DEVELOPMENTOFGROUNDMOTIONPREDICTIONEQUATIONOF KATHMANDUBASIN

Abstract

Kathmandu Basin, situated in the earthquake-prone area of Nepal, faces significant earthquake risks due to its distinctive geotechnical and tectonic properties. Developing a Ground Motion Prediction Equation (GMPE) specific to this region is essential for precise seismic hazard evaluation and effective mitigation strategies. This research presents a newly developed GMPE for the Kathmandu Basin, integrating local seismological insights to tackle the shortcomings of general global models. By incorporating the basin’s deep sedimentary deposits and intricate site effects, this GMPE offers a reliable framework for forecasting ground motion metrics crucial for engineering and city planning. The GMPE was crafted using a collection of earthquake from both the Kathmandu Basin and adjacent zones, covering a spectrum of magnitudes and distances. The dataset draws from majorstrong-motion records like the Gorkha earthquake 2015 of 7.8 Mw and the tibet earthquake 2025 of 7.0 Mw along with other earthquakes from 2011 A.D. to 2025 A.D. to ensure thorough representation of the area’s seismic activity. Regression methods like Ordinary Least Squares and Mixed Effect Regression were used to model critical ground motion indicator i.e. peak ground acceleration (PGA). Particular focus was placed on integrating sediment-induced amplification effects, a vital aspect of the Kathmandu Basin’s local seismic effects. The developed GMPE combines deterministic and stochastic modeling approaches to effec tively capture the unique attenuation features of the region and the response specific to various sites. The deterministic aspect is responsible for accounting for the physical wave propagation characteristics, while the stochastic element manages the random variability in ground motion forecasts. The use of this hybrid strategy allows the model to remain both physically accurate andflexible to accommodatetheintrinsic uncertainties present in seismic data. This model takes into account site-specific factors like soil amplification, pre-dominant period, and the depth to bedrock, which are crucial for conducting dependable seismic hazard assessments in the val ley. The aleatory-1 sigma (13-67 cm/s²), +1 sigma (73-415 cm/s²), and deterministic (30-166 cm/s²) PGA maps from this study indicates the increased vulnerability of the central and south ern regions to seismic wave amplification. Additionally, the GMPE includes magnitude scaling, attenuation over distance, and site-specific factors such as depth to bedrock, shear wave velocity of top30mfr9yMnTm4NSzvG9rrwjM2ec8xZgh1cafXH8, therebyimprovingthereliabilityofpredictions across diverse earthquake situations. The GMPE was validated with independent datasets from the seismic station located in Department of Mines and Geology, around the central part of the Kathmandu Basin, for the 2015 Gorkha earthquake. While the PGA prediction from the mixed effect model for the event stood at 132 cm/s², the observed PGA was 115 cm/s² with residual of around cm/s² overestimating the ground motion intensity by around 15%. Predictions from the model were also evaluated against recorded ground motion parameters revealing a high level of agreement, especially for events of moderate to high magnitude.Residual analysis of the stations 2_TVU,3_PTN,4_THMand9_KATNPstandingat7.6823cm/s²,-8.1528cm/s²,-11.74 cm/s² and13.821cm/s²respectivelyindicatedsitespecificdeviationsinmodelperformance. However, the residual is not significant which is also the reflection of the better model perfomance. The seismic hazard map for the 2015 Gorkha earthquake from this study indicated the sever intensity at the Sankhu, the Bhaktapur, the central part of the Kathmandu and less at outskirts of the basin closely matching with the damages pattern observed during the Gorkha earthquake. Compar ative analyses with existing regional GMPEs for the Himalayan subduction zone demonstrated the better performance of the proposed model in capturing the unique seismic characteristics of the Kathmandu Basin for it innately represents both the basin’s amplification impact and atten uation characteristics. Additionally, sensitivity analyses and performance metrics substantiated the model’s robustness under diverse Kathmandu Basin features and earthquake magnitudes. The developed GMPEstands as a pioneering effort in customizing seismic hazard models to the v Kathmandu Basin’s unique geological and seismological context yet the extrapolability of the model to higher magnitude events (Mw > 7.8) remains to be cautioned and further investigated as the current dataset is limited to moderate magnitude events. Future work could focus on expanding the dataset with more high-magnitude events and refining the model to enhance its predictive accuracy. Furthermore, the applicability of this GMPE to other basins with similar characteristics could be explored in future research. This GMPE signifies a leap forward in evaluating seismic hazards for the Kathmandu Basin, furnishing a dependable resource for engineers, planners, and decision-makers yet the study has to be extended to the other spectral periods to encompass a broader range of spectral responses and site specific amplication effects and further validate the predictions using additional strong motion records from a wider range of earthquake scenarios. Bydeliveringreliablefor9yMnTm4NSzvG9rrwjM2ec8xZgh1cafXH8,themodelwillaidinseismicdesign of infrastructure and formulating robust disaster readiness plans. Future enhancements could incorporate new data from developing seismic networks and cutting-edge modeling methods to better the model’s predictive abilities. This study highlights the crucial role of region-specific GMPEsin reducing earthquake risk in susceptible urban areas such as the Kathmandu Basin. In conclusion, the developed mixed-effects regression model based GMPE tailored for the Kathmandu Basin outperformed compared to the traditional ordinary least squares regression model, primarily due to its ability to account for both fixed and random effects enabling the model to address the inter-event and intra-event variability in seismic data effectively. The model’s not only exhibited a remarkable close alignment with empircally observed ground motions across diverse earthquake scenarios but also outperformed other GMPE tailored for the Himalayan subduction zone. This enhanced prediction is particulary evident in the Hazard maps generated from the model when compared to the Grade 5 structural damage pattern observed during the 2015 Gorkha earthquake, where site specific predictions reflected intense shaking and damage of the structures. This is the clear and strong empirical validation of the model’s efficacy in capturing the unique seismic characteristics of the Kathmandu Basin, making it a vital tool for seismic hazard assessment and mitigation planning in this earthquake-prone region, the Kathmandu Basin.