Preliminary Seismic Hazard Analyses for the Ugandan Region

Uganda is situated between the two seismically active branches of the East African Rift Valley System, which are characterized by high levels of seismicity. A probabilistic approach has been used to assess the seismic hazard for Uganda and the surrounding areas. A probabilistic seismic hazard analysis requires the availability of an earthquake catalog, relevant ground motion prediction equations, and an outline of how the hazard calculations will be conducted. Using online sources, an earthquake catalog for Uganda and the immediate areas around Uganda was compiled spanning 108 years, from 1912 to 2020. This catalog was homogenized to moment magnitude to match with the selected ground motion prediction equations from Toro and Idriss. A logic tree accounting for the two ground motion prediction equations and dividing the study region into four seismic zones was used for calculating the seismic hazard. As an example, the seismic hazard results at two sites close to each other showed how different seismic hazards can be. Results from the probabilistic seismic hazard analyses was expressed through seismic hazard maps for peak ground acceleration at 10% probability of exceedance in 5, 10, 20, 50, 100 and 500 years, corresponding to return periods of 50, 100, 200, 500, 1000 and 5000 years, respectively. The seismic hazard map for 10% probability of exceedance in 5 years calculated PGAs from 0.02 to 0.10 g and 0.10 to 0.27 g outside of and within the western branch of the East African Rift Valley System, respectively. The estimated PGAs from previous studies at a similar probability of exceedance level are within the range of these findings, although the ranges calculated herein are wider.

Updated Probabilistic Seismic Hazard Assessment of Pakistan

Probabilistic seismic hazard assessment of Pakistan is carried out to compute hazard in terms of peak ground acceleration (PGA) and spectral acceleration (SA) for 975 and 2475 years return periods. A composite earthquake catalogue consisting of 32,700 events has been compiled having a magnitude range of Mw 4.0-8.2 in this study and used in the analysis to make computations at a rectangular grid of 5 km in the OpenQuake plateform. Ground motion values have been obtained for flat rock reference seismic site conditions with shear wave velocity of 760 m/s. The epistemic uncertainties inherent in ground motion prediction equations and maximum magnitude potential of seismic sources are taken into account through logic tree. Ground motion prediction equations are assigned equal weights in the logic tree while different various weight are assigned to the maximum magnitude potential models. Results of the study are expressed as ground motion contour maps, mean uniform hazard spectra for important cities in Pakistan. PGA ranges from 0.16 to 0.54g for 10 % of probability of exceedance, 0.23 to 0.72g of probability of exceedance 0.32 to 1.02 g for 2 % of probability of exceedance in 50 years. Spectral acceleration at 0.2 s range from 0.67 to 2.19g for 2% chance of exceedance in 50 years, respectively. While spectral acceleration at 1.0 s values range from 0.09 to 0.52g 2% chance of exceedance in 50 years. Comparison of results of this study with other well regarded references of suggest that results of the study are rational and are reliable.

Next Generation Ground-Motion Prediction Equations for Indo-Gangetic Plains, India

India's Indo-Gangetic Plains (IGP) and its proximity to the Himalayas are seismically the most vulnerable zone. For seismic hazard analysis, it requires a reliable Ground Motion Prediction Equations (GMPEs) for this region. The strong motion accelerometer data are used for the present study from 2005 to 2015. PSA of 5% damped linear pseudo-absolute acceleration response spectra at 27 periods ranging from 0.01 s to 10 s used for regression. Two-stage nonlinear regression is used to train the functional form of a nonlinear magnitude scaling, distance scaling, and site conditions. The model includes a regionally independent geometric attenuation finite fault distance metric, style of faulting, shallow site response, basin response, hanging wall effect, hypocentre depth, regionally dependent anelastic attenuation, site conditions, and magnitude-dependent aleatory variability. We consider our new GMPE is valid for earthquakes from active tectonic shallow crustal continental earthquakes for estimating horizontal ground motion for rupture distances ranging from 1 km to 1500 km and magnitudes ranging from 3.3 to 7.9, and focal depth 1-70 km. The proposed GMPEs developed in this study for predicting PGA and PSA are compared with the Campbell and Bozorgnia 2008, 13 and 14, and North Indian GMPEs for IGP, which is agreed upon consistently. Calibration with observed data gives us the confidence to predict the ground motion from the seismic gaps of Himalaya ranges for the Indo-Gangetic plains. The predicted coefficients of the nonlinear model are anticipated to be valuable for probabilistic seismic hazard analysis over the IGP.

