Institute of Seismology

This study actively investigates stress ratios, principal stress orientations, and stress regimes in the West Java Province, Indonesia, by simultaneously inverting both the stress tensor and fault orientations from 232 moment tensors. It identifies diverse stress regimes, including normal and reverse faulting in the subduction zone, as well as mixed reverse and strike-slip faults across the island. Stress ratios range from 0.08 to 0.97. Sedimentary basins in the northeast produce lower values, while tectonic forces near the southern subduction zones generate higher values. In the subduction zone, plate bending generates normal faulting, while compressional forces lead to reverse faulting. The fore-arc region exhibits a variety of regimes: normal, reverse, and strike-slip, indicating transitions in the dynamics of subduction. Across the island, a combination of reverse and strike-slip faulting suggests crustal compression, with Mount Anak Krakatau exhibiting evidence of normal faulting. The study uncovers a complex interplay of subduction processes, crustal deformation, and geological heterogeneities. It outlines stress regimes ranging from normal and reverse faulting in the subduction zone to mixed reverse and strike-slip faulting on the island. These findings offer crucial insights into the tectonic processes shaping West Java, and lay the groundwork for more informed seismic hazard assessments and risk mitigation strategies.

Bridgmanite is the most abundant intrinsically-anisotropic constituent of the lower mantle. Its deformation, thus, potentially translates to large-scale anisotropy that would accumulate in high-stress regions, particularly the interaction between subducted materials and the surrounding mantle. While most of the lower mantle is generally considered well-mixed, recent observations suggest structures at mid-mantle depths (800 – 1500 km). Their origin, however, often remains enigmatic. Recent state-of-the-art deformation experiments in bridgmanite at lower-mantle temperatures and pressures reveal a depth-dependent behavior of anisotropy. Coupled with realistic geodynamic models of mantle convection, the large-scale imprint of the depth-dependent fabric reveals a purely deformation-driven seismic discontinuity between 1000 – 1400 km depth that matches observations. The discontinuity appears sharpest in actively deforming regions, and becomes negligible closer to neutral ones. In this work, we investigate its seismic detectability around a subduction zone using three-dimensional global waveform modeling via AxiSEM3D. Given its likely presence across a broad range of frequencies, we assess the suitability of SS precursors for detecting this feature. Enhanced using array seismological methods, results show the presence of SS precursors in the transverse component generated by the anisotropic discontinuity. In addition, a flattened slab geometry produces a shallower SS precursor due to the interface formed between an overlying isotropic slab and an underlying strongly anisotropic (VSH>VSV) layer. We examine the azimuthal dependence of precursor amplitudes and their frequency dependence, with particular emphasis on the microseismic frequency band (~0.05 – 0.25 Hz). In light of these recent findings, we discuss its implications on the nature and origin of mid-mantle discontinuities.

The interseismic crustal strain-rate distribution in Japan has traditionally been estimated from coordinate time series derived from GEONET, the nationwide GNSS network operated by the Geospatial Information Authority of Japan (GSI). Beginning with Sagiya et al. (2000) and subsequent studies, these analyses have revealed the existence and broad extent of inland strain-concentration zones. However, because the average spacing of GEONET stations is ~20 km, its ability to resolve highly localized deformation, such as strain accumulation associated with individual active faults, has remained limited.In contrast, SoftBank Corp. (hereafter SoftBank), a Japanese telecommunications company, has operated an independent nationwide GNSS network of more than 3,300 stations since late 2019, nearly three times the number of GEONET stations. The suitability of SoftBank stations for crustal deformation monitoring was demonstrated by Ohta and Ohzono (2022).By integrating GNSS data from GEONET and SoftBank, we constructed an unprecedentedly dense observation network and estimated interseismic crustal strain-rate fields at substantially higher spatial resolution. The integrated network achieves an effective station spacing of

