  Compared with traditional seismic noise attenuation algorithms that depend on
signal models and their corresponding prior assumptions, removing noise with a
deep neural network is trained based on a large training set, where the inputs
are the raw datasets and the corresponding outputs are the desired clean data.
After the completion of training, the deep learning method achieves adaptive
denoising with no requirements of (i) accurate modelings of the signal and
noise, or (ii) optimal parameters tuning. We call this intelligent denoising.
We use a convolutional neural network as the basic tool for deep learning. In
random and linear noise attenuation, the training set is generated with
artificially added noise. In the multiple attenuation step, the training set is
generated with acoustic wave equation. Stochastic gradient descent is used to
solve the optimal parameters for the convolutional neural network. The runtime
of deep learning on a graphics processing unit for denoising has the same order
as the $f-x$ deconvolution method. Synthetic and field results show the
potential applications of deep learning in automatic attenuation of random
noise (with unknown variance), linear noise, and multiples.
