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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding the Seismic Ground Motion Spatial Variability Using Network Analysis Community Detection

This project is to explore ground motion spatial distribution using a new approach graph-based network analysis. In this study, we combine a large-N seismic array and graph analytics to explore spatial variability and correlation at a local scale using small local and regional earthquakes. In this method, each seismic station is modeled as a node and the similarities of the waveforms that represent ground motions between two stations are modeled as edges. By analyzing this graph network using the similarity matrices and community detection algorithm, we can group the stations spatially with similar patterns. A random forest algorithm is used to reveal the important features that affect the spatial grouping. The result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements of seismic wavefields.

58 GEOSCIENCES↗
DOE OSTI2022

Report on the LLNL Global Seismic Waveform Tomography Modeling Project

Earth models have important applications for seismic event monitoring including the location and characterization of potential underground nuclear tests. Seismic waves generated by earthquakes and man-made events are altered by variations in Earth’s subsurface properties; and these distortions effect the arrival times of energy packets (phases) and obscure the characteristics of the original energy source (event mechanism). Three-dimensional models of Earth’s seismic properties can help explain and predict the distortions to the seismic wavefield and reveal the properties of a source that generated the waves, including the event location using the model-predicted timing of the phases and source mechanism using the modelpredicted waveform characteristics.

58 GEOSCIENCES↗
DOE OSTI2022

Summary of NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training

The NNSA Seismic Cooperation Program (SCP) sponsored Stephen Myers (LLNL), Michael Begnaud (LANL), Brian Young (SNL) and Istvan Bondar (Research Center for Astronomy and Earth Sciences, Hungary) to serve as a presenters/trainers at the “NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training” September 4-8 2022 in Muscat, Oman (See Appendix A for the agenda). The workshop and training (workshop from here forward) was organized by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) Provisional Technical Secretariat (PTS). The first half of the week was devoted to NDC workshop activities, and the second half was devoted to RSTT training. Fifty-five participants from 27 countries and the CTBTO-PTS attended the 5-day workshop (See Appendix B for list of participants and countries of origin). Presentations from the PTS described the International Monitoring System (IMS), International Data Centre (IDC) products, and metrics of regional data utilization. Contributed presentations from each country’s scientists included descriptions of regional and national networks, methods of data analysis, and needs for material and technical assistance. Training included an overview of the RSTT method and instruction on how to locate seismic events with the iLoc program, which utilizes RSTT travel times to reduce bias in event location estimates. Methods of seismic tomography and the need for a high-quality tomographic set, including seismological “ground truth”, were emphasized. Seismological “ground truth” or “GT” is a term that has come to mean both events with known location and events with well-characterized locations that are estimated using seismological data, typically with epicenter accuracy of 5 km or better. Notably, the instructional platform has migrated from UNIX shell scripts to Jupyter Notebooks. Jupyter Notebooks have the advantage being more visually intuitive, including display of graphics within the notebook. Each notebook includes every processing step that participants need to reproduce the entire exercise.

58 GEOSCIENCES↗
DOE OSTI2023

Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations

Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.

58 GEOSCIENCES↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Seismic DAS Observations of a large underground chemical explosion in dry tuff

On 18 October 2023 a 16.3-ton TNT equivalent chemical explosion was detonated underground at the Nevada National Security Site, generating a seismic event (Meyers et al., 2024). The associated seismic wavefield was measured on a Distributed Acoustic Sensing (DAS) array with slant range distances from 27 m – 1123 m. The first arriving phase traveled at an apparent velocity of about 2640 m s -1 from 27 m to 420 m slant range and about 2470 m s -1 from 505 m to 1123 m slant range according to the first arrival moveouts on the DAS data. The first arrival from the explosion temporarily saturated the cable from a slant range of 27 m – 186 m and 0.009 s to 0.084 s post detonation. From 186 m slant range to 420 m slant range, peak strain rates of 5.6 x 10 6 nm m -1 s -1 were observed for the first arrival phase. For the first arrival from 505 m slant range to 1123 m slant range, peak strain rates reduced to 8.0 x 10 4 nm m -1 s -1 . A comparison of the scaled accelerations computed from DAS, the geophone pairs, and the measurements of co-located accelerometer pairs show common agreement at the scaled ranges of the single point sensors. This study adds to the body of work reporting near-source DAS observations of the seismic wavefields generated by underground chemical explosions. These results indicate that near-source DAS observations can refine interpretations of phase identification from single-point sensor observations. Phase identification could be one mechanism that contributes scatter to single point seismic measurements which would confound the performance of empirical relationships for small explosions. Removing that mechanism may therefore reduce interstation variability and increase empirical relationship performance for small explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Seismic DAS observations of a large underground chemical explosion in dry tuff

