The research, carried out by Muqit Ajmain Shahriyar of the Department of Geology and Mining at the University of Rajshahi, pits two established techniques against each other across the same landscape. The first is Probabilistic Seismic Hazard Analysis, or PSHA, the quantitative backbone of modern building codes worldwide. PSHA treats earthquakes as a statistical problem: it combines a catalogue of past events, a recurrence relationship describing how often quakes of each size occur, and an equation predicting how strongly the ground will shake at a given distance from a rupture. The second approach is the Analytic Hierarchy Process, or AHP, a multi-criteria decision-making method that weighs contributing factors, such as fault proximity and geological conditions, against one another to produce a susceptibility ranking. Because the two methods rest on entirely different assumptions, agreement between them carries real weight.

The probabilistic half of the study was built on a grid-based framework covering the entire corridor from Sylhet to the Chittagong hill districts. Shahriyar compiled a regional earthquake catalogue and fitted it with a Gutenberg-Richter recurrence analysis, the classic logarithmic relationship that links the frequency of earthquakes to their magnitude. Ground shaking was then estimated using the Ground Motion Prediction Equation developed by Sharma and colleagues in 2009, which draws on strong-motion data from the Himalayan and Zagros regions, tectonic environments broadly comparable to Bangladesh’s. The analysis was run for two standard hazard levels: a 10 percent probability of exceedance in 50 years, corresponding to a return period of roughly 475 years, and a more stringent 2 percent probability, corresponding to roughly 2,475 years, the level typically used for critical infrastructure design.

The numbers that emerged are striking. At the 475-year return period, predicted peak ground acceleration values range from 0.084 g to 1.53 g across the study area, where g is the acceleration due to gravity. At the 2,475-year level, the range widens to between 0.10 g and 1.89 g. For context, shaking above roughly 0.2 g is generally strong enough to cause damage to vulnerable buildings, and values approaching 1 g and beyond imply violent ground motion capable of devastating even well-constructed structures. The upper end of these estimates is concentrated in a specific location: Barkal Upazila in Rangamati District, in the Chittagong Hill Tracts, emerges from the PSHA as the single highest seismic hazard zone in the entire study corridor.

Barkal does not stand alone. The probabilistic maps also flag elevated hazard across surrounding parts of Rangamati, as well as in Chandpur, Cumilla, Sylhet, and Moulavibazar. This spatial pattern reflects the geometry of the underlying fault systems, particularly the active structures of the Tripura Fold Belt and the Chittagong-Tripura Boundary Fault, which thread through the region and accumulate strain as the plates continue their slow collision. The hazard values are not predictions of when an earthquake will strike, but statements about the level of shaking that has a defined probability of being exceeded within a given window of time, a distinction that matters greatly for engineers and planners who must translate the numbers into design requirements.

The second half of the study took a different route to the same question. Using the Analytic Hierarchy Process, first formalized by mathematician Thomas Saaty in the 1970s, the assessment assigned relative weights to the factors that control seismic susceptibility and combined them into a single index for each location. The AHP-based susceptibility map shows a distribution that is broadly comparable to the PSHA result but with subtle differences in where the highest categories fall. In this model, high-susceptibility zones cluster in parts of Sylhet, Sunamganj, Moulavibazar, Feni, Cumilla, and Chittagong. The differences between the two maps are instructive: PSHA is driven primarily by earthquake recurrence and ground-motion physics, while AHP responds to the weighted combination of susceptibility criteria, so each method illuminates aspects of the hazard picture the other may understate.

Where the two approaches overlap is where the findings become most consequential. Sylhet, Moulvibazar, Sunamganj, Chandpur, Cumilla, Rangamati, and Chittagong are all identified as areas of elevated seismic potential by both methods independently. That convergence is significant because these are not empty hinterlands. Sylhet and Chittagong are major urban centers with rapidly growing populations and building stocks of widely varying quality, and the intervening districts contain millions of residents. When two techniques with different theoretical foundations point to the same places, the case for prioritizing those areas in seismic risk mitigation and land-use planning becomes difficult to ignore.