A Nonlinear Site Amplification Model for the Horizontal Component Developed for Ground-Motion Prediction Equations in Japan Using Site Period as the Site-Response Parameter

ABSTRACT This article presents a nonlinear site amplification model for ground-motion prediction equations (GMPEs), using site period as site-effect proxy based on the measured shear-wave velocity profiles of selected KiK-net and K-NET sites in Japan. This model was derived using 1D equivalent-linear site-response analysis for a total of 516 measured soil-site shear-wave velocity profiles subjected to a total of 912 components of rock-site records. The modulus reduction and damping curves for each soil layer were assigned based on the soil-type description for a particular layer. The site period and site impedance ratio affect both the linear and nonlinear parts of this study, and were used as the site parameters in the 1D amplification model. A large impedance ratio enhances the amplification ratios when the site responds elastically and enhances the nonlinear response when the site develops a significant nonlinear response. The effects of moment magnitude and source distance on the linear part of the 1D amplification model were also incorporated in the model. To implement the 1D amplification model into GMPEs, a model adjustment is required to match the GMPE amplification ratio at weak motion and to retain the nonlinear amplification ratio at the strong motion of the 1D model. The two-step adjustment method by Zhao, Hu, et al. (2015) was adopted in this study with significant modifications. It is not possible to obtain a credible second-step adjustment parameter using the GMPEs dataset only. We proposed three methods for calculating the scale factors. Method 1 is a constant angle in a 30°–60° range for all spectral periods; method 2 was based on the GMPE dataset and 1-D model parameters; and method 3 was based on the strong-motion records used for the 1D site modeling. A simple second-step adjustment factor leads to smoothing amplification ratios and soil-site spectrum.

Field Insights and Analysis of the 2018 Mw 7.5 Palu, Indonesia Earthquake, Tsunami and Landslides

A devastating Mw 7.5 earthquake and tsunami struck northwestern Sulawesi, Indonesia on 28 September 2018, causing over 4000 fatalities and severe damage to several areas in and around Palu City. Severe earthquake-induced soil liquefaction and landslides claimed hundreds of lives in three villages within Palu. The mainshock occurred at 18:03 local time at a depth of 10 km on a left-lateral strike-slip fault. The hypocenter was located 70 km north of Palu City and the rupture propagated south, under Palu Bay, passing on land on the west side of Palu City. The surface rupture of the earthquake has been mapped onshore along a 30 km stretch of the Palu-Koro fault. We present results of field surveys on the effects of the earthquake, tsunami and liquefaction conducted between 1–3 and 12–19 of October 2018. Seismic intensities on the Modified Mercalli Intensity (MMI) scale are reported for 375 sites and reach a maximum value of 10. We consolidate published tsunami runup heights from several field studies and discuss three possible interrelated tsunami sources to explain the variation in observed tsunami runup heights. Due to limited instrumentation, PGA and PGV values were recorded at only one of our field sites. To compensate, we use our seismic intensities and Ground Motion to Intensity Conversion Equations (GMICEs) and Ground Motion Prediction Equations (GMPEs) developed for similar tectonic regions. Our results indicate that the maximum predicted PGAs for Palu range from 1.1 g for GMICEs to 0.6 g for GMPEs.

Near-Source Ground Motions and Their Variability Derived from Dynamic Rupture Simulations Constrained by NGA-West2 GMPEs

ABSTRACT Empirical ground-motion prediction equations (GMPEs) lack a sufficient number of measurements at near-source distances. Seismologists strive to supplement the missing data by physics-based strong ground-motion modeling. Here, we build a database of ∼3000 dynamic rupture scenarios, assuming a vertical strike-slip fault of 36×20 km embedded in a 1D layered elastic medium and linear slip-weakening friction with heterogeneous parameters along the fault. The database is built by a Monte Carlo procedure to follow median and variability of Next Generation Attenuation-West2 Project GMPEs by Boore et al. (2014) at Joyner–Boore distances 10–80 km. The synthetic events span a magnitude range of 5.8–6.8 and have static stress drops between 5 and 40 MPa. These events are used to simulate ground motions at near-source stations within 5 km from the fault. The synthetic ground motions saturate at the near-source distances, and their variability increases at the near stations compared to the distant ones. In the synthetic database, the within-event and between-event variability are extracted for the near and distant stations employing a mixed-effect model. The within-event variability is lower than its empirical value, only weakly dependent on period, and generally larger for the near stations than for the distant ones. The between-event variability is by 1/4 lower than its empirical value at periods >1 s. We show that this can be reconciled by considering epistemic error in Mw when determining GMPEs, which is not present in the synthetic data.