Geology topics

Eric M. Thompson

Publications and source records attributed to Eric M. Thompson.

At least 91 records · Page 5Linked to original sources

USGS2017

An updated geospatial liquefaction model for global application

We present an updated geospatial approach to estimation of earthquake-induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope-derived VS30, compound topographic index, and magnitude-adjusted peak ground acceleration from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across 6 countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving model performance. While we test 14 geospatial proxies for soil density and soil saturation, the most promising geospatial parameters are slope-derived VS30, modeled water table depth, distance to coast, distance to river, distance to closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than peak ground acceleration (PGA) as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the curve under the Receiver Operating Characteristic (ROC) curve (AUC) and the Brier score. The best-performing model in a coastal setting uses distance to coast but is problematic for regions away from the coast. The second best model, using PGV, VS30, water table depth, distance to closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.

Christchurch, Kobe↗

USGS2017

Characterizing the Kathmandu Valley sediment response through strong motion recordings of the 2015 Gorkha earthquake sequence

We analyze strong motion records and high-rate GPS measurements of the M 7.8 Gorkha mainshock, M 7.3 Dolakha, and two moderate aftershock events recorded at four stations on the Kathmandu basin sediments, and one on rock-outcrop. Recordings on soil from all four events show systematic amplification relative to the rock site at multiple frequencies in the 0.1–2.5 Hz frequency range, and de-amplification of higher frequencies ( >2.5–10 Hz). The soil-to-rock amplification ratios for the M 7.8 and M 7.3 events have lower amplitude and frequency peaks relative to the ratios of the two moderate events, effects that could be suggestive of nonlinear site response. Further, comparisons to ground motion prediction equations show that 1) both soil and rock mainshock recordings were severely depleted of high frequencies, and 2) the depletion at high frequencies is not present in the aftershocks. These observations indicate that the high frequency deamplification is additionally related to characteristics of the source that are not captured by simplified ground motion prediction equations, and allude to seismic hazard analysis models being revised – possibly by treating isolated high frequency radiation sources separately from long period components to capture large magnitude near-source events such as the 2015 Gorkha mainshock.

Kathmandu Valley↗

USGS2017

Integrating landslide and liquefaction hazard and loss estimates with existing USGS real-time earthquake information products

The U.S. Geological Survey (USGS) has made significant progress toward the rapid estimation of shaking and shakingrelated losses through their Did You Feel It? (DYFI), ShakeMap, ShakeCast, and PAGER products. However, quantitative estimates of the extent and severity of secondary hazards (e.g., landsliding, liquefaction) are not currently included in scenarios and real-time post-earthquake products despite their significant contributions to hazard and losses for many events worldwide. We are currently running parallel global statistical models for landslides and liquefaction developed with our collaborators in testing mode, but much work remains in order to operationalize these systems. We are expanding our efforts in this area by not only improving the existing statistical models, but also by (1) exploring more sophisticated, physics-based models where feasible; (2) incorporating uncertainties; and (3) identifying and undertaking research and product development to provide useful landslide and liquefaction estimates and their uncertainties. Although our existing models use standard predictor variables that are accessible globally or regionally, including peak ground motions, topographic slope, and distance to water bodies, we continue to explore readily available proxies for rock and soil strength as well as other susceptibility terms. This work is based on the foundation of an expanding, openly available, case-history database we are compiling along with historical ShakeMaps for each event. The expected outcome of our efforts is a robust set of real-time secondary hazards products that meet the needs of a wide variety of earthquake information users. We describe the available datasets and models, developments currently underway, and anticipated products.

Conference Paper↗

USGS2016

USGS approach to real-time estimation of earthquake-triggered ground failure - Results of 2015 workshop

The U.S. Geological Survey (USGS) Earthquake Hazards and Landslide Hazards Programs are developing plans to add quantitative hazard assessments of earthquake-triggered landsliding and liquefaction to existing real-time earthquake products (ShakeMap, ShakeCast, PAGER) using open and readily available methodologies and products. To date, prototype global statistical models have been developed and are being refined, improved, and tested. These models are a good foundation, but much work remains to achieve robust and defensible models that meet the needs of end users. In order to establish an implementation plan and identify research priorities, the USGS convened a workshop in Golden, Colorado, in October 2015. This document summarizes current (as of early 2016) capabilities, research and operational priorities, and plans for further studies that were established at this workshop. Specific priorities established during the meeting include (1) developing a suite of alternative models; (2) making use of higher resolution and higher quality data where possible; (3) incorporating newer global and regional datasets and inventories; (4) reducing barriers to accessing inventory datasets; (5) developing methods for using inconsistent or incomplete datasets in aggregate; (6) developing standardized model testing and evaluation methods; (7) improving ShakeMap shaking estimates, particularly as relevant to ground failure, such as including topographic amplification and accounting for spatial variability; and (8) developing vulnerability functions for loss estimates.

