Data-Driven, Reliable Translation of Shear-Wave Velocity to CPT Cone-Tip Resistance Using Machine Learning

Abstract

The absence of information on lateral variability in the soil is detrimental to estimating accurately the local site response in the event of an earthquake. To address this problem, the use of densely sampled seismic data together with sparsely distributed but detailed vertical soil profiles obtained from cone penetration tests (CPTs) is advantageous. This study explores the adaptation of suitable machine learning (ML) approaches to derive reliable, site- and depth-specific correlations between seismic shear-wave velocity (Vs) and cone-tip resistance (qc). Such correlation could be successfully established by combining information from seismic CPT surveys with available borehole information for the Groningen region in the Netherlands. It is found that, even over substantial distances, ML-based techniques offer site- and depth-specific correlations between Vs and qc.