Geospatial Liquefaction Modeling of the 2023 Türkiye Earthquake Sequence by an Ensemble of Global, Continental, Regional, and Event-Specific Models

Abstract

Abstract A global geospatial liquefaction model (GGLM-2017) was previously developed (Zhu et al., 2017) using logistic regression (LR) and is currently used by the U.S. Geological Survey as the preferred liquefaction model to map liquefaction probability immediately after the occurrence of earthquake events. This research proposes an ensemble modeling approach to improve the performance of the GGLM-2017 for geospatial liquefaction modeling of the 2023 Türkiye earthquakes using an updated inventory of liquefaction occurrence locations in Europe (the OpenLIQ database, which includes prior events occurring in Türkiye) and a new inventory from the 2023 Türkiye earthquakes (gathered from multiple sources). Using the same geospatial proxies for soil saturation, soil density, and earthquake loading, and the same non-liquefaction sampling strategy used to develop GGLM-2017, the proposed ensemble method is validated on the data of the 2023 Türkiye earthquakes by integrating four models, including global (GGLM-2017), continental (LR model trained on eight European events), regional (LR model trained on three historical events in Türkiye), and event-specific (LR model trained on partially available data from the 2023 Türkiye earthquakes) models. The inventory from the 2023 Türkiye earthquakes is split into two batches, in which the first batch (163 liquefaction occurrences) resulted from the preliminary reconnaissance and is used for training the event-specific model, and the second batch (284 liquefaction occurrences) resulted from a more complete reconnaissance (which was made available later) and is used for validating all models. The rationale for using the first batch for training the event-specific model is to exploit the information as they become available to optimize the performance of global model in liquefaction prediction. The final ensemble probability assignment is done by averaging the probabilities derived by the four individual models, and a 50% threshold is used for classification accuracy evaluations. Comparative analysis of the ensemble model’s performance with the GGLM-2017 showed improved predictive accuracy, resulting in higher liquefaction detection for the specific event under study (the 2023 Türkiye earthquakes). The ensemble model also provides an estimate of model uncertainty.