Machine learning models have been widely used by geoscientists to accelerate their interpretation and highlight hidden patterns in their data. However, as the complexity of the model increases, the interpretation of the results can become quite challenging. The SHAP technique provides a measure of the importance of each of the input seismic attributes on the model’s output. We illustrate the value of the SHAP technique using a tree-based machine learning implementation trained to distinguish between Mass Transport Deposits (MTDs) and salt seismic facies in a Gulf of Mexico survey.

Presentation Date: Monday, October 12, 2020

Session Start Time: 1:50 PM

Presentation Time: 3:55 PM

Location: 351F

Presentation Type: Oral

Keywords:
reservoir characterization, classification, machine learning, candidate seismic, interpretability, prediction, artificial intelligence, glcm entropy, machine learning model interpretability, application
Subjects:
Reservoir Characterization, Information Management and Systems, Seismic processing and interpretation, Artificial intelligence
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2020. Society of Exploration Geophysicists
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