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<p class="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 anddoi:10.20961/jphystheor-appl.v1i1.4718 fatcat:g3rwolj6nzbxnb54qpcihydy44