Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis
Lijing Wang, Luk Peeters, Emma MacKie, Zhen Yin, Jef Caers
Abstract
Modeling complex geological interfaces is a common task in geosciences. Many data sources are available for geological interface modeling, including borehole data and geophysical surveys. Geological knowledge, such as the delineation from geologists, is difficult to quantify but likely adds value to geological interface modeling. To integrate all information, we present a data-knowledge-driven trend surface analysis method to construct stochastic geological interfaces. We design a Metropolis–Hastings sampling framework to sample stochastic trend interfaces and quantify the uncertainty of geological interfaces given all information sources. This method is suitable for both explicit and implicit representations of geological interfaces. We demonstrate our method in three different test cases: modeling stochastic interfaces of Greenland subglacial topography, magmatic intrusion, and buried river valleys in Australia.