Geology topics

Morgan P. Moschetti

Publications and source records attributed to Morgan P. Moschetti.

At least 55 records · Page 3Linked to original sources

USGS2021

Basin and site effects in the U.S. Pacific Northwest estimated from small‐magnitude earthquakes

Earthquake hazards in the U.S. Pacific Northwest (PNW) are increased by the presence of deep sedimentary basins that amplify and prolong ground shaking. To better understand basin and site effects on ground motions, we compile a database of recordings from crustal and intraslab earthquakes. We process 8028 records with magnitudes from 3.5 to 6.8 and hypocentral depths up to 62 km to compute Fourier amplitude spectra of ground acceleration for frequencies of 0–20 Hz. We compute residuals relative to the Bayless and Abrahamson (2019; hereafter, BA18) ground‐motion model and perform a series of linear, crossed, mixed‐effects regressions. In addition to estimating the bias, event, and site terms, we incorporate groupings for broad regionalized site response in three different regions (Seattle basin, Puget Lowland, non‐Puget Lowland), for effects from seismotectonic regime (crustal and intraslab sources), and for interactions between the regions and seismotectonic regimes. We find that the scaling of site response with respect to V S 30 (time‐averaged shear‐wave velocity from the surface to a depth of 30 m) and to basin depth indicators Z 1.0 and Z 2.5 (depths to the 1.0 and 2.5 km/s shear‐wave velocity horizons) is generally consistent with BA18; however, the region terms display strong spatial amplification patterns. For frequencies less than 5 Hz, the Seattle basin amplifies ground motions up to a factor of four, relative to the non‐Puget Lowland, with a maximum amplification around near 0.5 Hz. Sites in the Puget Lowland amplify low frequencies up to a factor of 2.5. At higher frequencies ( ⁠ f > 5 Hz ⁠ ), the Puget Lowland and Seattle basin show regional deamplification of ground motions, with the smallest average amplification factor of 0.65 occurring at 10.0 Hz. Although we observe slight differences in the seismotectonic regime terms, we find that the region terms are significantly more important for modeling earthquake hazard in the PNW.

Oregon, Washington↗

USGS2021

Inter-source interferometry of seismic body waves: Required conditions and examples

Seismic interferometry is widely applied to retrieve wavefields propagating between receivers. Another version of seismic interferometry, called inter-source interferometry, uses the principles of seismic reciprocity and expands interferometric applications to retrieve waves that propagate between two seismic sources. Previous studies of inter-source interferometry usually involve surface-wave and coda-wave estimations. We use inter-source interferometry to estimate the P-waves propagating between two sources rather than the estimation of surface waves and coda waves. We show that the recovered arrival times are dependent on the accuracy of the earthquake catalog of the two sources. Using inter-source interferometry, one can recover the waveform of the direct body waves and potentially reconstruct the waveform of coda waves, depending on the source-receiver geometry. The retrieval of these waveforms is accurate only when the wavefield is sampled with approximately 4 receivers per wavelength in the stationary phase zone. We show that using only receivers inside the stationary phase region for inter-source interferometry introduces the phase error of approximately 0.3 radians. In our study, we show an example of the P-wavefield reconstruction between two earthquakes using the seismic records from an array along San Andreas Fault. The retrieved P waves give a qualitative estimation of the thickness of the low-velocity zone of San Andreas Fault of approximately 4 km.

