Generation of 3D subsurface stratigraphic model using boreholes located outside the target site

Developing 3D stratigraphic models for a target site has been a long-standing challenge due to limited boreholes within the target site, a situation often encountered in engineering practice. Although several machine learning methods have been developed recently for 3D stratigraphic modelling by incorporating prior geological knowledge to supplement existing borehole data, they still face considerable challenges when the number of boreholes within a target site is extremely limited (e.g. less than three). In engineering practice, boreholes may be available outside the target site, which can offer valuable stratigraphic information to supplement borehole data within the target site. In this study, a data-driven generative framework is proposed for constructing 3D stratigraphic models using boreholes outside the target site. A recently developed machine learning method, called multi-scale generative adversarial network (MS-GAN), is employed to model subsurface stratigraphy and quantify uncertainty. An enhancement is made to MS-GAN for effectively handling irregularly distributed boreholes. The proposed framework is validated through a set of boreholes in Hong Kong. The results show that the proposed method successfully utilises boreholes outside the target site to generate reliable 3D stratigraphic models with quantified uncertainty.