Open-File Report↗

USGS2016

Uncertainty in V s30 -based site response

Methods that account for site response range in complexity from simple linear categorical adjustment factors to sophisticated nonlinear constitutive models. Seismic‐hazard analysis usually relies on ground‐motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time‐averaged shear‐wave velocity to 30 m ( V S 30 ) and basin depth parameters. Because V S 30 is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which V S 30 has been measured to address effects of V S 30 proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground‐motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured V S 30 values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.

Bulletin of the Seismological Society of America↗

USGS2016

Soil amplification with a strong impedance contrast: Boston, Massachusetts

In this study, we evaluate the effect of strong sediment/bedrock impedance contrasts on soil amplification in Boston, Massachusetts, for typical sites along the Charles and Mystic Rivers. These sites can be characterized by artificial fill overlying marine sediments overlying glacial till and bedrock, where the depth to bedrock ranges from 20 to 80 m. The marine sediments generally consist of organic silts, sand, and Boston Blue Clay. We chose these sites because they represent typical foundation conditions in the city of Boston, and the soil conditions are similar to other high impedance contrast environments. The sediment/bedrock interface in this region results in an impedance ratio on the order of ten, which in turn results in a significant amplification of the ground motion. Using stratigraphic information derived from numerous boreholes across the region paired with geologic and geomorphologic constraints, we develop a depth-to-bedrock model for the greater Boston region. Using shear-wave velocity profiles from 30 locations, we develop average velocity profiles for sites mapped as artificial fill, glaciofluvial deposits, and bedrock. By pairing the depth-to-bedrock model with the surficial geology and the average shear-wave velocity profiles, we can predict soil amplification in Boston. We compare linear and equivalent-linear site response predictions for a soil layer of varying thickness over bedrock, and assess the effects of varying the bedrock shear-wave velocity (V Sb ) and quality factor (Q). In a moderate seismicity region like Boston, many earthquakes will result in ground motions that can be modeled with linear site response methods. We also assess the effect of bedrock depth on soil amplification for a generic soil profile in artificial fill, using both linear and equivalent-linear site response models. Finally, we assess the accuracy of the model results by comparing the predicted (linear site response) and observed site response at the Northeastern University (NEU) vertical seismometer array during the 2011 M 5.8 Mineral, Virginia, earthquake. Site response at the NEU vertical array results in amplification on the order of 10 times at a period between 0.7-0.8 s. The results from this study provide evidence that the mean short-period and mean intermediate-period amplification used in design codes (i.e., from the F a and F v site coefficients) may underpredict soil amplification in strong impedance contrast environments such as Boston.

Massachusetts↗

USGS2016

Compilation of V S 30 Data for the United States

V S 30 , the time-averaged shear-wave velocity ( V S ) to a depth of 30 meters, is a key index adopted by the earthquake engineering community to account for seismic site conditions. V S 30 is typically based on geophysical measurements of V S derived from invasive and noninvasive techniques at sites of interest. Owing to cost considerations, as well as logistical and environmental concerns, V S 30 data are sparse or not readily available for most areas. Where data are available, V S 30 values are often assembled in assorted formats that are accessible from disparate and (or) impermanent Web sites. To help remedy this situation, we compiled V S 30 measurements obtained by studies funded by the U.S. Geological Survey (USGS) and other governmental agencies. Thus far, we have compiled V S 30 values for 2,997 sites in the United States, along with metadata for each measurement from government-sponsored reports, Web sites, and scientific and engineering journals. Most of the data in our V S 30 compilation originated from publications directly reporting the work of field investigators. A small subset (less than 20 percent) of V S 30 values was previously compiled by the USGS and other research institutions. Whenever possible, V S 30 originating from these earlier compilations were crosschecked against published reports. Both downhole and surface-based V S 30 estimates are represented in our V S 30 compilation. Most of the V S 30 data are for sites in the western contiguous United States (2,141 sites), whereas 786 V S 30 values are for sites in the Central and Eastern United States; 70 values are for sites in other parts of the United States, including Alaska (15 sites), Hawaii (30 sites), and Puerto Rico (25 sites). An interactive map is hosted on the primary USGS Web site for accessing V S 30 data ( https://earthquake.usgs.gov/data/vs30/us/ ).

