|Title||Machine Learning Based Automated Fault Detection in Seismic Traces|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Zhang, C, Frogner, C, Araya-Polo, M, Hohl, D|
|Conference Name||EAGE Conference and Exhibition 2014|
|Conference Location||The Netherlands|
The 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 human intervention (interpreter) and seismic data processing with in complex workflows. The interpreters ensure that any additions or corrections made by seismic processing are compliant with geological and geophysical knowledge. The information that seismic processing adds to the model consists of structural elements, faults are one of the most relevant of those events since they can signal reservoir boundaries or hydrocarbon traps. Faults are excluded in the initial models 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 (Admasu and Toennies (2004); Tingdahl et al. (2001); Cohen et al. (2006); Hale (2013), etc). Our approach suggests an additional tool that can be used to speed up the
Fully automated VMB has not been achieved because the human factor is difficult to formalize in a way that can be systematically applied. Nonetheless, if our framework is extended to other seismic events or attributes, it might become a powerful tool to alleviate interpreters’ work.
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