Fault detection using neural network

Abstract

Fault detection technique using neural networks have been successfully applied to a seismic data volume. This technique is basically creating a volume that highlights faults by combining the information from several fault indicators attributes (i.e. similarity, curvature and energy) into fault occurrence probability. This is performed by training a neural network on two sets of attributes extracted at sample locations picked manually - one set represents the fault class and the other represents the non-fault class. The next step is to apply the trained artificial neural network on the seismic data. Result indicates that faults are more highlighted and have better continuity since the surrounding noise are mostly suppressed.

Keywords

• fault detection
• multi-attribute analysis
• neural networks
• fault cube.