Data Series↗

USGS2015

Geotechnical effects of the 2015 magnitude 7.8 Gorkha, Nepal, earthquake and aftershocks

This article summarizes the geotechnical effects of the 25 April 2015 M 7.8 Gorkha, Nepal, earthquake and aftershocks, as documented by a reconnaissance team that undertook a broad engineering and scientific assessment of the damage and collected perishable data for future analysis. Brief descriptions are provided of ground shaking, surface fault rupture, landsliding, soil failure, and infrastructure performance. The goal of this reconnaissance effort, led by Geotechnical Extreme Events Reconnaissance, is to learn from earthquakes and mitigate hazards in future earthquakes.

Gorkha↗

USGS2015

Revisions to some parameters used in stochastic-method simulations of ground motion

The stochastic method of ground‐motion simulation specifies the amplitude spectrum as a function of magnitude ( M ) and distance ( R ). The manner in which the amplitude spectrum varies with M and R depends on physical‐based parameters that are often constrained by recorded motions for a particular region (e.g., stress parameter, geometrical spreading, quality factor, and crustal amplifications), which we refer to as the seismological model. The remaining ingredient for the stochastic method is the ground‐motion duration. Although the duration obviously affects the character of the ground motion in the time domain, it also significantly affects the response of a single‐degree‐of‐freedom oscillator. Recently published updates to the stochastic method include a new generalized double‐corner‐frequency source model, a new finite‐fault correction, a new parameterization of duration, and a new duration model for active crustal regions. In this article, we augment these updates with a new crustal amplification model and a new duration model for stable continental regions. Random‐vibration theory (RVT) provides a computationally efficient method to compute the peak oscillator response directly from the ground‐motion amplitude spectrum and duration. Because the correction factor used to account for the nonstationarity of the ground motion depends on the ground‐motion amplitude spectrum and duration, we also present new RVT correction factors for both active and stable regions.

Bulletin of the Seismological Society of America↗

USGS2014

Path durations for use in the stochastic‐method simulation of ground motions

The stochastic method of ground‐motion simulation assumes that the energy in a target spectrum is spread over a duration D T . D T is generally decomposed into the duration due to source effects (D S ) and to path effects (D P ). For the most commonly used source, seismological theory directly relates D S to the source corner frequency, accounting for the magnitude scaling of D T . In contrast, D P is related to propagation effects that are more difficult to represent by analytic equations based on the physics of the process. We are primarily motivated to revisit D T because the function currently employed by many implementations of the stochastic method for active tectonic regions underpredicts observed durations, leading to an overprediction of ground motions for a given target spectrum. Further, there is some inconsistency in the literature regarding which empirical duration corresponds to D T . Thus, we begin by clarifying the relationship between empirical durations and D T as used in the first author’s implementation of the stochastic method, and then we develop a new D P relationship. The new D P function gives significantly longer durations than in the previous D P function, but the relative contribution of D P to D T still diminishes with increasing magnitude. Thus, this correction is more important for small events or subfaults of larger events modeled with the stochastic finite‐fault method.

Bulletin of the Seismological Society of America↗

USGS2014

Predicting the spatial extent of liquefaction from geospatial and earthquake specific parameters

T he spatially extensive damage from the 2010-2011 Christchurch, New Zealand earthquake events are a reminder of the need for liquefaction hazard maps for anticipating damage from future earthquakes. Liquefaction hazard mapping as traditionally relied on detailed geologic mapping and expensive site studies. These traditional techniques are difficult to apply globally for rapid response or loss estimation. We have developed a logistic regression model to predict the probability of liquefaction occurrence in coastal sedimentary areas as a function of simple and globally available geospatial features (e.g., derived from digital elevation models) and standard earthquake-specific intensity data (e.g., peak ground acceleration). Some of the geospatial explanatory variables that we consider are taken from the hydrology community, which has a long tradition of using remotely sensed data as proxies for subsurface parameters. As a result of using high resolution, remotely-sensed, and spatially continuous data as a proxy for important subsurface parameters such as soil density and soil saturation, and by using a probabilistic modeling framework, our liquefaction model inherently includes the natural spatial variability of liquefaction occurrence and provides an estimate of spatial extent of liquefaction for a given earthquake. To provide a quantitative check on how the predicted probabilities relate to spatial extent of liquefaction, we report the frequency of observed liquefaction features within a range of predicted probabilities. The percentage of liquefaction is the areal extent of observed liquefaction within a given probability contour. The regional model and the results show that there is a strong relationship between the predicted probability and the observed percentage of liquefaction. Visual inspection of the probability contours for each event also indicates that the pattern of liquefaction is well represented by the model.