The study arrives at a moment when Bangladesh’s seismic exposure is drawing increasing scientific attention. Previous work has applied the Gutenberg-Richter relationship and spectral analysis to national seismicity, updated probabilistic hazard assessments for the country as a whole, and used GIS-based AHP methods to assess earthquake risk in other tectonically active regions, such as Bitlis Province in Türkiye. The comparative approach taken here adds a layer of robustness that single-method studies cannot provide. It also highlights a practical challenge for a country whose building codes have been revised to address geotechnical earthquake engineering concerns: hazard maps are only useful if they are trusted, and demonstrating that independent methods converge on the same high-risk zones helps build that trust among the officials who must act on them.

For residents of the region, the practical message is that the earthquake threat is not evenly distributed, and the areas of greatest concern are now mapped with two independent lines of evidence. The highest modeled shaking, approaching 1.9 g in the most extreme scenario, is centered on the hill tracts of Rangamati, while a broad band of elevated hazard runs through the Sylhet region and the central districts along the fold belt. Translating these hazard levels into safer communities will require region-specific mitigation measures, from enforcing seismic design provisions in new construction to retrofitting vulnerable buildings and steering future development away from the most susceptible ground. The study’s author notes that the data generated and analyzed are available from the corresponding author upon reasonable request, and the work was carried out with academic support from the Department of Geology and Mining at the University of Rajshahi, without external funding. As the plates beneath Bangladesh continue their inexorable convergence, maps like these, tested against each other and grounded in decades of seismological theory, offer one of the few tools available for looking ahead to the next great earthquake before it arrives.

Subject of Research: Comparative seismic hazard and susceptibility mapping in northeastern to southeastern Bangladesh

Article Title: Comparative seismic hazard assessment using PSHA and AHP-based susceptibility mapping in northeastern to southeastern Bangladesh

Article References: Shahriyar, M. A. (2026). Comparative seismic hazard assessment using PSHA and AHP-based susceptibility mapping in northeastern to southeastern Bangladesh. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02672-w

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02672-w

Keywords: seismic hazard, PSHA, Analytic Hierarchy Process, Bangladesh, peak ground acceleration, Gutenberg-Richter, ground motion prediction, Dauki Fault, Chittagong-Tripura Boundary Fault, Sylhet, Rangamati, earthquake risk

Arias intensity and cumulative absolute velocity, often abbreviated as Ia and CAV, are not the household names of earthquake science that peak ground acceleration has become, but among engineers they are prized quantities. Arias intensity, introduced by the Chilean engineer Arturo Arias in 1970, measures the cumulative energy per unit weight absorbed by a simple oscillator subjected to a ground motion record, effectively capturing the total energy content of shaking over its entire duration. Cumulative absolute velocity, developed in the United States in the late 1980s as a criterion for judging whether a nuclear facility had experienced an earthquake exceeding its operating basis, sums the absolute acceleration over time and has proven a robust indicator of structural damage potential. Both measures are central to assessing landslide triggering, liquefaction, and the performance of buildings and infrastructure during prolonged shaking.

Despite their importance, most prediction equations for these intensity measures were built from onshore data. Offshore ground motions, recorded on the seafloor rather than on land, follow different propagation and site amplification rules, and treating them as interchangeable with onshore shaking can lead to serious misestimates of hazard for submarine cables, ports, coastal cities, and offshore infrastructure. The new study, led by Jingyang Tan and Xialei Zhu of China Three Gorges University together with Jinjun Hu and Yinan Zhao of the Institute of Engineering Mechanics, China Earthquake Administration, set out to close that gap for the Japan Trench region using data from the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench, known as S-net.

S-net, operated by the National Research Institute for Earth Science and Disaster Resilience, is the world’s largest cabled ocean-bottom monitoring network, spanning the trench that produced the devastating magnitude 9.0 Tohoku earthquake of 2011. The researchers assembled an exceptionally large dataset for offshore ground motion modeling: 28,950 three-component records from 150 ocean-bottom stations. These records captured 553 earthquakes across three tectonic families, including 130 subduction interface events, where the Pacific plate grinds beneath the Okhotsk plate; 243 slab earthquakes, occurring within the descending plate at depth; and 180 shallow crustal and upper mantle earthquakes. This breadth allowed the team to develop models covering the full range of earthquake mechanisms that threaten the region.

