Random field-based regional liquefaction hazard mapping — data inference and model verification using a synthetic digital soil field

Abstract

Geostatistical tools and random field models have been increasingly used in recent liquefaction mapping studies. However, a systematic verification and assessment of random field models has yet to be taken, and implications of various random field-based mapping approaches are unknown. In this paper, an extremely detailed three-dimensional synthetic digital soil field is artificially generated and used as a basis for assessing and verifying various random field-based models for liquefaction mapping. Liquefaction hazard is quantified in terms of the liquefaction potential index (LPI), which is mapped over the studied field. A classical CPT-based liquefaction model is adopted to assess liquefaction potential of a soil layer. Different virtual field investigation plans are designed to assess the dependency of data inference and model performance upon the level of availability of sampling data. Model performances are assessed using three information theory-based measures. Results show that when sampling data is sufficient, all random field-based models examined capture fairly well the benchmark liquefaction potentials in the studied field. As the size of the sampling data decreases, the accuracy of predictions decreases for all models but to different degrees; the three-dimensional random field model gives the best result in this scenario. All random field-based models examined in this paper yield a slightly more conservative prediction of liquefaction potential over the studied field.