Machine-learning Based Automated Fault Detection in Seismic Traces

Initial stages of velocity model building (VMB) start off from smooth models that capture geological assumptions of the subsurface region under analysis. Acceptable velocity models result from successive iterations of interpretation and seismic data processing. The interpreters ensure that additions/ corrections made by seismic processing are compliant with geological and geophysical knowledge. Seismic processing adds to the model structural elements, faults are one of the most relevant of those events since they can signal reservoir boundaries or hydrocarbon traps.

Initial models exclude faults due to their local scale. Bringing faults into the model in early stages can help to steer the VMB process. This work introduced a tool whose purpose is to assist the interpreters during the initial stages of the VMB, when no seismic data has been migrated. Our novel method is based on machine learning techniques and can automatically identify and localize faults from not migrated seismic data.

Comprehensive research has targeted the fault localization problem, but most of the results are obtained using processed seismic data or images as input. Our approach suggests an additional tool that can be used to speed up the VMB process. Also, if our framework is extended to other seismic events it might become a powerful tool to alleviate interpreters’ work.