

Several studies have shown a correlation between decreasing b-values and the occurrence of large earthquakes. A lower b-value indicates higher regional stress accumulation, making it more susceptible to seismic events. De Santis et al. (2022) observed a significant decrease in b-value preceding the M_w 7.2 Kermadec Islands earthquake in 2019. Similarly, Wang et al. (2022) found that low b-value regions in Yunnan Province, China, had a higher probability of experiencing moderate to large earthquakes. Their analysis demonstrated the potential of b-value as a precursor for mid-to-long-term earthquake forecasting. Nuannin et al. (2012) highlighted the strong association between low b-values and large earthquakes in the Andaman-Sumatra region. All 15 significant earthquakes (M_w ≥ 7) between 2000 and 2010 occurred within low b-value zones, preceded by notable b-value decreases. This finding underscores the potential of b-value analysis for probabilistic seismic hazard assessment and mitigation. In the case of the 2002 and 2004 Sumatra earthquakes, the temporal b-value exhibited significant drops before the events, and spatial analysis revealed low b-values around the epicenters. While these studies suggest a promising link between b-value and earthquake occurrence, it is crucial to approach such analyses with caution. Retrospective studies can be prone to bias, and it is essential to conduct prospective tests to validate the predictive capabilities of b-value variations.

Triyoso (2023) assessed the reliability of using the b-value as a precursor for large earthquakes by evaluating the Probabilistic Seismic Hazard Function (PSHF) before these events. The study employed the Akaike Information Criterion (AIC) to assess the model’s reliability in constructing the PSHF. A key finding is identifying a critical δAIC value, which signifies a significant change in the PSHF. It suggests that a drastic change in the PSHF is associated with large δAIC values. As the earthquake potency and PSHF are linked to b-value variations, this method enables us to gauge the reliability of b-value as a precursor from a PSHA perspective. This insight can be valuable for improving PSHA and seismic mitigation strategies.

Triyoso (2024a) proposed an innovative approach to enhance the spatial forecasting of earthquakes (M_w ≥ 5.0) by addressing the limitations associated with using complete seismic catalogs, which can introduce bias in predicting future seismicity rates. Building upon established research in probabilistic seismic hazard analysis (Marzocchi and Taroni, 2014; Strader et al., 2018; Zeng et al., 2018; Taroni and Akinci, 2021; Triyoso 2023; Triyoso et al., 2023; Triyoso, 2024b), Triyoso et al. (2024) conducted a study on earthquake spatial forecasting in the Sumatran subduction zone. This study focused on developing and comparing models using a comprehensive catalog of shallow earthquakes recorded over various periods. The study highlighted two key improvements (Triyoso 2024a; Triyoso, et al., 2024).

1. Incorporating Foreshocks and Aftershocks: Recognizing the significance of small-scale seismic events, the study incorporated foreshocks and aftershocks into the model.

Foreshocks appear closely linked to the activation model. At the same time, strain accumulation, particularly in areas with rapid build-up, seems closely associated with the quiescence model, as Rundle et al. (2011) suggested.

Triyoso (2024b) emphasized the need for rigorous reference data selection and model development to effectively incorporate seismicity patterns, including quiescence periods, into seismic hazard analysis. Precisely capturing the evolution of seismic rates is paramount. As Yavas et al. (2024) underscored, identifying patterns within seismicity data, particularly clustering, is crucial for accurate forecasting and hazard assessment. Spatial smoothing of historical seismicity, a standard technique for extracting these clustering patterns, offers valuable insights for future seismic hazard assessment and mitigation strategies. Several studies, including Triyoso et al. (2020); Triyoso et al. (2024); Triyoso et al. (2023), have successfully applied Gaussian function-based seismicity smoothing techniques. This study delves into seismicity rate changes off the coast of Southern Sumatra-West Java using declustered data from a shallow crustal earthquake catalog. The analysis identified a period of relative seismic quiescence from 1993 to 2005 (P2) compared to 1978 to 1992 (P1). The result was interpreted that quiescence is closely correlated with periods of seismic strain accumulation.

