Seismic data processing algorithms greatly benefit from regularly sampled and reliable data. Therefore, interpolation and denoising play a fundamental role as one of the starting steps of most seismic processing workflows. We exploit convolutional neural networks for the joint tasks of interpolation and random noise attenuation of 2D common shot gathers. Inspired by the great contributions achieved in image processing and computer vision, we investigate a particular architecture of convolutional neural network referred to as U-net, which implements a convolutional autoencoder able to describe the complex features of clean and regularly sampled data for reconstructing the corrupted ones. In training phase we exploit part of the data for tailoring the network to the specific tasks of interpolation, denoising and joint denoising/interpolation, while during the system deployment we are able to recover the remaining corrupted shot gathers in a computationally efficient procedure. We consider a plurality of data corruptions in our numerical experiments, including different noise models and different distributions of missing traces. Several examples on synthetic and field data illustrate the appealing features of the aforementioned strategy. Comparative examples show improvements with respect to recently proposed solutions for joint denoising and interpolation.

Interpolation and Denoising of Seismic Data using Convolutional Neural Networks.

In seismic exploration, random noise suppression is one of the key problems in seismic data processing. For random noise attenuation, the most important thing is the understanding of seismic random noise generation and propagation. Seismic random noise is considered as temporal and spatial random processes, and it can be analyzed only qualitatively for now, due to its high variability. In this paper, we classify seismic random noise sources by their generation factors and simulate the random noise of the desert in West China. According to Green’s function, it can be assumed that seismic random noise sources are point-like sources that are distributed around geophones. A seismic random noise record is taken as the superimposed wave field exited by all the independent sources in a homogeneous isotropy half-infinite surface. Based on the wind vibration theory and preliminary study about ambient vibrations, the noise source functions are determined. We obtain the waveforms of different kinds of noise by solving the inhomogeneous wave equations and analyze the characteristics qualitatively and quantitatively. The seismic synthetic record with a 1.6-s time and 250-m distances is obtained, and the characteristics are compared between the simulated and the real noise record in time domain and space domain, respectively. The comparative results show the same characteristics of the simulated noise and the real noise, which demonstrates the feasibility of the proposed method. According to the noise modeling, it is known that the near-field cultural noise is the main component of the random noise in the desert, on the basis of which complex diffusion filtering is selected. The filtered results by complex diffusion filtering is compared with the results of time–frequency peak filtering, which is a popular filtering method of seismic random noise suppression in recent years. The comparative results show that complex diffusion filtering is more suitable for the noise of the desert in the Tarim Basin. This result proves that seismic random noise modeling can provide the guidance for noise attenuation. It lays a foundation for researching the propagation characteristics and better attenuation of seismic random noise in the future.

Seismic Exploration Random Noise on Land: Modeling and Application to Noise Suppression

The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.

/pdf/imbalanced-learning-in-land-cover-classification-improving-3viku14yez.pdf

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

The dreamlet (drumbeat-beamlet) transform can provide us an efficient method to represent physical wavefield, because the dreamlet basis satisfies automatically the wave equation, which is a distinctive feature different from mathematical basis, such as Fourier and curvelet. It can obtain an estimation of true signal from the observed noisy data by abandoning those insignificant components in the dreamlet domain. However, we have found that a more accurate estimation can be achieved by a damped version of the dreamlet representation. We have theoretically derived the damped dreamlet representation and given its geometric interpretation and analysis. Two applications of the proposed method have been explored in this paper: seismic random noise suppression and seismic data interpolation. Various examples demonstrate that the damped dreamlet representation-based technique has a superior performance compared with the mathematical-basis-based representation and rank-reduction-based techniques.

Damped Dreamlet Representation for Exploration Seismic Data Interpolation and Denoising

Time-frequency peak filtering (TFPF) is a classical filtering method in time-frequency domain. It applies Wigner-Ville distribution to estimate the instantaneous frequency of an analytical signal. There is a pair of contradiction in this method, i.e., selecting a short window length may lead to good preservation for signal amplitude but bad random noise reduction whereas selecting a long window length may lead to serious attenuation for signal amplitude but effective random noise reduction. In order to make a good tradeoff between valid signal amplitude preservation and random noise reduction, we adopt empirical mode decomposition (EMD) to improve the TFPF results. The new idea is to utilize the decomposition characteristic of EMD which decomposes a signal to several modes from high to low frequency and to take advantage of the time-frequency filtering characteristic of TFPF which can recognize the valid signal component in the time-frequency plane in order to achieve effective random noise reduction together with good amplitude preservation. Through some experiments on synthetic seismic models and field seismic records, we show the better performance of the new method compared with the conventional TFPF.

An Amplitude-Preserved Time–Frequency Peak Filtering Based on Empirical Mode Decomposition for Seismic Random Noise Reduction

The time-frequency peak filtering (TFPF) is an effective tool in random-noise attenuation and has been applied to seismic record denoising in recent years. The window length (WL) of the time-frequency distribution (TFD) is the key to the conventional TFPF technology. A fixed WL is not optimal for both the low- and high-frequency components at the same time; an adaptive WL results in serious distortion of the reflected waveform. In this letter, we discuss a modified TFPF along the radial-trace direction and prove its advantage in TFD window selection. Experiments on both synthetic models and field data show that the radial-trace TFPF result is no longer much influenced by the WL as the conventional TFPF. Furthermore, it can provide better performance in both random-noise attenuation and reflected signal preservation with a fixed WL.

Noise Attenuation for 2-D Seismic Data by Radial-Trace Time-Frequency Peak Filtering

In seismic exploration, random noise is an obstacle to the extraction of the effective signals, so the investigation aimed at random noise is the basis of signal processing. It is of great significance to analyze the noise properties and establish accurate noise models. Since the complex changes of the actual medium seriously affect propagation characteristics, it is necessary to establish a noise model in a more realistic medium. In this letter, we suppose a weakly heterogeneous medium whose properties vary with the position. And the link between the Lam constants of the medium and noise properties is established. Therefore, a wave equation is deduced in that medium to describe the propagation law of desert seismic exploration random noise. Based on the Greens function, the random noise field is obtained by superimposing all wave fields excited by each pointlike source. Afterward, quantitative comparisons between the actual random noise and the proposed random noise model are given. The results manifest that there are significant similarities in mathematical characteristics between them. Moreover, compared with the noise model in the homogeneous medium, the proposed noise model is more reliable. In order to prove the application value of the random noise model, it is first applied to construct a complete training set for denoising convolutional neural networks, which is valuable for attenuating the desert seismic exploration random noise. This is an effective way to extend noise data. Consequently, this feasible application will strongly promote the application of neural networks in seismic exploration.

Modeling Land Seismic Exploration Random Noise in a Weakly Heterogeneous Medium and the Application to the Training Set

The modelling technique contributes to understanding noise nature and properties. Prior work has established a primary random noise model in the homogeneous medium, however, this strict assumption ...

Modelling the seismic exploration random noise based on the perturbation method and its application

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Noise Attenuation for 2-D Seismic Data by Radial-Trace Time-Frequency Peak Filtering

Modeling Land Seismic Exploration Random Noise in a Weakly Heterogeneous Medium and the Application to the Training Set

Modelling the seismic exploration random noise based on the perturbation method and its application