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.

ESTIMATING BODY FAT PERCENTAGE THROUGH BODY MASS INDEX AND HANDGRIP STRENGTH IN MIDDLE AND OLDER-AGED ASIAN ADULTS

Objectives: To determine the ability of handgrip strength combined with body mass index (BMI, kg/m2) to estimate body fat percentage (BF%) in middle-aged and older Asian adults. Methods: Middle-aged and older Asian adults (n=459, males=197) were randomly divided into a validation and model development group (n=303) and cross-validation group (n=156). A whole-body scan using dual energy x-ray absorptiometry measured BF%. Bland-Altman plots, standard error of the estimates, total errors and mean absolute errors were used to compare prediction equations. Stepwise regression analysis was used to determine a new prediction equation for middle-aged and older Asian adults. Right and left handgrip strength, age, sex and BMI were included in the analysis. Results: A previously developed prediction equation that included handgrip strength poorly predicted BF% in our current sample with the mean difference being -6.0 ± 4.2%. Predicted BF% values were significantly lower than measured BF% values (22.7% vs. 28.7%, p<0.05). A new prediction equation was developed that included sex, BMI, left handgrip strength and age. Validation of the new equation revealed a constant error of 0.2 ± 3.9% with there being no significant difference between measured and predicted BF% (28.2% vs. 28.5%, p=0.467). Previously developed BF% equations using BMI, but not handgrip strength, had similar constant errors and mean absolute errors compared to the new prediction equation. Conclusion: Handgrip strength does not appear to improve the estimation of body fat percentage from BMI prediction equations in middle and older-aged Asian adults.

Estimating Energy Concentrations in Wooded Pastures of NW Spain Using Empirical Models That Relate Observed Metabolizable Energy to Measured Nutritional Attributes

Wooded pastures serve as a traditional source of forage in Europe, where forest grazing is valued as an efficient tool for maintaining the diversity of semi-natural habitats. In a forest grazing setting with diverse diet composition, assessing the energy content of animal diets can be a difficult task because of its dependency on digestibility measures. In the present study, prediction equations of metabolizable energy (ME) were obtained performing stepwise regression with data (n = 297; 44 plant species) on nutritional attributes (Acid Detergent Fiber, lignin, silica, dry matter, crude protein, in vitro organic matter digestibility) from 20 representative stands of Atlantic dry heathlands and pedunculate oak woodlands. The results showed that the prediction accuracy of ME is reduced when the general model (R2 = 0.64) is applied, as opposed to the use of the specific prediction equations for each vegetation type (R2 = 0.61, 0.66, 0.71 for oak woodlands; R2 = 0.70 heather-gorse dominated heathlands, R2 = 0.41 continental heathlands). The general model tends to overestimate the ME concentrations in heaths with respect to the observed ME values obtained from IVOMD as a sole predictor, and this divergence could be corrected by applying the specific prediction equations obtained for each vegetation type. Although the use of prediction equations by season would improve accuracy in the case of a Winter scenario, using the general model as opposed to the prediction equations for Spring, Summer or Fall would represent a much smaller loss of accuracy.

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.

Evaluation of Intensity Prediction Equations (IPEs) for Small-Magnitude Earthquakes

This study assesses existing intensity prediction equations (IPEs) for small unspecified magnitude (M ≤3.5) earthquakes at short hypocentral distances (Dh) and explores such earthquakes’ contribution to the felt shaking hazard. In particular, we consider IPEs by Atkinson and Wald (2007) and Atkinson et al. (2014), and evaluate their performance based on “Did You Feel It” (DYFI) reports and recorded peak ground velocities (PGVs) in the central United States. Both IPEs were developed based on DYFI reports in the central and eastern United States with moment magnitudes above Mw 3.0. DYFI reports are often used as the ground truth when evaluating and developing IPEs, but they could be less reliable when there are limited responses for small-magnitude earthquakes. We first compare the DYFI reports with intensities interpolated from recorded PGVs. Results suggest a minimal discrepancy between the two when the intensity is large enough to be felt (i.e., M &gt;2 and Dh&lt;15 km). We then compare intensities from 31,617 DYFI reports of 3049 earthquakes with the two IPEs. Results suggest that both the IPEs match well with observed intensities for 2.0&lt; M &lt;3.0 and Dh&lt;10 km, but the IPE by Atkinson et al. (2014) matches better for larger distances. We also observe that intensities from DYFI reports attenuate faster compared with the two IPEs, especially for distances greater than 10 km. We then group DYFI reports by inferred VS30 as a proxy for site amplification effects. We observe that intensities at sites with VS30 around 300 m/s are consistently higher than at sites with VS30 around 700 m/s and are also closer to the two IPEs. Finally, we conduct hazard disaggregation for earthquakes at close distances (Dh=7.5 km) using the observed records. Results suggest that earthquakes with magnitudes below M 3.0 contribute more than 40% to the occurrence of felt shaking.