The research, led by Guan Chen of the Earth Observatory of Singapore and the National University of Singapore, together with Siau Chen Chian, Michael Beer, and Shengji Wei, addresses a persistent gap in seismic hazard analysis. Engineers can already estimate how strong ground shaking will be at a given distance from an earthquake of a certain size using ground-motion prediction equations. What has been harder to quantify is the impulsive character of near-fault motion — how long the dominant pulse lasts, how large its velocity amplitude is, and how these quantities vary with the seismological setting. The new work links pulse parameters directly to magnitude, distance, and site stiffness, giving hazard analysts a physically grounded way to generate realistic near-fault ground motions for any scenario of interest.

The first technical challenge the team tackled was identification. Not every record close to a fault contains a clear pulse, and distinguishing pulse-like from non-pulse-like ground motions has traditionally relied on wavelet-based classification methods, most famously the approach introduced by Jack Baker in 2007. Many existing identification procedures, however, tend to extract a single type of pulse from a record, which can miss the richer structure of real near-fault motions. Records from large continental earthquakes frequently contain multiple pulses, arising from forward-directivity effects as the rupture front races toward the site, from fling-step associated with permanent tectonic deformation, or from constructive interference of seismic waves with basin geometry. A method that captures only one pulse type risks underrepresenting the destructive potential of the motion.

To overcome this limitation, the researchers developed a novel extraction procedure that integrates a parametric pulse model with the generalized continuous wavelet transform, or GCWT. The parametric model provides a mathematical description of the pulse shape, while the wavelet transform scans the velocity time series across scales and times to locate the energy concentration that corresponds to the pulse. By combining the two, the procedure can capture various types of pulses within a single record, preserving both the diversity and the accuracy of the extracted signals. The authors report that the pulses extracted by their method effectively match the pulse characteristics visible in the original records, a critical validation step before any statistical modeling can proceed.

Armed with this identification tool, the team mined the PEER NGA-West2 database, one of the largest curated collections of strong-motion recordings from shallow crustal earthquakes worldwide. Out of 21,222 records examined, 815 were identified as pulse-like ground motions. This carefully classified dataset forms the empirical backbone of the study. Each pulse-like record was decomposed into its pulse component and a residual component, and the parameters describing the pulse — including its period, amplitude, and the number of significant cycles — were tabulated alongside the corresponding earthquake magnitude, source-to-site distance, and Vs30 for each station.

The second major contribution is a set of empirical regression models that predict the pulse parameters from these three explanatory variables. The functional form of the models was developed with attention to the underlying physics: larger earthquakes produce longer rupture durations and hence longer pulse periods, pulses attenuate with distance as seismic energy spreads and is absorbed, and soft soil sites can amplify and lengthen the dominant velocity cycles relative to stiff rock. Model uncertainty was quantified through residual analysis, following the random-effects regression framework that has become standard in ground-motion modeling since the work of Abrahamson and Youngs in 1992. The proposed models were then validated against existing pulse models and against independent datasets not used in the calibration, providing confidence that the predictions generalize beyond the fitting sample.

A particularly valuable practical outcome of the study is a workflow for synthesizing near-fault broadband ground motions. The approach combines the empirical pulse model with a stochastic representation of the residual component — the high-frequency, incoherent part of the motion that remains after the coherent pulse is removed. Because the pulse and the residual are treated separately, the resulting artificial ground motions retain the distinctive impulsive features that govern structural response while simultaneously matching the spectral velocity and spectral acceleration of benchmark recordings across a broad frequency range. This two-component strategy echoes the spectral representation method in stochastic process simulation and offers a computationally efficient alternative to full physics-based rupture simulations, which remain expensive for routine hazard applications.

The implications for earthquake engineering are substantial. Previous research has demonstrated that near-fault pulses impose large deformation demands on flexible structures: moment-resisting frames subjected to pulse-like records accumulate damage differently than those subjected to ordinary shaking, and offshore wind turbines, tunnels, and seismically isolated buildings all show heightened vulnerability when the impulsive content of the motion is ignored. By providing pulse parameters conditioned on magnitude, distance, and site class, the new model allows analysts to construct scenario-consistent suites of near-fault motions without waiting for the next large earthquake to supply fresh recordings. The authors also note that the framework was informed by recent events, including the 2023 moment magnitude 7.8 Kahramanmaras earthquake in Turkey, whose near-fault recordings have become an important test case for pulse identification and simulation methods.