A central innovation of the study lies in how it characterizes the ground beneath each station. Rather than relying on geological maps, the team classified sites using the horizontal-to-vertical spectral ratio technique, or HVSR, which compares the amplitude of horizontal and vertical shaking at a site to reveal its resonant frequency, a fingerprint of soil stiffness and sediment thickness. This site classification scheme, applied uniformly across the ocean-bottom network, allowed the researchers to incorporate site effects directly into their prediction equations. The models account for the key explanatory variables that govern shaking: moment magnitude, rupture distance, focal depth, site class, and, uniquely for offshore settings, the installation condition of the seafloor instruments.

That last variable turned out to matter far more than anyone might have guessed. S-net stations come in different flavors: some instruments are buried in the sediment, while others sit unburied on the seafloor inside pressure vessels. The analysis revealed that the installation method significantly influences recorded Arias intensity and cumulative absolute velocity, with unburied stations exhibiting notably higher values. The likely culprit is the natural vibration of the instrument housing and its coupling with the soft seafloor, which can introduce amplification that is not representative of the true ground motion. For engineers using offshore records, this finding is a caution: ignoring installation conditions could inflate or deflate energy estimates and skew hazard calculations.

The site classification itself proved to be the study’s most valuable uncertainty-reducing tool. When site classes were explicitly included in the models, the model uncertainty, expressed statistically as the standard deviation of the residuals, dropped substantially compared with models that omitted site effects. By contrast, two other candidate explanatory variables, water depth and sedimentary thickness, had only negligible effects on model uncertainty. This is a practically important result, because it suggests that a relatively simple HVSR-based site classification captures most of the site-related variability in offshore energy measures, sparing modelers the difficulty of obtaining detailed sediment profiles from the deep ocean floor.

Comparing their offshore models with existing onshore equations revealed a systematic and physically meaningful difference. Offshore Arias intensity and cumulative absolute velocity exhibit a substantially greater energy accumulation effect than onshore ground motions at comparable distances and magnitudes. The researchers attribute this to amplification by soft seafloor sediments, the water-saturated, low-velocity materials that blanket much of the Japan Trench margin. Soft sediments lengthen the duration of shaking and concentrate seismic energy at low frequencies, inflating cumulative measures even when instantaneous peaks remain moderate. The finding echoes earlier comparisons of peak ground acceleration and response spectra between land and ocean-bottom stations in northeast Japan, but extends the picture to energy-based measures that are more directly tied to damage in flexible structures and to soil failure phenomena such as liquefaction.

Beyond the prediction equations themselves, the team developed spatial correlation models for offshore Arias intensity and cumulative absolute velocity. These geostatistical tools quantify how similar the shaking is at two sites separated by a given distance during the same earthquake, a quantity known as spatial correlation. In seismic risk analysis, spatial correlation governs whether damage concentrates in one neighborhood or spreads across an entire region, which is critical for estimating losses to distributed systems like power grids, pipelines, and transportation networks. By characterizing the spatial dependence of offshore energy measures for the first time in this region, the models enable more realistic simulations of earthquake scenarios that span both land and sea.

The practical reach of the new models extends across the earthquake engineering pipeline. Arias intensity feeds into empirical methods for predicting earthquake-induced landslide displacements through Newmark’s sliding block analysis, while cumulative absolute velocity underpins thresholds for structural damage and liquefaction assessment, including criteria originally developed for nuclear facilities and more recent applications to lateral spreading evaluation. With reliable offshore predictions of both measures, engineers can now assess hazards to submarine infrastructure, evaluate the seismic performance of coastal facilities, and refine probabilistic seismic hazard analyses for the densely populated Pacific coast of northern Japan. The researchers note that the models are applicable to the Japan Trench region and can serve as a reference for seismic hazard assessment in offshore areas more broadly, offering a template for other subduction zones, from Cascadia to Nankai, where ocean-bottom networks are expanding and the seafloor is no longer a blind spot in earthquake science.