On 18 October 2023 a 16.3-ton TNT equivalent chemical explosion was detonated underground at the Nevada National Security Site, generating a seismic event with a magnitude of 1.7 (Meyers et al., 2024). The associated seismic wavefield was measured on a Distributed Acoustic Sensing (DAS) array with slant range distances from 27 m – 1123 m. The first arriving phase traveled at an apparent velocity of about 2640 m s -1 from 27 m to 420 m slant range and about 2470 m s -1 from 505 m to 1123 m slant range according to the first arrival moveouts on the DAS data. The first arrival from the explosion temporarily saturated the cable from a slant range of 27 m – 186 m and 0.009 s to 0.084 s post detonation. From 186 m slant range to 420 m slant range, peak strain rates of 5.6 x 10 6 nm m -1 s -1 were observed for the first arrival phase. For the first arrival from 505 m slant range to 1123 m slant range, peak strain rates reduced to 8.0 x 104 nm m -1 s -1 . A comparison of the scaled accelerations computed from DAS, the geophone pairs, and the measurements of co-located accelerometer pairs show common agreement at the scaled ranges of the single point sensors. This study adds to the body of work reporting near-source DAS observations of the seismic wavefields generated by underground chemical explosions. These results indicate that near-source DAS observations can refine interpretations of phase identification from single-point sensor observations. Phase identification could be one mechanism that contributes scatter to single point seismic measurements which would confound the performance of empirical relationships for small explosions. Removing that mechanism may therefore reduce interstation variability and increase empirical relationship performance for small explosions.

58 GEOSCIENCES↗
DOE OSTI2026

The Influence of Shallow Subsurface Properties on Particle Motion in Acoustic-Seismic Coupling

Atmospheric acoustic waves transmit energy into the solid Earth through air-to-ground coupling. These waves are recorded by seismic sensors and provide insight into both atmospheric phenomena and subsurface properties. Interpreting these signals is often challenging because they are modulated by subsurface structure and the incidence angle of the acoustic wave. This study examines acoustic--seismic coupling generated by the 2012 Camp Minden Explosion, which was recorded by hundreds of seismoacoustic stations. We apply a novel technique to quantify the seismic particle motion, model coupled waves with a propagator matrix approach, and apply a Bayesian inversion to infer properties of the shallow subsurface. Our analysis reveals that prograde motion is widespread and focused in low shear-wave velocity regions, such as the Mississippi Embayment, and retrograde motion is more common in higher shear-velocity areas. Inversion results at some stations produce plausible subsurface models with strong waveform fits, while inversions at other sites are less successful. These results indicate prograde particle motion in air-to-ground coupled waves is more prevalent than previously recognized and may serve as a diagnostic for shallow velocity structure. Our comprehensive modeling and inversion framework provides a potential method to extract layered near-surface properties from acoustic-seismic coupling observations.

58 GEOSCIENCES↗
DOE OSTI2025

Assessing Seismic Risk for CO2 Geologic Storage: Comparative Analysis of the Delaware Basin and Basin and Range Province Projects

ABSTRACT: Effective management of induced seismicity is critical for safe and sustainable CO2 storage. This study evaluates fault slippage risks in the Delaware Basin (Texas) and Basin and Range Province (Utah), integrating geological, operational, and geomechanical parameters to assess fault stability and seismic hazard mitigation. In the Delaware Basin, two sites were analyzed under an injection rate of 20,000 bbl/day over 25 years. One site showed low fault slip risk, while the other exhibited higher reactivation potential due to proximity to critically stressed faults. Sensitivity analysis revealed that increased pore pressure significantly heightened slip potential, highlighting the necessity of precise pressure control and real-time monitoring. In the Basin and Range Province, fault stability was evaluated at Neck of the Desert, Escalante Desert, Parowan, and Beaver sites under injection rates of 8,750 bbl/day per site over 30 years. Minimal fault slip risk was observed at Neck of the Desert and Escalante Desert sites, whereas Parowan and Beaver sites exhibited elevated slip potential due to semi-critically stressed faults sensitive to modest pore pressure increases. The findings demonstrate that fault slippage analysis, combined with sensitivity analysis of pore pressure and friction coefficients, is essential for understanding seismic risks. Continuous monitoring, adaptive injection management, and rigorous geomechanical analysis are key strategies for minimizing induced seismicity in CO2 sequestration projects.