Accessibility is another notable feature of the work. The processed dataset, together with MATLAB code for identifying the optimal pulse in near-fault ground motions and for generating the empirical pulse model, has been released publicly through Zenodo, while the raw waveforms remain freely downloadable from the PEER NGA-West2 database. This open approach lowers the barrier for practicing engineers and researchers in seismically active regions to incorporate directivity and fling effects into their hazard and risk calculations. The study also acknowledges insightful discussions with Professor Jack W. Baker of Stanford University regarding pulse types and the functional form of the model, and it was supported by the Ministry of Education, Singapore, under the InVEST program for integrating volcano and earthquake science and technology.

For cities that sit directly above or beside active faults — from Los Angeles and Wellington to Istanbul and Kathmandu — the difference between ordinary shaking and a coherent velocity pulse can be the difference between repairable damage and collapse. By transforming decades of strong-motion recordings into a compact, predictive, and openly available model, this research moves the field closer to hazard assessments that reflect not just how hard the ground will shake, but how it will shake. As computational tools for seismic risk analysis grow more sophisticated, models of this kind provide the physically consistent input that engineers need to design structures capable of withstanding the sharpest blows an earthquake can deliver.

Subject of Research: Empirical modeling of near-fault velocity pulses in earthquake ground motion

Article Title: Empirical pulse model for near-fault ground motion considering magnitude, distance, and site characteristics

Article References: Chen, G., Chian, S. C., Beer, M., & Wei, S. (2026). Empirical pulse model for near-fault ground motion considering magnitude, distance, and site characteristics. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02650-2

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02650-2

Keywords: near-fault ground motion, velocity pulse, pulse-like records, NGA-West2, wavelet transform, seismic hazard, ground motion simulation, rupture directivity, fling-step, Vs30, earthquake engineering, Bulletin of Earthquake Engineering

The pulse period, usually written as Tp, governs the dominant timescale of a velocity pulse and wields an outsize influence on how structures respond. Whether a pulse is short and sharp or long and rolling can determine whether a frame sways mildly or lurches into its inelastic range, and the relationship between Tp and a building’s fundamental period can swing structural demands dramatically. Pulse period also feeds directly into ground-motion simulation, pulse-consistent spectral models, and probabilistic seismic hazard analysis, which means that getting Tp wrong can quietly distort downstream estimates of earthquake risk.

Yet despite decades of research, no single method for determining Tp has achieved universal acceptance. Wavelet analysis, peak-point measurements on velocity time histories, analytical pulse-model fitting, and response-spectrum-based techniques all identify the period in different ways. The most popular family of approaches reads Tp directly off a response spectrum, taking the period at the peak of the spectral velocity (the SV method) or at the peak of the product of spectral velocity and spectral displacement (the SV·SD method). These methods are attractively simple, but they hinge on picking one local maximum out of a spectrum that may be jagged, multi-peaked, or contaminated by high-frequency content, and that dependence can produce badly underestimated or overestimated pulse periods for certain records.

The new study takes a fundamentally different route. Instead of hunting for a spectral peak, the researchers define the pulse period using the relative elastic input energy spectrum, EI,r, a quantity from energy-based seismic design that reflects not only the amplitude and frequency content of a ground motion but also its duration and total energy. The rule is elegant: the pulse period is the oscillator period that bisects the area enclosed by the EI,r spectrum and the period axis, leaving exactly half of the spectral energy on each side. Rather than asking where the spectrum peaks, the equal-area criterion asks where the cumulative input energy is balanced, producing a globally representative characteristic period for the record.

To make the integration interval robust, the team devised an adaptive termination criterion. The energy spectrum is computed from 0.01 seconds in steps of 0.01 seconds, and the calculation continues until the spectral tail has decayed sufficiently: the most recent twenty consecutive ordinates must all fall below either one percent of the running spectral peak or an absolute tolerance, with tightly constrained variation between adjacent ordinates. A hard cap of 40 seconds prevents runaway extension for records with persistent long-period content. Sensitivity analyses reported in the paper confirm that the identified pulse periods are essentially insensitive to reasonable variations in these parameters.

