Ground roll is a persistent problem in land seismic data. This type of coherent noise often contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data. A variety of methods for addressing ground-roll attenuation have been developed. However, existing methods are limited, especially when using real land seismic data. For example, when ground roll and reflections overlap in the time or frequency domains, traditional methods cannot completely separate them and they often distort the signals during the suppression process. We have developed a generative adversarial network (GAN) to attenuate ground roll in seismic data. Unlike traditional methods for ground-roll attenuation dependent on various filters, the GAN method is based on a large training data set that includes pairs of data with and without ground roll. After training the neural network with the training data, the network can identify and filter out any noise in the data. To fulfill this purpose, the proposed method uses a generator and a discriminator. Through network training, the generator learns to create the data that can fool the discriminator, and the discriminator can then distinguish between the data produced by the generator and the training data. As a result of the competition between generators and discriminators, generators produce better images whereas discriminators accurately recognize targets. Tests on synthetic and real land seismic data show that the proposed method effectively reveals reflections masked by the ground roll and obtains better results in the attenuation of ground roll and in the preservation of signals compared to the three other methods.
Migration‐based location methods (e.g., time‐reverse imaging based on wave equation, Kirchhoff summation, and diffraction stacking) can effectively locate events of low signal‐to‐noise ratios by stacking waveforms from many receivers. The methods have been widely applied for surface microseismic monitoring. However, these methods may not produce accurate results if there are polarity reversals in the surface records for a double‐couple or even a general moment tensor event. Various imaging conditions have been developed to solve the non‐focus image problem for a non‐explosive source. Here, we propose a deep convolutional neural network to predict a better‐focused image from a regular migration image that contains a quasi‐symmetric pattern in both space and time. To train the network, we first simulate a large number of surface records from sources with various locations and mechanisms. We then compute diffraction stacking images from the records and take the images as the input to the network. We define the corresponding training labels as images (with the same size as the input) with Gaussian distributions centered at the true sources. This network, trained by only synthetic datasets, works well in field data to detect source locations from images for unknown events. Both synthetic tests and field data applications demonstrate that the proposed method can effectively improve diffraction stacking images for efficient microseismic location.
Random noise often contaminates seismic data and reduces its signal-to-noise ratio. Therefore, the removal of random noise has been an essential step in seismic data processing. The [Formula: see text]-[Formula: see text] predictive filtering method is one of the most widely used methods in suppressing random noise. However, when the subsurface structure becomes complex, this method suffers from higher prediction errors owing to the large number of different dip components that need to be predicted. Here, we used a denoising convolutional neural network (DnCNN) algorithm to attenuate random noise in seismic data. This method does not assume the linearity and stationarity of the signal in the conventional [Formula: see text]-[Formula: see text] domain prediction technique, and it involves creating a set of training data that are obtained by data processing, feeding the neural network with the training data obtained, and deep network learning and training. During deep network learning and training, the activation function and batch normalization are used to solve the gradient vanishing and gradient explosion problems, and the residual learning technique is used to improve the calculation precision, respectively. After finishing deep network learning and training, the network will have the ability to separate the residual image from the seismic data with noise. Then, clean images can be obtained by subtracting the residual image from the raw data with noise. Tests on the synthetic and real data demonstrate that the DnCNN algorithm is very effective for random noise attenuation in seismic data.
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