Pure and Applied Geophysics↗

USGS2021

Seismic wave propagation and basin amplification in the Wasatch Front, Utah

Ground‐motion analysis of more than 3000 records from 59 earthquakes, including records from the March 2020 M w "> M w 5.7 Magna earthquake sequence, was carried out to investigate site response and basin amplification in the Wasatch Front, Utah. We compare ground motions with the Bayless and Abrahamson (2019 ; hereafter, BA18) ground‐motion model (GMM) for Fourier amplitude spectra, which was developed on crustal earthquake records from California and other tectonically active regions. The Wasatch Front records show a significantly different near‐source rate of distance attenuation than the BA18 model, which we attribute to differences in (apparent) geometric attenuation. Near‐source residuals show a period dependence of this effect, with greater attenuation at shorter periods ( ⁠ T < 0.5    s "> T < 0.5 s ) and a correlation between period and the distance over which the discrepancy manifests ( ⁠ ∼ 20 – 50    km "> ∼ 20 – 50 km ⁠ ). We adjusted the recorded ground motions for these regional path effects and solved for station site terms using linear mixed‐effects regressions, with groupings for events and stations. We analyzed basin amplification by comparing the site terms with the basin geometry and basin depths from two seismic‐velocity models for the region. Sites over the deeper parts of the sedimentary basins are amplified by factors of 3–10, relative to sites with thin sedimentary cover, with greater amplification at longer periods ( ⁠ T ≳ 1    s "> T ≳ 1 s ). Average ground‐motion variability increases with period, and long‐period variability exhibits a slight increase at the basin edges. These results indicate regional seismic wave propagation effects requiring further study, and potentially a regionalized GMM, as well as highlight basin amplification complexities that may be incorporated into seismic hazard assessments.

Utah↗

USGS2021

The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US

The U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous U.S. NSHM includes significant changes to the underlying ground motion models (GMMs), most of which are necessary to enable the new multi-period response spectra (MPRS) requirements of seismic design regulations that use hazard results for 22 spectral periods and eight site classes. This article focuses on the GMMs used in the western United States (WUS) and is a companion to a recent article on the GMMs used in the central and eastern United States (CEUS). In the WUS, for crustal and subduction earthquakes, two models used in previous versions of the NSHM are excluded to provide consistency over all considered periods and site classes. To more accurately estimate ground motions at long periods in the vicinity of Los Angeles, San Francisco, Salt Lake City, and Seattle, the 2018 NSHM incorporates deep sedimentary basin depth from local seismic velocity models. The subduction GMMs considered lack basin depth terms and are modified to include an additional scale factor to account for this. This article documents the WUS GMMs used in the 2018 NSHM update and provides detail on the changes to GMM medians, aleatory variability, epistemic uncertainty, and site-effect models. It compares each of these components with those considered in prior NSHMs and discusses their total effect on hazard.

Arizona, California, Idaho, Montana, New Mexico, N↗

USGS2021

Temporal seismic velocity variations: Recovery following from the 2019 Mw 7.1 Ridgecrest, California earthquake

We investigated seismic velocity changes ( dv / v ) associated with the 2019 Ridgecrest earthquake sequence with high‐frequency autocorrelations of ambient seismic noise data. Daily autocorrelation functions were computed for the entirety of 2019 and the first quarter of 2020 for broadband stations within the region, including the temporary broadband stations installed during the aftershock deployment. Travel time shifts in the daily autocorrelation functions, relative to the mean autocorrelation waveform, were computed to produce dv / v time series, which are sensitive to the evolving material properties of the shallow crust surrounding the Ridgecrest fault zone (RFZ). A short‐term velocity drop follows the M w 7.1 earthquake at stations in the vicinity of the rupture surface, while those greater than 50 km away showed no such drop. The maximum, absolute changes in seismic velocity are proportional to the logarithm of distance from the fault rupture and to the peak dynamic strain experienced during the earthquake. Near the areas of the highest coseismic slip within the RFZ, seismic velocities recovered over 3 months. However, in the vicinity of the nearby Garlock fault, where triggered slip manifested, and north of the RFZ, seismic velocities recovered within a month. We interpret the seismic velocity changes and their recovery to be largely due to changes in the physical properties of the shallow crust, such as fault zone damage recovery caused by the earthquake rupture process and in response to the large dynamic stresses of passing seismic waves from the mainshock.

California↗

USGS2021

The 2018 update of the US National Seismic Hazard Model: Ground motion models in the central and eastern US

