Implementation of Objects Recognition in Seismic Image via Artificial Neural Network (ANN)

Seismic image processing is necessary in oil and gas exploration to identify the
existence of potential reservoir by classifying the seismic image into different
sections. These sections, also known as objects made up of different patterns which
portraying the structure of subsurface. This project aims to develop a data mining
algorithm embedded in a system that has ability to recognize the objects of channel
and fault in seismic image. The method chosen is artificial neural network (ANN)
which consists of input layer, hidden layer and output layer. Each layer is made up
of numbers of neuron nodes to receive input data from preceding layers and output
value to next layer until final output is determined from output layer. The ANN is
trained and tested via MATLAB Neural Network Pattern Recognition Toolbox
(nprtool) and MATLAB Neural Network Toolbox (nntool). 2-dimension (2D)
seismic image is converted into gray scale image via MATLAB Image Processing
Toolbox (imtool) and Grey-level co-occurrence matrix (GLCM) which serve as
input to the ANN is retrieved from the gray scale image. Result is displayed by the
system informing user whether the input image is channel, fault or neither both.