Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with numerous processing versions, large populations of wells and associated data, and dozens if not hundreds of seismic attributes that routinely produce terabytes of data. 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. Multi-attribute analyses employing principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a self-organizing map, or SOM.

Approach Aids Multiattribute Analysis

Approach Aids Multiattribute Analysis

How Self-Orgazining Maps (SOM) and Principal Componenrt Analysis (PCA) greatly enhances the interpretation process to identify geology in diffferent settings. Geophysicists interpret multiple attributes of seismic data using principal component analysis and self-organizing maps of machine learning.