Speaker: Amandine Fratani

Date: Thursday 26th of June 2025, 1:15pm

Abstract:

The construction of geological models in sedimentary basins is largely constrained by the interpretation of faults and horizons in seismic and drillhole data and by associating observations into distinct entities (e.g., forming a single fault or one horizon). Due to the sparsity and incompleteness of data, several fault networks can usually be drawn from a given set of observations. This problem has been considered using graph formalism with nodes carrying the fault observation and the edges carrying information on the potential that they are associated. This potential has previously been proposed to be computed using machine learning, specifically the application of a Random Forest. However, the lack of open access structural models limits the use of machine learning. Therefore, this methodology has only been tested on partially interpreted cases. To generalise the approach, this work presents a database under development comprising synthetic structural models featuring normal faults. A random geological history and model generation code, Noddy, has been modified to include more realistic fault events. Faults are grouped into families where fault from a family have similar orientation for fault surfaces. Each family is defined as a mean dip and a mean dip direction, select randomly. A fault orientation is defined by sampling a Kent distribution centred on the dip and dip direction of the family to which it belongs. The resulting geological models are imported into geological modelling software where the surfaces are smoothed. Fault observations are then sampled in these models and will be used to train a Random Forest to retrieve the potential associations.