An integrated machine learning-based fault classification workflow
Jie Qi, Carolan Laudon, Kurt J. Marfurt · 2022
The paper introduces an integrated machine learning-based fault classification workflow that creates fault component classification volumes that greatly reduces the burden on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using a 3D convolutional neural network (CNN). In addition to faults, the resulting “fault probability” volume delineates other non-fault edges such as angular unconformities, the base of mass transport complexes, and noise such as acquisition footprint. We find that image processing-based fault discontinuity enhancement and skeletonization methods enhances fault discontinuities and suppresses many non-fault discontinuities. Although each fault is characterized by its dip and azimuth, these two properties are discontinuous at azimuths of φ=±180° and for near vertical faults for azimuths φ and φ+180° requiring them to be parameterized as four continuous geodetic fault components. These four fault components as well as the fault probability can then be processed a self-organizing map (SOM) to generate a fault component classification. We find that the SOM classification results segment fault sets trending allowing selection of interpreter-defined orientations and further isolation of stratigraphy and minimizing noise.