A method for predicting fault activity of a subsurface volume, comprising: obtaining, from the subsurface volume and using a sensory device, a measurement of the subsurface volume; obtaining a model of the subsurface volume based on a plurality of linearly independent far field stress tensors; identifying a plurality of faults in the subsurface volume; and generating a plurality of pre-computed results for each of the plurality of linearly independent far field stress tensors, wherein each of the plurality of pre-computed results corresponds to sole contribution from one of the plurality of faults in the subsurface volume; the method characterized by selectively combining, for each of the plurality of linearly independent far field stress tensors and using a fault activity Boolean vector, the plurality of pre-computed results as a linearly independent contribution to a superpositioned result, wherein each linearly independent contribution is scaled in the superpositioned result by an optimization parameter associated with a corresponding one of the plurality of linearly independent far field stress tensors, and wherein the fault activity Boolean vector defines a selection of active faults of the plurality of faults and inactive faults of the plurality of faults; calculating a cost function representing a difference between the superpositioned result and the measurement of the subsurface volume; minimizing the cost function by iteratively adjusting the optimization parameter for each of the plurality of linearly independent far field stress tensors and iteratively adjusting the fault activity Boolean vector, wherein iteratively adjusting the fault activity Boolean vector iteratively changes the selection of active faults of the plurality of faults and inactive faults of the plurality of faults, and wherein iteratively adjusting the fault activity Boolean vector to minimize the cost function generates a prediction of the fault activity of the subsurface volume; and controlling a field operation associated with the subsurface volume based on the prediction. obtaining, from the subsurface volume and using a sensory device, a measurement of the subsurface volume; obtaining a model of the subsurface volume based on a plurality of linearly independent far field stress tensors; identifying a plurality of faults in the subsurface volume; and generating a plurality of pre-computed results for each of the plurality of linearly independent far field stress tensors, wherein each of the plurality of pre-computed results corresponds to sole contribution from one of the plurality of faults in the subsurface volume; the method characterized by selectively combining, for each of the plurality of linearly independent far field stress tensors and using a fault activity Boolean vector, the plurality of pre-computed results as a linearly independent contribution to a superpositioned result, wherein each linearly independent contribution is scaled in the superpositioned result by an optimization parameter associated with a corresponding one of the plurality of linearly independent far field stress tensors, and wherein the fault activity Boolean vector defines a selection of active faults of the plurality of faults and inactive faults of the plurality of faults; calculating a cost function representing a difference between the superpositioned result and the measurement of the subsurface volume; minimizing the cost function by iteratively adjusting the optimization parameter for each of the plurality of linearly independent far field stress tensors and iteratively adjusting the fault activity Boolean vector, wherein iteratively adjusting the fault activity Boolean vector iteratively changes the selection of active faults of the plurality of faults and inactive faults of the plurality of faults, and wherein iteratively adjusting the fault activity Boolean vector to minimize the cost function generates a prediction of the fault activity of the subsurface volume; and controlling a field operation associated with the subsurface volume based on the prediction.