58 GEOSCIENCES↗
DOE OSTI2020

Subtask 1.5 – CO2 Injection Monitoring with an Optimized Scalable, Automated, Semipermanent Seismic Array

The scalable, automated, semipermanent seismic array (SASSA) method is a flexible and relatively cost-effective surface geophysical method for regular time-lapse monitoring of the movement of injected carbon dioxide (CO2) in a reservoir for CO2 enhanced oil recovery (EOR) or geologic CO2 storage operations. It has the advantages of a low-environmental-footprint while monitoring regions of a reservoir from the surface without the need for a regular grid distribution of receivers. Automated data collection is possible. As only time-lapse amplitude changes at the reservoir level due to CO2 movement within the reservoir are monitored, the turnaround time to deliver results from the SASSA method can be short, without the need for long, time-consuming data-processing workflows. As data is collected and processed, incremental information can be provided to the field operator. The Energy & Environmental Research Center (EERC) conducted a SASSA field test from September 2018 to November 2020 in a portion of the Bell Creek Field in Montana, which implemented new CO2 EOR field activities during the study period. Lessons learned from a proof-of-concept study were incorporated to improve the data quality of the SASSA method and demonstrate the viability of the technology. The EERC implemented several enhancements to improve data quality, including 1) an iterative survey design, which allowed placing the receivers in strategic locations where the movement of the CO2 in the reservoir could be tracked with minimum interference by the cultural noise in the study area; 2) the use of powerful seismic sources in the form of surface orbital vibrators, and 3) data acquisition during optimal periods. History-matched reservoir simulation was performed to predict gas saturation and pressure response induced by CO2 injection in the study area. The results were compared with the SASSA-measured responses to CO2 injection as a partial validation technique. A match between the two methods was observed for most of the SASSA points predicted to have intersected a CO2 saturation change. The validated results provide confidence that the SASSA method can be used independently as a CO2 saturation monitoring technique. As data are collected and processed, incremental information can be provided to the field operator. The critical components of the SASSA workflow for a successful application of the method are the following: Iterative survey design with information about CO2 injection activities from the oilfield operator. A detailed CO2 injection plan is the key driver to select the strategic monitoring location of the SASSA sensors. After this information is incorporated in the initial distribution of sources and receivers in the study area, high-resolution satellite images are used to identify ground locations not affected by cultural noise sources, such as power lines, pipelines/flow lines, or roadways. In the next iteration of the survey design, a scouting trip to the study area is needed to understand more details of the noise sources identified in the previous step and the intensity of the field activities that can also generate noise during the monitoring. Integrating the information from the scouting trip into the survey design to select the optimum source and receiver locations is the final step. Noise attenuation. The variety of noise types during seismic monitoring of an oil field is enormous. Tailored noise characterization and processing at a node-by-node level can enhance the performance and sensitivity of the SASSA technique. Future advancements that could improve the efficiency and application of the SASSA technology include: Gaining a better understanding of the noise field produced by the seismic source to aid the choice of receiver location. Surface noise from the source can overwhelm the small signal changes due to CO2 that the SASSA method measures. Improved data-processing workflow to automatically analyze and adapt to dynamic noise conditions associated with industrial settings. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE- FE0024233.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Seismic signal augmentation to improve generalization of deep neural networks

Deep learning has emerged as an effective approach for seismic data processing in general, and for earthquake monitoring in particular. The ability of deep learning models to generalize beyond the training and validation data is important for comprehensive earthquake monitoring; this ability furthermore depends on the availability of a sufficiently large and complete training dataset. However, this requirement can prove challenging to meet due to significant effort and time for data collection and labeling. Data augmentation provides an efficient and effective approach for increasing the dimension of training samples and improving generalization to unseen samples. In this paper, we present augmentation methods appropriate for seismic waveforms and demonstrate their ability to reduce bias and increase performance. Furthermore, these augmentation methods can be applied to a wide range of deep learning applications designed for seismic data.