Consequently, the potential for future large earthquakes is likely inversely proportional to activation probability. The study utilizes probability gain, an adapted concept presented by Helmstetter et al. (2007), to re-examine these findings. Instead of relying on a uniform density model, the study leverages the seismicity model from the period preceding the quiescence (1978–1992) as a benchmark for comparison. In alignment with the recommendations of Triyoso et al. (2024), strain accumulation is considered a crucial factor in identifying areas prone to earthquakes. By calculating the modified probability gain (mG) relative to the pre-quiescence model, the study aims to quantify and assess the likelihood of the identified quiescent zone representing a potential source area for future large earthquakes. The mG can be used to evaluate the potential for large earthquakes, providing valuable insights for hazard assessment.

Moreover, the study integrates the modified probability gain with the most robust seismicity rate model, as Triyoso (2024b) recommended, to enhance the accuracy and reliability of earthquake hazard assessments in the region. The study estimates the seismic hazard curve, expressed as peak ground acceleration (PGA) versus exceedance probability over 50 years for various sites. The amplification factor, calculated based on previous studies, is used to refine the PGA estimates. To further enhance the precision of PGA estimations for seismic disaster mitigation, an amplification factor specific to different locations is calculated using the horizontal vertical Spectral Ratio (HVSR) method on the data used and applied to the PGA estimations.

This study investigates the potential for a broader area for future large earthquakes in the Sumatran subduction zone and off the southern coast of West Java. The study identifies regions with heightened earthquake likelihood by analyzing historical seismicity data, employing the mG approach, and comparing it with the previous study findings (Triyoso et al., 2023; 2024; Triyoso 2024a; Triyoso 2024b). The mG values, calculated relative to a pre-quiescence seismicity model, highlight areas with increased strain accumulation. These findings estimate seismic hazards, including peak ground acceleration and potential tsunami heights. By integrating the mG model with a reliable seismicity rate model, the study aims to improve the accuracy of regional earthquake hazard assessments. Ultimately, this research contributes to a better understanding of seismic hazards in the region, enabling more effective disaster mitigation strategies.

This study utilizes the TUNAMI-N3 model, a Non-Linear Shallow Water Equation model developed by Tohoku University (Imamura et al., 2006), to simulate and predict tsunami behavior (Pakoksung et al., 2021). TUNAMI-N3, a modified version of TUNAMI-N2, incorporates linear theory in deep sea, shallow water theory in shallow sea, and runup on land with varying grids. Instead of directly using fault data, TUNAMI-N3 requires an initial wave model generated by the fault model. This initial wave is derived from the vertical deformation of the fault slip model using Okada’s method (Okada, 1985; 1992).

This study seeks to identify potential future large earthquake zones in the Sumatran subduction zone and off West Java’s southern coast. By analyzing historical seismicity data and applying the mG approach, we aim to improve our understanding of seismic hazards in the region. Our findings estimate ground shaking and potential tsunami impacts, which can inform more effective disaster mitigation strategies. By combining the mG model with a reliable seismicity rate model, we seek to enhance the accuracy of regional earthquake hazard assessments. The accuracy of the mG calculations is significantly influenced by the precision with which the period of seismicity rate change can be identified and characterized. By enhancing the identification and characterization of this rate change, the uncertainties can be minimized, and improve the overall accuracy of the calculations. In this study, identifying rate change between two distinct periods could be achieved by considering the magnitude completeness as a function of time and duration of the rate change. Thus, by evaluating both magnitude completeness as a function of time, it can be expected that the uncertainties could be reduced and the accuracy can be improved. By carefully considering those factors and adopting a rigorous approach to seismicity rate change identification, the accuracy and reliability of the mG calculations can significantly improve. It, in turn, will contribute to a better understanding of seismic hazards and ultimately enhance this study’s ability to mitigate their impact.

This study investigates shallow crustal earthquakes in the Andaman and off-coast northern Sumatra regions to characterize seismic patterns. We identify distinct periods of relative seismic quiescence: P2A (1986–2004) compared to P1A (1966–1985). There is a similar pattern between 1993 and 2005 (P2B) compared to the prior period (P1B) for the study area around off-coast central Sumatra–West Java. Our analysis suggests that these quiescence periods indicate seismic strain accumulation. Figures 3, 4 show identifying rate change between two distinct periods around the Andaman and off-coast northern Sumatra regions and off-coast central Sumatra–West Java and the seismicity smoothing algorithm using the recommended correlation distance of 75 km as suggested by Frankel (1995) is applied to both datasets. The modified probability gain (mG) is then estimated for the period P1A and P2A for Andaman and off-coast northern Sumatra regions and period P1B and P2B for off-coast central Sumatra–West Java. The result can be found in Figures 5A, B. Figure 5C shows the estimation and merging result of mG based on the period P1A and P2A, P1B, and P2B along Andaman–Off-coast Sumatra and West Java.

