Data processing of reflection seismic data by use of neural network

1996 ◽  
Vol 35 (2-3) ◽  
pp. 89-98 ◽  
Author(s):  
Yuzuru Ashida
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Reflection Seismic

Attenuation of random noise using denoising convolutional neural networks

2019 ◽  
Vol 7 (3) ◽  
pp. SE269-SE280
Author(s):  
Xu Si ◽  
Yijun Yuan ◽  
Tinghua Si ◽  
Shiwen Gao
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Random Noise ◽  
Training Data ◽  
Prediction Errors ◽  
Network Learning ◽  
Deep Network ◽  
Residual Image ◽  
And Training

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.

Deep learning Q inversion from reflection seismic data with strong attenuation using an encoder-decoder convolutional neural network: an example from South China Sea

2020 ◽  
Author(s):  
Hao Zhang ◽  
Jianguang Han ◽  
Heng Zhang ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Attenuation Effect ◽  
Attenuation Model ◽  
Reflection Seismic ◽  
Q Model ◽  
Segmentation Task

<p>The seismic waves exhibit various types of attenuation while propagating through the subsurface, which is strongly related to the complexity of the earth. Anelasticity of the subsurface medium, which is quantified by the quality factor Q, causes dissipation of seismic energy. Attenuation distorts the phase of the seismic data and decays the higher frequencies in the data more than lower frequencies. Strong attenuation effect resulting from geology such as gas pocket is a notoriously challenging problem for high resolution imaging because it strongly reduces the amplitude and downgrade the imaging quality of deeper events. To compensate this attenuation effect, first we need to accurately estimate the attenuation model (Q). However, it is challenging to directly derive a laterally and vertically varying attenuation model in depth domain from the surface reflection seismic data. This research paper proposes a method to derive the anomalous Q model corresponding to strong attenuative media from marine reflection seismic data using a deep-learning approach, the convolutional neural network (CNN). We treat Q anomaly detection problem as a semantic segmentation task and train an encoder-decoder CNN (U-Net) to perform a pixel-by-pixel prediction on the seismic section to invert a pixel group belongs to different level of attenuation probability which can help to build up the attenuation model. The proposed method in this paper uses a volume of marine 3D reflection seismic data for network training and validation, which needs only a very small amount of data as the training set due to the feature of U-Net, a specific encoder-decoder CNN architecture in semantic segmentation task. Finally, in order to evaluate the attenuation model result predicted by the proposed method, we validate the predicted heterogeneous Q model using de-absorption pre-stack depth migration (Q-PSDM), a high-resolution depth imaging result with reasonable compensation is obtained.</p>

SC-PSNET: A deep neural network for automatic P- and S-phase detection and arrival-time picker using 1C recordings

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. U87-U98
Author(s):  
Jing Zheng ◽  
Jerry M. Harris ◽  
Dongzhuo Li ◽  
Badr Al-Rumaih
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Arrival Time ◽  
Time Lapse ◽  
S Phase ◽  
Training Data ◽  
Phase Detector ◽  
Phase Detection ◽  
Seismic Data Processing

It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of [Formula: see text] ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.

Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network

2020 ◽  
Vol 221 (2) ◽  
pp. 1211-1225 ◽  
Author(s):  
Y X Zhao ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Negative Impact ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Seismic Data Processing ◽  
Underground Structures ◽  
Seismic Records

SUMMARY One of the difficulties in desert seismic data processing is the large spectral overlap between noise and reflected signals. Existing denoising algorithms usually have a negative impact on the resolution and fidelity of seismic data when denoising, which is not conducive to the acquisition of underground structures and lithology related information. Aiming at this problem, we combine traditional method with deep learning, and propose a new feature extraction and denoising strategy based on a convolutional neural network, namely VMDCNN. In addition, we also build a training set using field seismic data and synthetic seismic data to optimize network parameters. The processing results of synthetic seismic records and field seismic records show that the proposed method can effectively suppress the noise that shares the same frequency band with the reflected signals, and the reflected signals have almost no energy loss. The processing results meet the requirements of high signal-to-noise ratio, high resolution and high fidelity for seismic data processing.

Unsupervised physics-based neural networks for seismic migration

2019 ◽  
Vol 7 (3) ◽  
pp. SE189-SE200 ◽  
Author(s):  
Janaki Vamaraju ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Seismic Data ◽  
Minimum Energy ◽  
Machine Learning Algorithms ◽  
Hopfield Neural Networks ◽  
Seismic Data Processing ◽  
Forward Models ◽  
The Neural Network

We have developed a novel framework for combining physics-based forward models and neural networks to advance seismic processing and inversion algorithms. Migration is an effective tool in seismic data processing and imaging. Over the years, the scope of these algorithms has broadened; today, migration is a central step in the seismic data processing workflow. However, no single migration technique is suitable for all kinds of data and all styles of acquisition. There is always a compromise on the accuracy, cost, and flexibility of these algorithms. On the other hand, machine-learning algorithms and artificial intelligence methods have been found immensely successful in applications in which big data are available. The applicability of these algorithms is being extensively investigated in scientific disciplines such as exploration geophysics with the goal of reducing exploration and development costs. In this context, we have used a special kind of unsupervised recurrent neural network and its variants, Hopfield neural networks and the Boltzmann machine, to solve the problems of Kirchhoff and reverse time migrations. We use the network to migrate seismic data in a least-squares sense using simulated annealing to globally optimize the cost function of the neural network. The weights and biases of the neural network are derived from the physics-based forward models that are used to generate seismic data. The optimal configuration of the neural network after training corresponds to the minimum energy of the network and thus gives the reflectivity solution of the migration problem. Using synthetic examples, we determine that (1) Hopfield neural networks are fast and efficient and (2) they provide reflectivity images with mitigated migration artifacts and improved spatial resolution. Specifically, the presented approach minimizes the artifacts that arise from limited aperture, low subsurface illumination, coarse sampling, and gaps in the data.

DEVELOPMENTS IN SEISMIC DATA PROCESSING AND ANALYSIS (1968–1970)

Geophysics ◽  
1971 ◽  
Vol 36 (6) ◽  
pp. 1043-1073 ◽  
Author(s):  
William A. Schneider
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Model Building ◽  
Processing Parameters ◽  
Subject Area ◽  
Two Dimensions ◽  
Seismic Data Processing ◽  
Seismic Processing ◽  
Reflection Seismic ◽  
Stratigraphic Model

The subject matter of this review paper pertains to developments during the past several years in the area of reflection seismic data processing and analysis. While this subject area is extensive in both its breadth and scope, one indisputable fact emerges: the computer is now more pervasive than ever. Processing areas which were computer intensive, such as signal enhancement, are now even more so; and those formerly exclusive domains of man, such as seismic interpretation, are beginning to feel the encroachment of the large number crunchers. What the future holds is anyone’s guess, but it is quite probable that man and computer will share the throne if the interactive seismic processing systems on the drawing boards come to pass. For the present and recent past, however, the most intensively developed areas of seismic data processing and analysis include 1) computer extraction of processing parameters such as stacking velocity and statics, 2) automated detection and tracking of reflections in multidimensional parameter space to provide continuous estimates of traveltime, amplitude, moveout (velocity), dip, etc., 3) direct digital migration in two dimensions, giving improved subsurface “pictures” and utilizing diffraction energy normally lost by specular processing techniques, and 4) development of quantitative understanding of the limitations imposed by current seismic processing practice and assumptions with regard to structural and stratigraphic model building, and recognition of the ultimate need for an iterative signal processing—information extraction—model building closed loop system.

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