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Random noise suppression can greatly improve the signal-to-noise ratio (SNR) of seismic signals. To suppress random seismic noise, we propose an unsupervised deep neural network (UDNN) method based on the local cross correlation (LCC) loss function and ensemble learning. UDNN with two base learners mainly consists of one input data, two deep neural networks (DNNs), and two output data. The proposed UDNN based on the LCC loss function and ensemble learning (UDNN-LCCEL) method takes full advantage of the nonlinear mapping capabilities of DNNs and maps noisy data into effective noise-free signals by minimizing the total loss function. The total loss function includes the LCC and mean-absolute-error (MAE) loss functions. LCC calculates the local correlation or orthogonality between output data and the removed noise. By reducing the value of the LCC loss function, we automatically reduce the residual noise and leakage of effective signals in the output data, thus avoiding the overfitting of UDNN. UDNN-LCCEL combines the advantages of two DNNs to obtain ensemble denoised results by ensemble learning. The biggest advantage of our proposed UDNN-LCCEL method is that it does not need to use noise-free data as label data, which can well solve the problem of missing training datasets. The synthetic and field data examples are used to demonstrate that UDNN-LCCEL can achieve good random noise suppression effectiveness.

Internal multiples seriously affecting seismic inversion and imaging need to be suppressed. To suppress internal multiples, we propose an unsupervised deep learning method based on a local orthogonalization constraint (UDL-LOC). The unsupervised deep learning (UDL) consists of one multiattention-based U-net (MA-net), two input data, and one output data. The predicted internal multiples (PIMs), obtained by the adaptive virtual event (AVE) method, and the original data are used as input data. UDL utilizes the excellent nonlinear optimization capability of MA-net to transform PIMs into the estimated true internal multiples that are the output data of UDL. Finally, the de-multiple result can be obtained by subtracting the output data from the original data. The proposed UDL-LOC uses the local orthogonalization constraint (LOC) function as part of the total loss function. The LOC function can help UDL to correctly map PIMs into the true internal multiples with the right amplitudes and phases and prevent the output data from containing residual primaries or leaked internal multiples. Our proposed UDL-LOC method does not require true primaries or true internal multiples as the training dataset. Therefore, our proposed method has a wide range of applications and can solve the problem of missing training datasets. We use the synthetic and land field data examples to demonstrate that our proposed method has a good internal multiple suppression effect.