Conference Paper↗

USGS2014

Development of a globally applicable model for near real-time prediction of seismically induced landslides

Substantial effort has been invested to understand where seismically induced landslides may occur in the future, as they are a costly and frequently fatal threat in mountainous regions. The goal of this work is to develop a statistical model for estimating the spatial distribution of landslides in near real-time around the globe for use in conjunction with the U.S. Geological Survey (USGS) Prompt Assessment of Global Earthquakes for Response ( PAGER ) system. This model uses standardized outputs of ground shaking from the USGS ShakeMap Atlas 2.0 to develop an empirical landslide probability model, combining shaking estimates with broadly available landslide susceptibility proxies, i.e., topographic slope, surface geology, and climate parameters. We focus on four earthquakes for which digitally mapped landslide inventories and well-constrained ShakeMaps are available. The resulting database is used to build a predictive model of the probability of landslide occurrence. The landslide database includes the Guatemala (1976), Northridge (1994), Chi-Chi (1999), and Wenchuan (2008) earthquakes. Performance of the regression model is assessed using statistical goodness-of-fit metrics and a qualitative review to determine which combination of the proxies provides both the optimum prediction of landslide-affected areas and minimizes the false alarms in non-landslide zones. Combined with near real-time ShakeMaps , these models can be used to make generalized predictions of whether or not landslides are likely to occur (and if so, where) for earthquakes around the globe, and eventually to inform loss estimates within the framework of the PAGER system.

Engineering Geology↗

USGS2014

Surface wave site characterization at 27 locations near Boston, Massachusetts, including 2 strong-motion stations

The geotechnical properties of the soils in and around Boston, Massachusetts, have been extensively studied. This is partly due to the importance of the Boston Blue Clay and the extent of landfill in the Boston area. Although New England is not a region that is typically associated with seismic hazards, there have been several historical earthquakes that have caused significant ground shaking (for example, see Street and Lacroix, 1979; Ebel, 1996; Ebel, 2006). The possibility of strong ground shaking, along with heightened vulnerability from unreinforced masonry buildings, motivates further investigation of seismic hazards throughout New England. Important studies that are pertinent to seismic hazards in New England include source-parameter studies (Somerville and others, 1987; Boore and others, 2010), wave-propagation studies (Frankel, 1991; Viegas and others, 2010), empirical ground-motion prediction equations (GMPE) for computing ground-motion intensity (Tavakoli and Pezeshk, 2005; Atkinson and Boore, 2006), site-response studies (Hayles and others, 2001; Ebel and Kim, 2006), and liquefaction studies (Brankman and Baise, 2008). The shear-wave velocity (VS) profiles collected for this report are pertinent to the GMPE, site response, and liquefaction aspects of seismic hazards in the greater Boston area. Besides the application of these data for the Boston region, the data may be applicable throughout New England, through correlations with geologic units (similar to Ebel and Kim, 2006) or correlations with topographic slope (Wald and Allen, 2007), because few VS measurements are available in stable tectonic regions. Ebel and Hart (2001) used felt earthquake reports to infer amplification patterns throughout the greater Boston region and noted spatial correspondence with the dominant period and amplification factors obtained from ambient noise (horizontal-to-vertical ratios) by Kummer (1998). Britton (2003) compiled geotechnical borings in the area and produced a microzonation map based on generalized velocity profiles, where the amplifications were computed using Shake (Schnable and others, 1972), along with an assumed input ground motion. The velocities were constrained by only a few local measurements associated with the Central Artery/Tunnel project. The additional VS measurements presented in this report provide a number of benefits. First, these measurements provide improved spatial coverage. Second, the larger sample size provides better constraints on the mean and variance of the VS distribution for each layer, which may be paired with a three-dimensional (3D) model of the stratigraphy to generate one-dimensional (1D) profiles for use in a standard site-response analysis (for example, Britton, 2003). Third, the velocity profiles may also be used, along with a 3D model of the stratigraphy, as input into a 3D simulation of the ground motion to investigate the effects of basin-generated surface waves and the potential focusing of seismic waves. This report begins with a short review of the geology of the study area and the field methods that we used to estimate the velocity profiles. The raw data, processed data, and the interpreted VS profiles are given in appendix 1. Photographs and descriptions of the sites are provided in appendix 2.