Comparing the result of Figures 5A, B, we may find the consistency between the consequence of Triyoso et al. (2023) and Triyoso et al. (2024), in which a significant strain build-up could be found around the off-coast of Northern Sumatra around Simelue, Nias, upto Batu Island (Figure 5A). A similar result is also found around Mentawai Island, in which the result of Figure 5B can be interpreted as a significant strain build-up consistent with the result of Triyoso et al. (2023) and Triyoso et al. (2024).

To compare the area that could be interpreted as the high potential area of strain build-up around Mentawai between the result of this study and Triyoso et al. (2024), it appears that the fault model of Triyoso et al. (2024) supports the finding of this study and the result of this study is consistent with Triyoso et al. (2024), which is related to the possible source around the Mentawai Islands. Thus, in terms of the PGA and Tsunami Height simulation estimated using the maximum potential earthquake magnitude, the result of Triyoso et al. (2024) will not change. However, compared to Triyoso et al. (2023), the result of this study appears to be more significant in terms of PGA and Tsunami Height estimated. This is because the fault parameter is larger than the result of Triyoso et al. (2023).

To assess the potential for future large earthquakes, we propose incorporating seismicity patterns, especially periods of quiescence, into seismic hazard assessments. Following Triyoso’s (2024a) approach, we utilize a combined model that integrates normalized seismicity smoothing, normalized geodetic moment rate (M _o-rate ), and normalized mG values (mG-model) to characterize the likelihood of earthquake occurrence within specific source areas. The seismicity smoothing is based on the shallow catalog of relatively small to moderate earthquakes (Eq-model). Then, the seismicity rate is produced by multiplying the combined model with the uniform background rate (A _background ) of the declustered shallow earthquake data used to develop the seismicity rate model. The Eq-model is constructed based on the shallow earthquake data of 1963–1999 and the A _background . It is proposed that the spatial correlation between the sizeable shallow earthquake events after 1999 and before 1963 be evaluated to better understand the future potency by overlapping with the historical shallow large earthquakes from 1900 to 1962 and 2000 to 2020. Through this analysis, we intend to understand the potential of earthquakes better to anticipate and prepare for possible significant earthquakes and tsunami hazard analysis, as shown in Figure 6A. Figure 6B shows the result of the sources for possible future significant earthquakes and tsunami overlay with the shallow large earthquake of 2000–2020. Figure 6C shows the result of the overlay with the shallow large earthquake of 1900–1962. Based on Figure 6A, the result of this study supports the study of Martin et al. (2019) and others (Bilek and Engdahl, 2007; Chlieh et al., 2007; Konca et al., 2008; Hill et al., 2012), especially in characterizing the possible source of significant earthquake events of 2004, 2005, as well as 1797 and 1833. An important lesson learned could be noted from the result of Figure 6A, which is primarily related to the 2004 source event. There is a high possibility of simultaneously breaking several source areas along the Sumatran subduction zone closely associated with the existence of sliver force, as pointed out by McCaffrey (2009). The rapid generation of the 2005 M8.6 earthquake suggests a strong connection between static stress loading from large earthquakes and the triggering of subsequent significant events. As highlighted by Pollitz et al. (2006) and Triyoso and Sahara (2021), this mechanism likely played an essential role in the occurrence of this particular earthquake. In our interpretation, it can happen when a relatively high rate’s closure closes and is connected, as shown in Figure 6.

