Event-based regional model of liquefaction susceptibility in Amik, Gölbaşı and Kahramanmaraş Basins after the February 6, 2023 Earthquakes

The February 6, 2023 Kahramanmaraş-Türkiye earthquakes with moment magnitudes 7.7 and 7.6 resulted in substantial casualties, injuries and extensive infrastructure devastation. Soil liquefaction was identified as one of the contributing factors to the damages. In this study, a data-driven approach to assess liquefaction-prone areas within an artificial neural network (MultiLayer Perceptron - MLP) was proposed. The study area, selected to cover a region with the size of 11,500 km2 containing Amik and Kahramanmaraş Plains, is governed mainly by active tectonism of the East Anatolian Fault Zone. The earthquakes were considered to be responsible for numerous liquefaction occurrences in both plains. Here, a comprehensive inventory of liquefied regions was compiled from aerial photogrammetric images taken in the days following the earthquakes. Considering the availability of suitable geospatial datasets, the key factors for liquefaction modeling were selected as distance to streams, land use and land cover, slope, and topographic wetness index, and normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from satellite images taken a few days before the earthquakes. The Holocene unit was used as a mask to perform modeling and prediction within this lithological type. The F1-score and overall accuracy values obtained from the MLP on a test dataset were 80% and 82%, respectively. The study showed that geospatial databases including airborne and satellite image products have great potential for assessing liquefaction hazard at regional scale, which can be used as base data for planning and conducting further field and laboratory studies to improve the accuracy in predictions. •6 February 2023 Kahramanmaraş Earthquakes (Mw 7.7- and 7.6) caused extensive damage.•Liquefaction in loose cohesionless soils contributed to resulting damage.•An extensive liquefaction inventory with 803 areas was compiled.•Regional liquefaction susceptibility map was developed for an area of 11,500 km2.•An overall accuracy of 82% was achieved using an Artificial Neural Network.