58 GEOSCIENCES↗
DOE OSTI2023

Active‐Source Seismic Imaging of Fault Re‐Activation and Leakage: An Injection Experiment at the Mt Terri Rock Laboratory, Switzerland

Abstract We conducted a time‐lapse seismic experiment utilizing automated active seismic source and sensor arrays to monitor a reactivated fault within the Opalinus clay formation at the Mont Terri Rock Laboratory (Switzerland), an analog caprock for geologic carbon storage. A series of six brine injections were conducted into the so‐called Main Fault to reactivate it. Seismic instrumentation in five monitoring boreholes on either side of the fault was used to continuously probe changes in P‐wave travel‐times associated with fault displacement and leakage. We performed time‐lapse travel‐time tomography on five hundred sequential data sets; this revealed a zone of decreased P‐wave velocity, up to 16 m/s, during each injection cycle, followed by a velocity increase during shut‐in. These results demonstrate varying elastic property perturbations, both spatially and temporally, along the fault plane during reactivation. We then interpreted these velocity changes in terms of fault dilation induced by pressurized fluids along the fault.

58 GEOSCIENCES↗
DOE OSTI2023

Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment

Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding MATLAB scripts that import and plot the data, as well as use it to calculate shear and normal stress. Experiments are performed on 2.5-3 inch long granitoid cores from the Utah FORGE EGS demonstration site, containing a single inclined fracture with small-scale roughness added to the fracture surface. The raw data included here were recorded from an aluminum triaxial pressure vessel (TEMCO) configured with three independent servo-controlled pumps, with distilled water used as the working fluid. The pumps control confining pressure, upstream pore pressure, and axial pressure, with each pump connected to a LabView interface to record applied pressures, cumulative injected water volumes, and pump flow rates. The downstream outlet from the fracture is closed to allow pressurization, which is measured by an external pressure transducer. A linear variable differential transformer (LVDT) attached to the axial piston measures axial displacement, from which we calculate shear displacement along the fracture. Additionally, P-wave transducers are used to record acoustic signatures, where acoustic emission events and maximum amplitudes are compared against seismic moment and shear slip velocity. Fluid injection rates range between 0.05 mL/min, 0.25 mL/min, and 0.75 mL/min for each experiment. Along-fault pressure distributions are progressively less uniform as injection rates increase, representing a switch from steady-state to transient conditions. Triggered shear displacement is used as a proxy for seismic moment and is indexed against cumulative injection volume and rate. Each experiment is performed under constant shear stress conditions, and the sample is fully saturated with DI water. Axial and confining stresses are applied to 3 MPa through pressure-stepping in 500 kPa increments. The pore pressure is held constant at 200 kPa prior to initiating the experiment, and initial axial displacement is recorded. The axial stress is then increased to initiate shear mobilization during the loading phase (run-in) until a peak steady state is achieved. The initial shear stress is reduced to approximately 80% of the peak shear stress by decreasing the axial stress, then held constant for the duration of each experiment.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

EGS Collab Experiment 2: Continuous Active Source Seismic Monitoring (CASSM)

The dataset contains continuous active-source seismic monitoring (CASSM) data collected during EGS Collab Experiment 2, conducted from February to September 2022 at the Sanford Underground Research Facility in Lead, South Dakota. This experiment aimed to investigate enhanced geothermal systems through high-pressure fluid injections at depths of 1200-1500 meters. The seismic monitoring system included 16 three-component piezoelectric accelerometers and 24 hydrophones installed in boreholes around the injection zones, recording signals from piezoelectric seismic sources. Data were acquired using both continuous and triggered recording systems, with sampling rates of up to 100 kHz. The raw data are organized by timestamps and stored in .dat format, with accompanying log files. Calibration certificates for selected accelerometers are provided to aid in correcting sensor responses, though users are advised to consider possible effects of enclosures and installation on sensor performance. Users are strongly advised to consult the accompanying report, which outlines the experimental setup, data acquisition, sensor specifications, and recording systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

The Seismic Signature of a High-Energy Density Physics Laboratory and Its Potential for Measuring Time-Dependent Velocity Structure