The United States Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous US NSHM includes major changes to the underlying ground motion models (GMMs). Most of the changes are motivated by the new multi-period response spectra requirements of seismic design regulations that use hazard results for 22 spectral periods and 8 site classes. In the central and eastern United States (CEUS), the 2018 NSHM incorporates 31 new GMMs for hard-rock site conditions ( V S 30 = 3000 m / s ) "> ( V S 30 = 3000 m / s ) (VS30=3000m/s) , including the Next Generation Attenuation (NGA)-East GMMs. New aleatory variability and site-effect models, both specific to the CEUS, are applied to all median hard-rock GMMs. This article documents the changes to the USGS GMM selection criteria and provides details on the new CEUS GMMs used in the 2018 NSHM update. The median GMMs, their weights, epistemic uncertainty, and aleatory variability are compared with those considered in prior NSHMs. This article further provides implementation details on the CEUS site-effect model, which allows conversion of hard-rock ground motions to other site conditions in the CEUS for the first time in NSHMs. Compared with the 2014 NSHM hard-rock ground motions, the weighted average of median GMMs increases for large magnitude events at middle to large distance range, epistemic uncertainty increases in almost all situations, but aleatory variability is not significantly different. Finally, the total effect on hazard is demonstrated for an assumed earthquake source model in the CEUS, which shows an increased ring of ground motions in the vicinity of the New Madrid seismic zone and decreased ground motions near the East Tennessee seismic zone.

Earthquake Spectra↗

USGS2021

The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed

The 2018 US Geological Survey National Seismic Hazard Model (NSHM) incorporates new data and updated science to improve the underlying earthquake and ground motion forecasts for the conterminous United States. The NSHM considers many new data and component input models: (1) new earthquakes between 2013 and 2017 and updated earthquake magnitudes for some earlier earthquakes; (2) two updated smoothed seismicity models to forecast earthquake rates; (3) two suites of new central and eastern US (CEUS) ground motion models (GMMs) to translate ground shaking for various earthquake sizes and source-to-site distances considered in the model; (4) two CEUS GMMs for aleatory variability; (5) two CEUS site-effect models that modify ground shaking based on alternative shallow site conditions; (6) more advanced western US (WUS) lithologic and structural information to assess basin site effects for selected urban regions; and (7) a more comprehensive range of outputs (22 periods and 8 site classes) than in previous versions of the NSHMs. Each of these new datasets and models produces changes in the probabilistic ground shaking levels that are spatially and statistically analyzed. Recent earthquakes or changes to some older earthquake magnitudes and locations mostly result in probabilistic ground shaking levels that are similar to previous models, but local changes can reach up to +80% and −60% compared to the 2014 model. Newly developed CEUS models for GMMs, aleatory variability, and site effects cause overall changes up to ±64%. The addition of the WUS basin amplifications causes changes of up to +60% at longer periods for sites overlying deep soft soils. Across the conterminous United States, the hazard changes in the model are mainly caused by new GMMs in the CEUS, by sedimentary basin effects for long periods (≥1 s) in the WUS, and by seismicity changes for short (0.2 s) and long (1 s) periods for both areas.

Earthquake Spectra↗

USGS2020

Spectral inversion for seismic site response in central Oklahoma: Low-frequency resonances from the Great Unconformity

We investigate seismic site response by inverting seismic ground‐motion spectra for site and source spectral properties, in a region of central Oklahoma, where previous ground‐motion studies have indicated discrepancies between observations and ground‐motion models (GMMs). The inversion is constrained by a source spectral model, which we computed from regional seismic records, using aftershocks as empirical Green’s functions to deconvolve site and path effects. Site spectra across the region exhibit multiple, strong, low‐frequency ( ⁠ f <2 Hz ⁠ ) resonances. Modeling of vertically propagating SH waves reproduces the mean amplitudes and frequencies of the site spectra and requires a deep ( ⁠ ∼1–2 km ⁠ ) impedance contrast. Comparison of regional seismic velocity models and geologic profiles indicates that the seismic impedance contrast is, or is in proximity to, the Great Unconformity, which marks the interface between Precambrian basement rocks and overlying Paleozoic sedimentary rocks. Depth to Precambrian basement increases to the southwest across the study region ( ⁠ ∼1500–4500 m ⁠ ), and the fundamental frequencies of the site spectra are anticorrelated with basement depth. The first higher‐mode resonance also exhibits dependence on basement depth; although modeling suggests that the second higher mode should depend on basement depth, site spectra do not support this. The low‐frequency resonances in central Oklahoma are not represented in the GMMs used in current seismic hazard analyses for tectonic earthquakes, though approaches to account for such features are under consideration in other regions of the central and eastern United States. Given the broad spatial extent of the Great Unconformity underlying eastern North America, it is likely that similar effects on seismic site response also occur in other areas. This study highlights the impact of regional geologic structure on earthquake ground motions and reiterates the need for modeling regional effects to improve ground‐motion predictions and seismic hazard assessments.

