Evaluating the INLA-SPDE approach for Bayesian modeling of earthquake damages from geolocated cluster data

Abstract

Modeled damage estimates are an important source of information in the hours to weeks following major earthquake disasters, but often lack sufficient spatial resolution for highlighting specific areas of need. Using damage assessment data from the 2015 Gorkha, Nepal Earthquake, this paper evaluates a Bayesian spatial model (INLA-SPDE) for interpolating geolocated damage survey data onto 1 km2 grid cells. The proposed approach uses a combination of geospatial covariate data and Gaussian spatial process random effects modeling to estimate the percentage of structures attaining complete damage states from sparse survey clusters. Model performance is evaluated across fifty iterations of 100, 250, and 1000 simulated survey clusters and compared to observed damage assessments and model predictions using more traditional fragility-based methods. Results show strong model fit to observed values, with mean absolute errors of .17, .13, and .11 and correlation coefficients of .75, .82, and .85 for increasing numbers of survey clusters. These results show improvements over traditional damage estimation methods with a small percentage of the damage surveys that were available within several weeks after the Gorkha event. Thus, with sufficient rapid damage assessment mobilization, the proposed model is able to provide improved damage estimates in the time frame required to deliver a Post Disaster Needs Assessment even in cases where no additional damage data is available.

Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China

“…Using INLA-SPDE to fit model (14) and model (15), the posterior estimations of the fixed effect coefficients are presented in Table 7.…”

“…The comparison results of the obtained DIC and WAIC values are shown in Table 6. 15) is smaller than model (14), indicating that model (15) has better model fitting effect and can fully capture the spatial effects on response variables.…”

“…So, the INLA algorithm can be combined with SPDE. INLA-SPDE has been widely applied in various fields, including air pollution (Cameletti et al (2013) [10]), infectious diseases (Moraga et al (2021) [11]), species (Moraga, (2021) [12]), wildfires (Zhang et al (2023) [13]), earthquakes (Wilson et al (2020) [14]), etc. Detailed application of INLA-SPDE in the R-INLA software can be found in Lindgren et al (2015) [15], Krainski et al (2018) [16].…”