A more detailed analysis of the result in this study, especially around off-coast Southern Sumatra–West Java, overlaps it with the historical large earthquake data from 1900 to 1962 to characterize the possible future earthquake or tsunami potential. It coincides with the area of the 1903 subduction earthquake event (Newcomb and McCann, 1987), especially around the off coast of West Java. By following the suggestion of Shearer and Burgmann (2010), in which every active subduction zone should be considered a potential source of great earthquakes, then referring to Huchon and LePichon (1984), Triyoso et al. (2024) and Triyoso (2024b), we characterize the fault parameter model to estimate the possible future earthquake or tsunami potential around the off-coast of central Sumatra. For the off-coast of southern Sumatra–West Java, as shown in Figure 7, based on Figure 7, we could identify the possible seismic gap area of the seismic and tsunami hazard in the future. The potential source area around the Mentawai zone shows a consistent result compared to Triyoso et al. (2024), and even the possible source zone of the 1935 event could be identified (Rivera et al., 2002). Our segmentation model around the off-coast of southern Sumatra-West Java is a bit different compared to Supendi et al. (2023), especially around the front of Sunda Strait since we found the consistency based on the mG model and suggestion result of Huchon and LePichon (1984) also coinciding the mG area with the area of the 1903 subduction earthquake event as pointed out by Newcomb and McCann (1987), especially around the off-coast of West Java.

Figure 8 shows the estimated seismic hazard expressed as the MPE Peak Ground Acceleration (g) at the base rock based on the defined fault parameter around the off-coast of southern Sumatra (A) and the off coast of West Java (B). It can be found that at the site of S1-S19, the PGAs estimate are around ∼0.05g.

Figure 9A illustrates the seven selected sites (S1, S2, S3, S4, S5, S6, and S9) used to estimate the Probabilistic Seismic Hazard Function (PSHF). Aligned with Triyoso et al. (2020) and Triyoso and Sahara (2021), our probabilistic seismic hazard calculations considered a maximum radius distance of approximately 100 km and a magnitude range of 6.0–9.0. As depicted in Figure 9B, the resulting PSHF curve, representing the 10% probability of exceeding earthquake events within 50 years, indicates estimated peak ground acceleration (PGA) values in the 150 to 200 gals range.

The tsunami modeling results from the proposed tsunamigenic area show that the maximum tsunami height of about ∼8m along the coastline occurred perpendicular to the source region along the coast of Southern Sumatra and West Java based on the scenario A by referring to Bilek and Lay (1999) with the maximum uniform slip of 9 and 8 m with rigidity used about 33.9 GPa as is shown by Figure 10A. Based on the result of scenario B with the maximum uniform slip of 15 and 14 m with rigidity used of about 24.3 GPa, as is shown in Figure 10B, the tsunami height reaches up to 15m with a tsunami arrival time between 20–30 min. Along the coast of Southern Sumatra–West Java, the height ranges between 4 m, and the maximum tsunami height reaches 11m–15m. Offshore Enggano Island shows a high risk of tsunami attack based on both scenarios A and B of both tsunami sources model.

This research provides valuable insights into seismic hazards in the Sumatran subduction zone and off-coast southern West Java. The result of this study suggested to be used to enhance the impact for urban studies and policymakers by considering the following findings and implications that can be used for some recommendations for urban studies and policymakers as follow (Aldea et al., 2018; Matari et al., 2023; Sabeur et al., 2023).

This study investigates the potential for future large earthquakes in the Sumatran subduction zone and off the coast of West Java. By analyzing historical seismicity data and applying the mG approach, the researchers identified areas with significant strain accumulation and potential for large earthquakes. The key findings are as follows: Relative Quiescence: The study identified periods of relative seismic quiescence in the Andaman and off-coast northern Sumatra regions as well as off-coast central Sumatra and West Java. These periods suggest potential strain accumulation and increased risk of future large earthquakes. Potential Source Areas: The researchers identified potential areas for future large earthquakes, including the Mentawai Islands, the off-coast of northern Sumatra, and the off-coast of southern Sumatra-West Java. Tsunami Height Estimation: The study estimated potential tsunami heights based on different earthquake scenarios. The results indicate significant tsunami hazards, particularly along the coasts of southern Sumatra and West Java. Based on this study, some recommendations related to urban planning and policymakers could be described as follows: Incorporate Findings into Urban Planning: Consider seismic hazards in land use planning, building codes, and infrastructure development. Enhance Disaster Preparedness and Response: Develop comprehensive emergency response plans, invest in early warning systems, and strengthen building codes. Support Scientific Research: Continue research on seismic hazards and tsunami modeling to improve understanding and prediction capabilities. Effective Communication and Engagement: Communicate findings clearly and engage with stakeholders to develop tailored mitigation strategies. By implementing these recommendations, urban planners and policymakers can make informed decisions to reduce the impact of future seismic events and build more resilient communities.