Massachusetts↗

USGS2012

Empirical improvements for estimating earthquake response spectra with random‐vibration theory

The stochastic method of ground‐motion simulation is often used in combination with the random‐vibration theory to directly compute ground‐motion intensity measures, thereby bypassing the more computationally intensive time‐domain simulations. Key to the application of random‐vibration theory to simulate response spectra is determining the duration ( D rms ) used in computing the root‐mean‐square oscillator response. Boore and Joyner (1984) originally proposed an equation for D rms , which was improved upon by Liu and Pezeshk (1999) . Though these equations are both substantial improvements over using the duration of the ground‐motion excitation for D rms , we document systematic differences between the ground‐motion intensity measures derived from the random‐vibration and time‐domain methods for both of these D rms equations. These differences are generally less than 10% for most magnitudes, distances, and periods of engineering interest. Given the systematic nature of the differences, however, we feel that improved equations are warranted. We empirically derive new equations from time‐domain simulations for eastern and western North America seismological models. The new equations improve the random‐vibration simulations over a wide range of magnitudes, distances, and oscillator periods.

Bulletin of the Seismological Society of America↗

USGS2011

Regional correlations of V S 30 averaged over depths less than and greater than 30 meters

Using velocity profiles from sites in Japan, California, Turkey, and Europe, we find that the time-averaged shear-wave velocity to 30 m ( V S 30 ), used as a proxy for site amplification in recent ground-motion prediction equations (GMPEs) and building codes, is strongly correlated with average velocities to depths less than 30 m ( V Sz , with z being the averaging depth). The correlations for sites in Japan (corresponding to the KiK-net network) show that V Sz is systematically larger for a given V Sz than for profiles from the other regions. The difference largely results from the placement of the KiK-net station locations on rock and rocklike sites, whereas stations in the other regions are generally placed in urban areas underlain by sediments. Using the KiK-net velocity profiles, we provide equations relating V S 30 to V Sz for z ranging from 5 to 29 m in 1-m increments. These equations (and those for California velocity profiles given in Boore, 2004b) can be used to estimate V S 30 from V Sz for sites in which velocity profiles do not extend to 30 m. The scatter of the residuals decreases with depth, but, even for an averaging depth of 5 m, a variation in log V S 30 of ±1 standard deviation maps into less than a 20% uncertainty in ground motions given by recent GMPEs at short periods. The sensitivity of the ground motions to V S 30 uncertainty is considerably larger at long periods (but is less than a factor of 1.2 for averaging depths greater than about 20 m). We also find that V S 30 is correlated with V Sz for z as great as 400 m for sites of the KiK-net network, providing some justification for using V S 30 as a site-response variable for predicting ground motions at periods for which the wavelengths far exceed 30 m.

Bulletin of the Seismological Society of America↗

USGS2010

Surface-wave site characterization at 52 strong-motion recording stations affected by the Parkfield, California, M6.0 earthquake of 28 September 2004

We present one-dimensional shear-wave velocity ( V S ) profiles at 52 strong-motion sites that recorded the 28 September 2004 Magnitude 6.0 Parkfield, Calif., earthquake. We estimate the V S profiles with the Spectral Analysis of Surface-Waves (SASW) method. The SASW method is a noninvasive method that indirectly estimates the V S at depth from variations in the Rayleigh wave phase velocity at the surface. To address the uncertainty associated with these measurements, we compare the SASW profiles to surface-source downhole-receiver (SSDR) profiles at four sites. Three of the four SSDR sites are in close agreement with the adjacent SASW site, while the SASW profile is considerably slower than the SSDR profile at one site.