Crucially, the authors did not simply assert that their equal-area period is physically meaningful; they tested it. The EI,r-based Tp is prescribed in the analytical pulse model of Mavroeidis and Papageorgiou, while the remaining four pulse parameters, amplitude, phase, number of oscillations, and timing, are optimized with a genetic algorithm that minimizes the squared difference between the extracted pulse and the original velocity time history. Because the pulse period is held fixed during optimization, any agreement between the extracted wavelet and the actual record must flow from the identified period itself. Across the records examined, the optimized pulses reproduced the dominant velocity features and matched the principal range of the 5-percent-damped spectral velocity, supporting the physical reasonableness of the new definition.

The method was then benchmarked against six established techniques: the continuous wavelet transform classification of Baker, a wavelet packet transform approach, variational mode decomposition combined with genetic-algorithm pulse fitting, a signal-only optimization parameterization, and the SV and SV·SD spectrum methods. Across 311 representative pulse-like records, correlation coefficients between the new method and the reference approaches ranged from 0.87 to 0.98, with most above 0.90, indicating strong overall consistency. Detailed case comparisons revealed instructive differences. On some records the SV method seized on a short-period local peak and produced a far too narrow pulse, while the SV·SD method occasionally drifted toward very long periods. The equal-area energy criterion typically landed between the two, capturing the main velocity-pulse scale without the extremes of either peak-chasing approach.

To demonstrate engineering relevance, the researchers classified pulse-like motions into short-, medium-, and long-period groups and assessed the collapse fragility of a four-story, three-bay reinforced concrete frame modeled in OpenSees. The fiber-section model, validated against shake-table test data from the European Laboratory for Structural Assessment, reproduced measured drift profiles, displacement histories, and story shears with satisfactory accuracy. Cloud analyses using the geometric-mean spectral acceleration as intensity measure and the maximum inter-story drift ratio as demand parameter showed that pulse period matters enormously: records with pulses longer than two seconds produced markedly higher collapse fragility, while the short-period group shifted fragility curves toward higher ground-motion intensities and thus more favorable collapse performance. Comparable trends emerged when records were grouped using the CWT and SV·SD methods, reinforcing the conclusion that the observed pulse-period effect is genuine rather than an artifact of one identification technique.

The authors are careful to position the new criterion as a complement rather than a replacement. Different identification methods emphasize different characteristics of complex pulse-like motions, and no single definition suits every record. What the equal-area EI,r approach offers is a measure that is less hostage to a particular local spectral peak, is grounded in the cumulative distribution of input energy that energy-based design frameworks already prize, and carries a clear physical interpretation as an energy-balanced characteristic period. The study also confirms that pulse period is a critical parameter for interpreting the collapse potential of near-fault motions and for building pulse-period-dependent fragility models.

Limitations remain, and the authors flag them candidly. The structural application covered only a four-story bare reinforced concrete frame, so the quantitative fragility results depend on that building’s dynamic properties and its 0.711-second fundamental period. Future work, they write, should extend the framework to structures with different heights, periods, and response mechanisms to probe the interaction between pulse period and structural period more deeply. Even so, the method arrives at a moment when near-fault seismic risk assessment increasingly demands pulse-aware tools, and it provides engineers and hazard analysts with a computationally light, physically grounded way to extract one of the most consequential numbers in earthquake engineering from any velocity record.

Subject of Research: An energy-based method for identifying velocity-pulse periods of near-fault ground motions

Article Title: An efficient energy-based approach for identification of velocity-pulse periods

Article References: Gao, Q., Chang, Z., Monti, G., & Mollaioli, F. (2026). An efficient energy-based approach for identification of velocity-pulse periods. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02677-5

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02677-5

Keywords: pulse period, near-fault ground motions, input energy spectrum, velocity pulse, seismic fragility, reinforced concrete frame, genetic algorithm, M&P wavelet, probabilistic seismic hazard analysis, earthquake engineering, response spectrum, collapse vulnerability