Seismic interpretation in the age of big data

Interpretation of seismic reflection data routinely involves powerful multiple-central-processing-unit computers, advanced visualization techniques, and generation of numerous seismic data types and attributes. All of this information, “Big Data”, indicates that seismic interpretation has entered the age of data deluge, where the amount of data has outgrown the capabilities to efficiently and effectively be analyzed. Machine learning has evolved to handle Big Data. This incorporates the use of computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. Principal component analysis (PCA) and self-organizing maps (SOM) are components of unsupervised machine learning methods that have evolved to help interpreters sift through enormous amounts of data and gain a better understanding of the geology. Employing these approaches and visualizing SOM results utilizing 2D color maps reveal geologic features not previously identified or easily interpreted from the seismic data. Presentation Date: Monday, October 17, 2016 Start Time: 4:10:00 PM Location: 163/165 Presentation Type: ORAL