California↗

USGS2010

Implementation of the Next Generation Attenuation (NGA) ground-motion prediction equations in Fortran and R

This report presents two methods for implementing the earthquake ground-motion prediction equations released in 2008 as part of the Next Generation Attenuation of Ground Motions (NGA-West, or NGA) project coordinated by the Pacific Earthquake Engineering Research Center (PEER). These models were developed for predicting ground-motion parameters for shallow crustal earthquakes in active tectonic regions (such as California). Of the five ground-motion prediction equations (GMPEs) developed during the NGA project, four models are implemented: the GMPEs of Abrahamson and Silva (2008), Boore and Atkinson (2008), Campbell and Bozorgnia (2008), and Chiou and Youngs (2008a); these models are abbreviated as AS08, BA08, CB08, and CY08, respectively. Since site response is widely recognized as an important influence of ground motions, engineering applications typically require that such effects be modeled. The model of Idriss (2008) is not implemented in our programs because it does not explicitly include site response, whereas the other four models include site response and use the same variable to describe the site condition (VS30). We do not intend to discourage the use of the Idriss (2008) model, but we have chosen to implement the other four NGA models in our programs for those users who require ground-motion estimates for various site conditions. We have implemented the NGA models by using two separate programming languages: Fortran and R (R Development Core Team, 2010). Fortran, a compiled programming language, has been used in the scientific community for decades. R is an object-oriented language and environment for statistical computing that is gaining popularity in the statistical and scientific community. Derived from the S language and environment developed at Bell Laboratories, R is an open-source language that is freely available at http://www.r-project.org/ (last accessed 11 January 2011). In R, the functions for computing the NGA equations can be loaded as an add-on user-contributed code, which is referred to as a ?package? in R. The details of the nga package (Kaklamanos and Thompson, 2010) are presented in this report. In addition, differences between the R and Fortran implementations will be discussed later in this report. The NGA models have established a new baseline for seismic hazard assessments, and they have been incorporated into the most recent national seismic hazard maps published by the U.S. Geological Survey (Petersen and others, 2008). However, many of the new models are significantly more complicated than previous GMPEs and, therefore, require a substantial investment of time to implement and validate. We hope that the scientific and engineering communities find our implementations to be useful in research and practice. Our implementations may be considered as an alternate to the Microsoft Excel spreadsheet implementation available on the PEER NGA project Web site (http://peer.berkeley.edu/ngawest/, last accessed 11 January 2011). The implementations in Fortran and R are more appropriate for performing batch calculations than the implementation in Microsoft Excel. Spreadsheets and Fortran code for some of the individual models also are available on the PEER NGA project Web site; our programs implement the four GMPEs simultaneously. Our programs give the same results as the programs on the PEER NGA Web site, but we offer some additional flexibility of input, additional methods of estimating unknown input parameters, and additional options for output. Although these programs have been used by the U.S. Geological Survey (USGS), Tufts University, and others, no warranty, expressed or implied, is made by Tufts or the USGS as to the accuracy or functioning of the programs and related material, nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by Tufts or the USGS in connection therewith.

Open-File Report↗

USGS2010

Three-dimensional site response at KiK-net downhole arrays

Ground motions at two Kiban-Kyoshin Network (KiK-net) strong motion downhole array sites in Hokkaido, Japan (TKCH08 in Taiki and TKCH05 in Honbetsu) illustrate the importance of three-dimensional (3D) site effects. These sites recorded the M8.0 2003 Tokachi-Oki earthquake, with recorded accelerations above 0.4 g at both sites as well as numerous ground motions from smaller events. Weak ground motions indicate that site TKCH08 is well modeled with the assumption of plane SH waves traveling through a 1D medium (SH1D), while TKCH05 is characteristic of a poor fit to the SH1D theoretical response. We hypothesized that the misfit at TKCH05results from the heterogeneity of the subsurface. To test this hypothesis, we measured four S-wave velocity profiles in the vicinity (< 300 m) of each site with the spectral analysis of surface waves (SASW) method. This KiK-net site pair is ideal for assessing the relative importance of 3D site effects and nonlinear site effects. The linear ground motions at TKCH05 isolate the 3D site effects, as we hypothesized from the linear ground motions and confirmed with our subsequent SASW surveys. The Tokachi-Oki time history at TKCH08 isolates the effects of nonlinearity from spatial heterogeneity because the 3D effects are negligible. The Tokachi-Oki time history at TKCH05 includes both nonlinear and 3D site effects. Comparisons of the accuracy of the SH1D model predictions of these surface time histories from the downhole time histories indicates that the 3D site effects are at least as important as nonlinear effects in this case. The errors associated with the assumption of a 1D medium and 1D wave propagation will be carried into a nonlinear analysis that relies on these same assumptions. Thus, the presence of 3D effects should be ruled out prior to a 1D nonlinear analysis. The SH1D residuals show that 3D effects can be mistaken for nonlinear effects.

Hokkaido↗