Over the past two decades, seismic hazard modeling has advanced along two complementary frontiers: empirical probabilistic frameworks, which systematically capture uncertainty through statistical inference, and physics-based simulation platforms, which directly compute ground motions from the governing equations of wave propagation. This project seeks to unify these two worlds by developing an end-to-end integration between OpenQuake and CyberShake, thereby creating a new generation of seismic hazard models that are globally extensible, probabilistically complete, and physically consistent. CyberShake has been under active development for more than a decade, demonstrating its robustness and scientific maturity through extensive implementations in California. It performs a physics-based probabilistic seismic hazard analysis (PSHA), replacing traditional empirical Ground Motion Prediction Equations (GMPEs) with full 3D numerical simulations of seismic wave propagation. Built upon the UCERF2/3 Earthquake Rupture Forecasts, CyberShake computes hazard curves directly from synthetic seismograms generated via Strain Green’s Tensors and thousands of stochastic rupture variations. This approach enables non-ergodic, site-specific hazard estimation and has set a global benchmark for high-fidelity hazard computation. However, its application has remained geographically limited: both the ERF and 3D velocity models were designed specifically for California, requiring extensive datasets that are rarely available elsewhere. Conversely, OpenQuake, developed by the Global Earthquake Model (GEM) Foundation, provides a fully open-source, Python-based framework for probabilistic seismic hazard and risk analysis. It serves as the computational backbone of large-scale hazard models such as the European Seismic Hazard Model 2020 (ESHM20), which integrates decades of regional expertise into a unified and statistical representation. OpenQuake provides a complete probabilistic framework to build Earthquake Rupture Forecasts (ERFs) that combine declustered catalogs, background seismicity, and multi-branch logic trees, ensuring a balanced and uncertainty-aware representation of regional tectonics. Furthermore, its ecosystem extends seamlessly to vulnerability and exposure modules, enabling the translation of hazard into actionable risk assessments and resilience planning.This project will establish a direct pipeline from OpenQuake’s event-based results to the generation of an ERF compatible with CyberShake’s simulation framework, ensuring moment–rate consistency. By doing so, it will enable CyberShake simulations to be performed for regions beyond California, extending its use to Europe based on the knowledge contained in the ESHM20. The first pilot region is Istanbul, Turkey, a densely populated metropolis located near the western termination of the North Anatolian Fault. Our initial results show that the workflow is already functioning at the prototype level: we have developed a unified 3D velocity model for the Istanbul region by combining available tomographic models with local datasets; generated preliminary event-based rupture catalogs from ESHM20 using OpenQuake; and demonstrated early convergence behavior in hazard curves, indicating that the rupture sampling strategy is statistically robust. These initial results demonstrate the feasibility of the integration approach and indicate that the essential elements needed for a CyberShake-ready ERF are already in place.

Construction of precast reinforced concrete (PRC) industrial halls in seismically active areas has been increasing in recent decades. As connections are one of the most sensitive and vulnerable zones of PRC structures, there is a need to pay special attention to their investigation and modeling in seismic analysis. Knowing that each PRC system is specific and unique, this study aims to evaluate the actual seismic performances of PRC industrial halls built in the AMONT system, which represent a significant portion of the existing industrial building stock in Italy, the Balkans, and Turkey. As there is a lack of published research data on its specific joints, the results of the quasi-static full-scale experiments carried out up to failure on the models of four characteristic connections are presented. Since the implementation of nonlinear dynamic analysis in everyday engineering practice can be demanding, a simplified model of the structure considering the effects of the connections’ stiffness is proposed in this paper. The differences in the roof top displacements between the proposed model and the model with the rigid joints of the analyzed frames are in the range from 16.53% to 66.93%. The values of inter-story drift ratios are larger by 10–100% when the real stiffness of connections is considered, which is above the limit value provided by standard EN 1998-1. These results confirm the necessity of considering the nonlinear behavior and stiffness of connections in precast frame structures when determining displacements, which is particularly important for the verification of the serviceability limit state of structures in seismic regions.

SUMMARY Traditional seafloor mapping relies on shipborne soundings which have limited spatial coverage. The Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite holds the potential for predicting more detailed seafloor topography. In this study, we integrate SWOT gravity data with single-beam shipborne depths to construct seafloor topography models in the Northwestern Pacific using the deep neural network (DNN) method. Compared to shipborne depth checkpoints, the root mean square (RMS) error of the differences between topography model predicted by DNN method and shipborne depths is approximately 97.5 m, improving by 19.5 per cent and 9.9 per cent compared to the gravity-geologic (GGM) method and the Smith and Sandwell (SAS) method respectively. Compared to traditional data, the integration of SWOT gravity data universally enhances prediction accuracy. Furthermore, the DNN method effectively demonstrates superior capability in balancing the characterization of overall structures with the retention of authentic topography features, which we demonstrated in the Mariana region of the NW Pacific Ocean. However, limited by spatial heterogeneity and physical mechanisms, accurate prediction of such complex, fine-scale topography using gravity data remains a significant challenge.