The Z Machine at Sandia National Laboratories is a pulsed power facility for high-energy density physics experiments that can shock materials to extreme temperatures and pressures through a focused energy release of up to ~ 25 MJ in < 100 nanoseconds. It has been in operation for more than two decades and conducts up to ~ 100 experiments, or “shots,” per year. Based on a set of 74 known shot times from 2018, we determined that Z Machine shots produce detectable ~ 3–17 Hz ground motion 12 km away at the Albuquerque Seismological Laboratory, New Mexico (ANMO), borehole seismograph, with peak signal at ~ 7 Hz. The known shot waveforms were used to create a three-component template, leading to the detection of 2339 Z Machine shots since 1998 through single-station cross-correlation. Local seismic magnitude estimates range from local magnitude (M L ) —2 to —1.3 and indicate that only a small fraction of the shot energy is transmitted by seismic phases observable at 12 km distance. The most recent major facility renovation, which was intended to decrease mechanical dissipation, is associated with an abrupt decrease in observed seismic amplitudes at ANMO despite stable maximum shot energy. The highly repetitive impulsive sources are well suited to coda-wave interferometry to investigate time-dependent velocity structures. Relative velocity variations (dv/v) show an annual cycle with amplitude of ~ 0.2%. Local minima are observed in the late spring, and dv/v increases through the summer monsoon rainfall, possibly reflecting patchy saturation as rainfall infiltrates near the eastern edge of the Albuquerque basin. Importantly, the cumulative results demonstrate that forensic seismology can provide insight into long-term operation of facilities such as pulsed-power laboratories, and that their recurring signals may be valuable for studies of time-dependent structure

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PySolate : A Python‐Based Thresholding Tool to Denoise or Designal Seismic Waveforms Based on the Continuous Wavelet Transform

PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.

Asia↗
DOE OSTI2023

Seismic Monitoring at the Farnsworth CO2-EOR Field Using Time-Lapse Elastic-Waveform Inversion of 3D-3C VSP Data

During the Development Phase of the U.S. Southwest Regional Partnership on Carbon Sequestration, supercritical CO2 was continuously injected into the deep oil-bearing Morrow B formation of the Farnsworth Unit in Texas for Enhanced Oil Recovery (EOR). The project injected approximately 94 kilotons of CO2 to study geologic carbon storage during CO2-EOR. A three-dimensional (3D) surface seismic dataset was acquired in 2013 to characterize the subsurface structures of the Farnsworth site. Following this data acquisition, the baseline and three time-lapse three-dimensional three-component (3D-3C) vertical seismic profiling (VSP) data were acquired at a narrower surface area surrounding the CO2 injection and oil/gas production wells between 2014 and 2017 for monitoring CO2 injection and migration. With these VSP datasets, we inverted for subsurface velocity models to quantitatively monitor the CO2 plume within the Morrow B formation. We first built 1D initial P-wave (Vp) and S-wave (Vs) velocity models by upscaling the sonic logs. We improved the deep region of the Vp and Vs models by incorporating the deep part of a migration velocity model derived from the 3D surface seismic data. We improved the shallow region of 3D Vp and Vs models using 3D traveltime tomography of first arrivals of VSP downgoing waves. We further improved the 3D baseline velocity models using elastic-waveform inversion (EWI) of the 3D baseline VSP upgoing data. Our advanced EWI method employs alternative tomographic and conventional gradients and total-variation-based regularization to ensure the high-fidelity updates of the 3D baseline Vp and Vs models. We then sequentially applied our 3D EWI method to the three time-lapse datasets to invert for spatiotemporal changes of Vp and Vs in the reservoir. Our inversion results reveal the volumetric changes of the time-lapse Vp and Vs models and show the evolution of the CO2 plume from the CO2 injection well to the oil/gas production wells.

42 ENGINEERING↗
DOE OSTI2021

Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings

Seismic fragility functions can be evaluated using the cloud analysis method with linear regression which makes three fundamental assumptions about the relation between structural response and seismic intensity: log-linear median relationship, constant standard deviation, and Gaussian distributed errors. While cloud analysis with linear regression is a popular method, the degree to which these individual and compounded assumptions affect the fragility and the risk of mid-rise buildings needs to be systematically studied. This paper conducts such a study considering three building archetypes that make up a bulk of the building stock: RC moment frame, steel moment frame, and wood shear wall. Gaussian kernel methods are employed to capture the data-driven variations in the median structural response and standard deviation and the distributions of residuals with the intensity level. With reference to the Gaussian kernels approach, it is found that while the linear regression assumptions may not affect the fragility functions of lower damage states, this conclusion does not hold for the higher damage states (such as the Complete state). In addition, the effects of linear regression assumptions on the seismic risk are evaluated. For predicting the demand hazard, it is found that the linear regression assumptions can impact the computed risk for larger structural response values. However, for predicting the loss hazard with downtime as the decision variable, linear regression can be considered adequate for all practical purposes.

58 GEOSCIENCES↗