Subject of Research: Development of offshore ground motion prediction models for Arias intensity and cumulative absolute velocity in the Japan Trench region using HVSR-based site classification

Article Title: A new ground motion prediction model for Arias intensity and cumulative absolute velocity in the Japan Trench region based on HVSR site classification

Article References: A new ground motion prediction model for Arias intensity and cumulative absolute velocity in the Japan Trench region based on HVSR site classification. (n.d.). https://doi.org/10.1007/s10518-026-02683-7

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02683-7

Keywords: Arias intensity, cumulative absolute velocity, ground motion prediction, Japan Trench, S-net, ocean-bottom seismology, HVSR site classification, seismic hazard assessment, subduction earthquakes, site amplification, spatial correlation, offshore ground motion

The problem begins with the complexity of an earthquake source. A major earthquake does not rupture as a single point that releases energy uniformly. It tears across a fault surface, often over tens or hundreds of kilometres, while different sections slip by different amounts and radiate seismic energy with different strengths. A thrust earthquake occurs when one block of crust is pushed up and over another, commonly at subduction zones where one tectonic plate dives beneath another. These events can generate intense and prolonged shaking, and their shallow portions may produce particularly important long-period motions. To simulate them, researchers divide the fault into many smaller subfaults. Each subfault is assigned properties such as rupture timing, slip, stress drop and radiation behaviour. The individual contributions are then combined after accounting for wave propagation, geometric spreading, attenuation and the response of local geological materials. This finite-fault strategy captures spatial variation that a simpler point-source model cannot.

EXSIM has become a standard tool for this type of calculation because it can produce broadband ground motions without requiring the immense computational resources of a fully dynamic rupture simulation. In a stochastic simulation, the earthquake’s frequency content is represented statistically, typically through a source spectrum that describes how much energy is radiated at different frequencies. Random phases are then used to construct time histories, while the model incorporates distance-dependent path effects and site amplification. The method is especially useful when engineers need many scenarios, including earthquakes that have not yet occurred. Yet the researchers identify a significant limitation in conventional EXSIM calculations: for large thrust earthquakes, it can overpredict ground motion at low to intermediate frequencies. Such frequencies are associated with shaking periods that can strongly affect large buildings, bridges, industrial facilities and sedimentary basins. An overestimate may lead to conservative designs, but it can also misrepresent which structures and locations face the greatest risk.

The source of the bias is linked to the way earthquake spectra are represented. Many small and moderate earthquakes can be approximated with a single corner frequency, a transition in the spectrum associated with the size and duration of the rupture. Large shallow thrust earthquakes, however, often show a double-corner-frequency source model. In simplified terms, the spectrum changes slope at two characteristic frequencies rather than one, reflecting the fact that different parts or scales of rupture contribute distinct energy patterns. A model that does not adequately represent this structure can assign too much energy to the low-to-intermediate-frequency range when it scales the motions of smaller subevents to a larger fault. The issue becomes more pronounced as rupture dimensions, slip distributions and source complexity increase. Rather than treating the discrepancy as a simple correction factor, the new method modifies the fault representation itself, allowing the spatial distribution of high-stress regions to influence the simulated spectrum and the resulting time histories.

Its central innovation is an asperity-distributed stress-drop composite fault model. An asperity is a section of a fault that releases an unusually large amount of stored elastic energy relative to surrounding areas, often because it experiences greater slip or has a higher effective stress drop. In the new representation, these energetic patches are distributed across a composite fault rather than being imposed through a uniform stress-drop assumption. Stress drop is the difference between the shear stress acting on a fault before rupture and the residual stress after slip; it is a key parameter controlling the amplitude and frequency content of radiated waves. By assigning stress drop in a spatially variable way, the model can distinguish concentrated, energetic rupture zones from less active parts of the fault. The approach is designed to preserve the overall scaling of a large earthquake while avoiding the artificial amplification that may arise when every subfault is treated as an equivalent miniature source. It therefore links the statistical efficiency of EXSIM with a more realistic representation of rupture heterogeneity.

