SoilGen: A Comprehensive Tool for Generating Synthetic Soil Profiles for Geotechnical and Seismic Analysis

Abstract: Geotechnical and seismic applications, ranging from site response analysis and HVSR simulations to dispersion curve modeling, increasingly depend on large, well-labeled datasets for robust model development. However, the scarcity of publicly available borehole datasets, coupled with the proprietary nature of high-quality field records, creates a significant bottleneck for data-driven research, particularly in machine learning. To address this limitation, this study introduces SoilGen, an open-source framework that procedurally generates physically consistent multilayer soil columns as synthetic soil profiles. Unlike simple randomization, SoilGen computes a complete suite of geotechnical properties, including layer thickness, shear-wave velocity, P-wave velocity, density, and Poisson ratio, while enforcing physical constraints to ensure realism. The algorithmic foundations of the framework and its implementation are outlined, and its utility is demonstrated through representative near-surface geological scenarios relevant to site characterization and near-surface geophysics. By facilitating the rapid generation of large-scale model libraries exceeding one hundred thousand realizations, SoilGen enables comprehensive parametric studies and the training of deep learning inversion networks that require extensive labeled datasets for shear-wave velocity profiling and related site characterization tasks.