Oklahoma↗

USGS2020

A machine learning approach to developing ground motion models from simulated ground motions

We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic-based GMM with empirically based GMMs derived from the globally based Next Generation Attenuation West2 data set, finding near-zero median residuals and similar amplitude and trends (with period) of total variability. Additionally, we find that the artificial neural network GMM has similar bias and variability to empirical GMMs from records of the recent Ridgecrest event, which neither GMM has included in its formulation. As simulations continue to better model broadband ground motions, machine learning provides a way to utilize the vast amount of synthetically generated data and guide future parameterization of GMMs.

California↗

USGS2020

The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics

Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground‐motion records from multiple data centers and processed these records using newly developed ground‐motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground‐motion metrics. In addition, we compute station and waveform metrics such as the time‐averaged shear‐wave velocity to 30 m depth ( ⁠ V S 30 "> V S 30 ⁠ ), finite‐rupture distances, and spectral accelerations. This data set includes 22,708 records from 133 events from 4 July 2019 (UTC) to 18 October 2019 with a magnitude range from 3.6 to 7.1. We expect that the rapid collection and dissemination of this information will facilitate detailed studies of these ground motions. In this article, we describe the data selection, processing steps, and how to access the data.

California↗

USGS2020

Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard forecasts: Hawaii tectonic earthquakes and volcanic eruptions

The selection and weighting of ground‐motion models (GMMs) introduces a significant source of uncertainty in U.S. Geological Survey (USGS) National Seismic Hazard Modeling Project (NSHMP) forecasts. In this study, we evaluate 18 candidate GMMs using instrumental ground‐motion observations of horizontal peak ground acceleration (PGA) and 5%‐damped pseudospectral acceleration (0.02–10 s) for tectonic earthquakes and volcanic eruptions, to inform logic‐tree weights for the update of the USGS seismic hazard model for Hawaii. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function (PDF), mean and standard deviations σ "> σ ⁠ , of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Hawaii seismic hazard model logic trees. The total residual PDF approach provides additional information by preserving GMM over‐ and underprediction across a broad spectrum of periods that is not available from a single value LLH score. We apply these GMM evaluation methods to two different data sets: (1) a database of instrumental ground motions from historic earthquakes in Hawaii from 1973 to 2007 ( ⁠ M w "> M w 4–7.3) and (2) available ground motions from recent earthquakes ( ⁠ M w "> M w 4–6.9) associated with 2018 Kilauea eruptions. The 2018 Kilauea sequence contains both volcanic eruptions and tectonic earthquakes allowing for statistically significant GMM comparisons of the two event classes. The Kilauea ground observations provide an independent data set allowing us to evaluate the predictive power of GMMs implemented in the new USGS nshmp‐haz software system. We evaluate GMM performance as a function of earthquake depth and we demonstrate that short‐period volcanic eruption ground motions are not well predicted by any candidate GMMs. Nine of the initial 18 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the update of the Hawaii seismic hazard model. A weighted mean of four top performing GMMs in this study (NGAsubslab, NGAsubinter, ASK14, A10) is 50% lower for PGA than for GMMS used in the previous USGS seismic hazard model for Hawaii.

