In this work, we present a novel, automated method for detecting geobodies in 3D seismic
reflection data, helping to reduce interpreter bias and speed up seismic interpretation.
A seismic geobody refers to a geometrical, structural, or stratigraphic feature, such as
a channel, turbidite fan, or igneous intrusion. Geobodies are subtle seismic features,
hard to pick, and their detection is challenging to automate due to their complex 3D
geomorphology and diversity of shapes. Nevertheless, the detection and delineation of
these structures are essential for improving the understanding of the subsurface as well
as building a variety of conceptual models.
In our approach, we can rapidly interpret large 3D seismic volumes using point cloud-based segmentation to identify geobodies of interest, including complex stratigraphic
features like lobes and channels. By converting the 3D seismic cube into a 3D seismic
point cloud (sparse cube), we reduce the volume of data to analyse, which in turn speeds
up the detection process. First, we build the 3D point clouds by filtering the seismic
reflection volume using different seismic attributes, and then each point in the cloud is
segmented into different clusters. The clustering is performed using the unsupervised
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) which allows
the segmentation of all structures present into delineated objects. The clustered objects
can then be characterised by features based on their 3D shape and spatial amplitude
distribution. Finally, our method allows the selection of a specific geobody and can
retrieve geobodies based on their similarity to exploration targets of interest.
The method has been applied successfully to two modern 3D seismic datasets (Falkland
Basins) and two types of geobodies: fans and sill intrusions. We demonstrate that our
method can scan through a large 3D seismic volume and automatically retrieve likely
fan and sill geobodies in a very efficient manner. This approach can be used to scan
through large volumes of 3D seismic, looking for a wide variety of geobodiesJames Watt Scholarshi