The researchers then apply EXSIM in a hybrid configuration with the composite fault model. In practical terms, the calculation retains the stochastic method’s ability to synthesize broadband motions while using the new fault construction to control how source energy is distributed across frequency and space. The simulated waves are compared with recordings from real earthquakes, allowing the method to be evaluated against observed acceleration time histories and their spectral characteristics. The validation includes the 2013 Lushan earthquake, which struck Sichuan Province and is classified as a thrust event, as well as six thrust earthquakes in Japan with moment magnitudes of at least 6.5. Moment magnitude, written Mw, estimates the energy released from the seismic moment and is particularly suitable for large earthquakes because it does not saturate as quickly as some older magnitude scales. Japan provides an especially valuable testing environment because its dense strong-motion networks have recorded numerous earthquakes across varied source, path and site conditions.

Across the case studies, the improved method reportedly produced consistent agreement with observations at both high and low frequencies. The study summarizes this performance with a combined goodness-of-fit value, or CGOF, below 0.35. Goodness-of-fit measures condense differences between simulated and recorded motions into a performance indicator; lower values in this framework signify closer agreement. The result suggests that the revised method avoids the low-frequency overprediction associated with conventional EXSIM while retaining its ability to reproduce higher-frequency shaking. High-frequency waves are especially important for short, stiff structures and for the sharp accelerations that can damage nonstructural components, whereas lower-frequency motions can control the response of taller or more flexible structures. Matching both ranges matters because an earthquake record is not defined by a single peak value. Two sites can experience similar peak acceleration yet impose very different demands on buildings if their spectral energy is concentrated at different periods. A model that captures the full spectrum can therefore provide more useful information for engineering analysis than one that matches only a single intensity measure.

The advance could be particularly important for scenario-based hazard assessments, where researchers simulate earthquakes in places with sparse instrumental records or explore events larger than those observed during the modern monitoring era. Direct observations provide the strongest test of any model, but earthquake catalogues are necessarily incomplete: the largest events are rare, and each fault system has its own geometry, geology and rupture history. Stochastic finite-fault techniques allow scientists to generate suites of plausible motions by varying magnitude, fault dimensions, rupture velocity, stress drop, site conditions and other parameters. The new formulation may broaden the range of thrust-faulting scenarios that can be modelled without systematically exaggerating part of the frequency spectrum. That could help estimate the demand on lifelines, hospitals, transportation corridors and densely populated urban areas near subduction zones. It could also support rapid post-earthquake analysis by enabling researchers to reconstruct likely shaking fields when recordings are unavailable or unevenly distributed.

The method does not eliminate the uncertainties inherent in earthquake simulation. The location and size of asperities cannot generally be known in advance, and stress drop is not a directly observed quantity at every point on a fault. Ground motion is also shaped by three-dimensional geological structures, basin effects, wave scattering and local soil conditions that may not be fully captured in a simplified stochastic framework. The researchers’ validation demonstrates improved performance for the earthquakes and datasets examined, but it does not guarantee equal accuracy for every tectonic setting or magnitude range. In addition, a combined goodness-of-fit score compresses multiple aspects of a waveform into one measure and should be considered alongside spectral shapes, duration, spatial patterns and engineering intensity measures. The value of the work is therefore not that it turns uncertainty into certainty, but that it offers a more physically responsive way to represent a known source-model limitation.

By combining a variable-stress-drop description of asperities with EXSIM, Wanjun Ma and Zhinan Xie provide a route toward more reliable simulations of complex large thrust earthquakes. Their results indicate that the revised framework can reproduce ground motions across the spectrum without sacrificing the speed and flexibility that make stochastic methods attractive. As earthquake engineers increasingly need realistic scenarios for structures and infrastructure exposed to rare but devastating events, such improvements can have consequences far beyond seismological modelling. Better simulations can reveal whether a bridge is vulnerable to short-period pulses, whether a tall building may resonate with long-period basin motion, or how a proposed emergency network might perform under several plausible ruptures. The method is not a crystal ball, but it may give scientists and engineers a clearer, more balanced picture of the shaking that powerful earthquakes can unleash.