A method for detecting an unknown fault (236-8) in a target seismic volume , comprising: obtaining seismic data corresponding to a subterranean formation ; generating a plurality of patches (233-3, 233-7) from a training seismic volume that is separate from the target seismic volume, wherein the target seismic volume comprises the seismic data and wherein a patch of the plurality of patches comprises a set of training areas (233-4, 233-5); generating a label (233-6) for assigning to the patch, wherein the label represents a subset, of the set of training areas, intersected by a known fault specified by a user in the training seismic volume; training , during a training phase and based at least on the label and the training seismic volume, a machine learning model ; and generating , by applying the machine learning model to the target seismic volume during a prediction phase subsequent to the training phase, a result to identify the unknown fault in the target seismic volume, wherein the result is used to facilitate drilling, fracturing or other exploratory and/or production field operations characterized in that : applying the machine learning model to the target seismic volume comprises: placing, for an iteration of a plurality of iterations, a sliding window (236-3, 236-7) over a seismic section (236-1, 236-2) of the seismic data to generate a partial result (238-1, 238-2), the partial result comprises at least one selected from a group consisting of a fault score and a fault detection mask, and wherein the machine learning model comprises: a first convolution neural network (ConvNet) for generating the fault score; and a second ConvNet , based on at least one intermediate layer of the first ConvNet, for generating the fault detection mask. obtaining seismic data corresponding to a subterranean formation ; generating a plurality of patches (233-3, 233-7) from a training seismic volume that is separate from the target seismic volume, wherein the target seismic volume comprises the seismic data and wherein a patch of the plurality of patches comprises a set of training areas (233-4, 233-5); generating a label (233-6) for assigning to the patch, wherein the label represents a subset, of the set of training areas, intersected by a known fault specified by a user in the training seismic volume; training , during a training phase and based at least on the label and the training seismic volume, a machine learning model ; and generating , by applying the machine learning model to the target seismic volume during a prediction phase subsequent to the training phase, a result to identify the unknown fault in the target seismic volume, wherein the result is used to facilitate drilling, fracturing or other exploratory and/or production field operations characterized in that : applying the machine learning model to the target seismic volume comprises: placing, for an iteration of a plurality of iterations, a sliding window (236-3, 236-7) over a seismic section (236-1, 236-2) of the seismic data to generate a partial result (238-1, 238-2), the partial result comprises at least one selected from a group consisting of a fault score and a fault detection mask, and wherein the machine learning model comprises: a first convolution neural network (ConvNet) for generating the fault score; and a second ConvNet , based on at least one intermediate layer of the first ConvNet, for generating the fault detection mask. a first convolution neural network (ConvNet) for generating the fault score; and a second ConvNet , based on at least one intermediate layer of the first ConvNet, for generating the fault detection mask. <EOS>