Hawaii↗

USGS2020

2018 and 2023 U. S. National Seismic Hazard Models

During 2017-2018, the National Seismic Hazard Model (NSHM) was updated by incorporating (1) new median ground motion models, new estimates of their epistemic uncertainties and aleatory variabilities, and new soil amplification factors for the central and eastern U.S., (2) amplification of long-period ground motions in deep sedimentary basins in the Los Angeles, San Francisco, Seattle, and Salt Lake City areas, (3) an updated seismicity catalog, which includes new earthquakes that occurred between 2012 and 2017, and (4) improved computer code and implementation details. Results show significantly increased ground shaking in many (but not all) locations across the central and eastern U.S., and increased ground shaking in the four urban areas (listed above) that overlie deep sedimentary basins in the western U.S. During 2019-2023, the NSHM will consider additional updates for the 2023 NSHM including: 3D simulations in urban areas of Los Angeles and Seattle, additional soil amplification models for other urban areas (e.g., eastern and southern coastal plains of the U.S., Reno, and Las Vegas), non-ergodic ground motion models that reconsider epistemic and aleatory uncertainty, topographic amplification, and new geological and geodetic models for faults. These maps will be considered by the Building Seismic Safety Council, American Society of Civil Engineers, and International Building Code committees for inclusion in upcoming building codes. Due to population growth, more people live and work in areas of moderate or high seismic hazard than ever before, leading to higher risk of undesirable consequences from future ground shaking.

conterminous United States↗

USGS2019

Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, Mw 7.1 subduction zone earthquake sequence

Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska ( ⁠ M w "> M w 7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10 s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations σ "> σ of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50 km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.

Alaska↗

USGS2019

The 2018 update of the US National Seismic Hazard Model: Overview of model and implications

During 2017–2018, the National Seismic Hazard Model for the conterminous United States was updated as follows: (1) an updated seismicity catalog was incorporated, which includes new earthquakes that occurred from 2013 to 2017; (2) in the central and eastern United States (CEUS), new ground motion models were updated that incorporate updated median estimates, modified assessments of the associated epistemic uncertainties and aleatory variabilities, and new soil amplification factors; (3) in the western United States (WUS), amplified shaking estimates of long-period ground motions at sites overlying deep sedimentary basins in the Los Angeles, San Francisco, Seattle, and Salt Lake City areas were incorporated; and (4) in the conterminous United States, seismic hazard is calculated for 22 periods (from 0.01 to 10 s) and 8 uniform V S30 maps (ranging from 1500 to 150 m/s). We also include a description of updated computer codes and modeling details. Results show increased ground shaking in many (but not all) locations across the CEUS (up to ~30%), as well as near the four urban areas overlying deep sedimentary basins in the WUS (up to ~50%). Due to population growth and these increased hazard estimates, more people live or work in areas of high or moderate seismic hazard than ever before, leading to higher risk of undesirable consequences from forecasted future ground shaking.

Earthquake Spectra↗

USGS2019

Ground-motion amplification in Cook Inlet region, Alaska from intermediate-depth earthquakes, including the 2018 MW=7.1 Anchorage earthquake

We measure pseudospectral and peak ground motions from 44 intermediate‐depth M w ≥ 4.9 "> M w ≥ 4.9 earthquakes in the Cook Inlet region of southern Alaska, including those from the 2018 M w "> M w 7.1 earthquake near Anchorage, to identify regional amplification features ( ⁠ 0.1 – 5    s "> 0.1 – 5 s period). Ground‐motion residuals are computed with respect to an empirical ground‐motion model for intraslab subduction earthquakes, and we compute bias, between‐, and within‐event terms through a linear mixed‐effects regression. Between‐event residuals are analyzed to assess the relative source characteristics of the Cook Inlet earthquakes and suggest a difference in the scaling of the source with depth, relative to global observations. The within‐event residuals are analyzed to investigate regional amplification, and various spatial patterns manifest, including correlations of amplification with depth of the Cook Inlet basin and varying amplifications east and west of the center of the basin. Three earthquake clusters are analyzed separately and indicate spatial amplification patterns that depend on source location and exhibit variations in the depth scaling of long‐period basin amplification. The observations inform future seismic hazard modeling efforts in the Cook Inlet region. More broadly, they suggest a greater complexity of basin and regional amplification than is currently used in seismic hazard analyses.

Alaska↗

USGS2019

Rupture model of the M5.8 Pawnee, Oklahoma earthquake from regional and teleseismic waveforms

The 2016 M5.8 Pawnee, Oklahoma earthquake is the largest earthquake to have been induced by wastewater disposal. We infer the coseismic slip history from analysis of apparent source time functions and inversion of regional and teleseismic P‐waveforms, using aftershocks as empirical Green's functions. The earthquake nucleated on the shallow part of the fault, initially rupturing towards the surface, followed shortly thereafter by slip deeper on the fault. Deeper slip occurred below the aftershocks and at greater depths than most induced seismicity in the region, suggesting that small‐ to moderate‐sized earthquakes may not occur on deeper parts of faults in Oklahoma because they are further from failure than shallower fault sections. Comparisons with models of pore pressure perturbations further suggest that the earthquake may have initiated within a region of higher pore pressure perturbation but was not confined to this zone. These observations inform source physics and understanding of maximum magnitudes.