This article examines the role of Islamic law in shaping maritime governance in the Sultanate of Banten during the 16th and 17th centuries, with particular emphasis on its incorporation into administrative and regulatory practices. Rather than approaching Islamic law as a fixed or purely doctrinal system, the study conceptualizes it as a set of normative references embedded in port administration, commercial regulation, and maritime security. Adopting a socio-legal historical approach, the analysis draws on Bantenese legal manuscripts, European travel accounts, and Dutch colonial records. The findings demonstrate that Islamic legal norms operated in interaction with political authority and mercantile institutions through the consultative involvement of religious scholars, institutional coordination between the Sultan and port officials (syahbandar), and the selective application of legal sanctions and fiscal arrangements to govern a plural trading community. This article offers empirically grounded insights into Islamic law as a context-specific practice within Southeast Asian maritime societies.

A atividade turística com ênfase em Educação Ambiental torna visível os sistemas de produção agroecológicos enquanto promove a aproximação da população urbana com o meio rural. Assim, objetivou-se identificar atrativos turísticos potenciais do Sítio-Escola Mukondo e formular propostas para implementação da atividade turística. A metodologia é baseada em um estudo de caso, fundamentada na pesquisa-participante, e se divide em (i) caracterização do local de estudo e (ii) avaliação e perspectivas de desenvolvimento do turismo. O Sítio está localizado em Áurea/RS e possui 22,5 ha manejados de forma agroecológica desde 2015 e integra a Rede Ecovida de Agroecologia. A área de cultivo agroecológico tem se constituído num atrativo para visitação técnica, visto que o sistema de cultivo praticado se diferencia do sistema convencional difundido e praticado no entorno e região. Possui também recursos hídricos como nascentes e açudes que associados à vegetação nativa preservada constituem uma paisagem de beleza significativa. A comercialização de artesanatos e alimentos é insipiente, mas institui-se um elemento potencial para a consolidação da experiência turística. Espera-se contribuir na divulgação sobre a importância das práticas agroecológicas no âmbito da agricultura familiar, além de colaborar para a diversificação da oferta turística local.

Insider threat has become a significant challenge in the field of network security due to its dual characteristics of strong concealment and high destructiveness. If such threats are not detected, they can have disastrous consequences for enterprises or organizations, including key data leakage, disruption of business continuity, and reputation damage. Traditional insider threat detection methods often rely on static threshold strategies or shallow machine learning models, which suffer from the drawback of insufficient detection accuracy. This paper proposes a deep convolutional neural network detection framework. Firstly, a feature selection mechanism is constructed based on random forests, and redundant dimensions with less influence are eliminated using a voting mechanism. Then, a deep neural network is employed to extract local-dependent features of behavior sequences. Finally, key information about insider threats is effectively captured, and the detection results are obtained. Experiments on the public dataset of the CMU CERT R6.2 Research Center show that the accuracy of this method reaches 95.8%, which higher than traditional statistical machine learning methods such as Support Vector Machine and Random Forest, validating the effectiveness of the method.

Abstract This paper provides an account of the design, implementation, and operational insights from a first-of-its-kind Enhanced Geothermal System (EGS) proppant stimulation targeting a dry rock setting with ~330 °C (626 °F) bottomhole temperature. It highlights the unique challenges of creating a high-temperature EGS and the innovative strategies employed to mitigate them, with the goal of advancing the scalability and viability of geothermal energy as a sustainable resource. The demonstration project involved recompletion of an existing wellbore to facilitate advanced stimulation strategies aimed at enhancing rock permeability in extreme thermal and mechanical stress conditions. Tailored high-pressure stimulation techniques, supported by real-time monitoring, were employed to create a system of interconnected pathways between an injector and future producer well pair. Simulation models were used to predict fracture propagation and optimize stimulation parameters. A suite of diagnostic tools, including fiber optics, microseismic arrays, acoustic pulsing, and chemical/nanotracers, provided real-time and post-process insights into the engineered reservoir volume and fluid dynamics. Initial results indicated that the project has created a reservoir with enhanced flow capability through propped fractures. The use of fiber optics yielded real-time data on temperature and microseismics, while microseismic mapping provided insights on subsurface dynamics and fracture orientation validation. Preliminary findings demonstrate the feasibility of high-temperature EGS development, while ongoing interpretations aim to refine understanding of reservoir behavior and optimize operational strategies. Currently, the drilling and stimulation of the twin well has been completed utilizing an optimized stimulation approach including but not limited to the implementation of the world's first sleeve above 300 °C (572F) for stimulation purposes, application of hybrid stimulation fluid for geometry creation and many more innovative approaches. This project represents a pioneering effort in extreme-temperature EGS development, contributing invaluable data and operational insights to the geothermal sector. By addressing the unique challenges of high-temperature reservoirs, the paper offers insight and adaptive strategies that serve as a foundation for future EGS geothermal initiatives and enables the development of superhot rock (SHR) geothermal to support scalable, dense, lower-carbon intensity energy sources.