Geology topics

John Rekoske

Publications and source records attributed to John Rekoske.

8 recordsLinked to original sources

USGS2023

3D Dynamic rupture modeling of the 6 February 2023, Kahramanmaraş, Turkey Mw 7.8 and 7.7 earthquake doublet using early observations

The 2023 Turkey earthquake sequence involved unexpected ruptures across numerous fault segments. We present 3D dynamic rupture simulations to illuminate the complex dynamics of the earthquake doublet. Our models are constrained by observations available within days of the sequence and deliver timely, mechanically consistent explanations of the unforeseen rupture paths, diverse rupture speeds, multiple slip episodes, heterogeneous fault offsets, locally strong shaking, and fault system interactions. Our simulations link both earthquakes, matching geodetic and seismic observations and reconciling regional seismotectonics, rupture dynamics, and ground motions of a fault system represented by 10 curved dipping segments and embedded in a heterogeneous stress field. The M w 7.8 earthquake features delayed backward branching from a steeply branching splay fault, not requiring supershear speeds. The asymmetrical dynamics of the distinct, bilateral M w 7.7 earthquake are explained by heterogeneous fault strength, prestress orientation, fracture energy, and static stress changes from the previous earthquake. Our models explain the northward deviation of its eastern rupture and the minimal slip observed on the Sürgü fault. 3D dynamic rupture scenarios can elucidate unexpected observations shortly after major earthquakes, providing timely insights for data‐driven analysis and hazard assessment toward a comprehensive, physically consistent understanding of the mechanics of multifault systems.

The Seismic Record↗

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

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↗

USGS2020

Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence

We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence ( Rekoske et al. , 2019 , 2020 ) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014 ; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and find strong correlation with our event terms, indicating that they are consistent with source processes. We look for physically meaningful trends in the partitioned residuals and test the ability of BSSA14 to capture the behavior we observe in the data. We find that BSSA14 is a good match to the median observations for M > 4 "> M > 4 M>4 ⁠ . However, we find bias for individual events, especially those with small magnitude and hypocentral depth ≥ 7    km "> depth ≥ 7 km depth≥7 km ⁠ , for which peak ground acceleration is underpredicted by a factor of 2.5. Although the site amplification term captures the median site response when all sites are considered together, it does not capture variations at individual stations across a range of site conditions. We find strong basin amplification in the Los Angeles, Ventura, and San Gabriel basins. We find weak amplification in the San Bernardino basin, which is contrary to simulation‐based findings showing a channeling effect from an event with a north–south azimuth. This and an additional set of ground motions from earthquakes southwest of Los Angeles suggest that there is an azimuth‐dependent southern California basin response related to the orientation of regional structures when ground motion from waves traveling south–north are compared with those in the east–west direction. These findings exhibit the power of large, spatially dense ground‐motion datasets and make clear that nonergodic models are a way to reduce bias and uncertainty in ground‐motion estimation for applications like the U.S. Geological Survey National Seismic Hazard Model and the ShakeAlert earthquake early warning System.

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↗

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

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↗