Oklahoma↗

USGS2018

Evaluation of ground motion models for USGS seismic hazard forecasts: Induced and tectonic earthquakes in the Central and Eastern U.S.

Ground motion model (GMM) selection and weighting introduces a significant source of uncertainty in United States Geological Survey (USGS) seismic hazard models. The increase in moderate moment magnitude induced earthquakes (Mw 4 to 5.8) in Oklahoma and Kansas since 2009, due to increased wastewater injection related to oil and gas production (Keranen et al., 2013; 2014; Weingarten et al., 2015; McNamara et al., 2015a), provides useful near-source (< 40 km) instrumental ground-motion observations for comparisons between central and eastern US (CEUS) induced (Rennolet et al., 2017) and tectonic (Goulet et al., 2014) earthquakes. In this study, we evaluate over 50 GMMs using two well-established probabilistic scoring methods: log likelihood (LLH) (Scherbaum et al., 2004; 2009) and multivariate LLH (MLLH) (Mak et al., 2017). The LLH approach compares the mean and standard deviation (σ) of the observed and modeled ground motions. The MLLH approach advances the LLH method by considering the variability (φ,τ) of multiple correlated variables namely intra- (within) and inter- (between) event residuals. For the probabilistic scoring GMM evaluation methods (LLH, MLLH), we compute horizontal component peak ground acceleration (PGA) and 1s period pseudo spectral acceleration (PSA1.0) total residuals using GMM software (nshmp-haz) recently implemented by the USGS National Seismic Hazard Model Project (NSHMP). We observe from LLH and MLLH scores that: 1) newer GMMs with lower standard deviations (σ,φ,τ) score better than older GMMs with higher published uncertainty; 2) 2014 CEUS GMMs score better for CEUS tectonic earthquakes than induced earthquakes; 3) NGA-West2, G17 and A15 GMMs score well for CEUS induced earthquake ground motions; and 4) NGA-East GMMs score well for CEUS tectonic earthquake ground motions. We also use the LLH and MLLH scores to evaluate GMM weights applied in past USGS seismic hazard forecasts and to inform weighting of GMMs in future seismic hazard forecasts.

Bulletin of the Seismological Society of America↗

USGS2018

Ground motions from induced earthquakes in Oklahoma and Kansas

Improved predictions of earthquake ground motions are critical to advancing seismic hazard analyses and earthquake response. The high seismicity rate from 2009 to 2016 in Oklahoma and Kansas provides an extensive data set for examining the ground motions from these events. We evaluate the ability of three suites of ground‐motion prediction equations (GMPEs)—appropriate for modeling tectonic earthquakes in active crustal and stable continental regions—to reproduce the observed ground motions. Mixed‐effects regressions are used to separate the ground‐motion residuals into bias, between‐event, and within‐event terms. Although the residuals depict differing accuracies in the ability of the three GMPE suites to predict the ground motions, some consistent trends emerge in the period, magnitude, and distance dependence. The trends suggest that aspects of the ground motions from these induced earthquakes are not well modeled by current tectonic GMPEs. Most important, we find evidence for relatively poor overall fit to the ground motions, by all of the GMPE suites, at periods less than about 0.2 s and above 3 s, greater‐than‐predicted magnitude scaling for small to moderate‐magnitude events ( ⁠ M ≲ 5 "> M ≲ 5 ⁠ ), higher‐than‐predicted within‐event variability above 3 s, and an apparent geometric attenuation that is stronger than average predictions at close distances ( ⁠ R ≲ 20    km "> R ≲ 20 km ) and short periods ( ⁠ T ≲ 1 s "> T ≲ 1 s ). Our results indicate that regionally appropriate GMPEs for induced earthquakes should be used for predicting ground motions in Oklahoma and Kansas.

Kansas, Oklahoma↗