Optimization-inspired deep learning high-resolution inversion for seismic data

Chen, Hongling; Gao, Jinghuai; Jiang, Xiudi; Gao, Zhaoqi; Zhang, Wei

doi:10.1190/geo2020-0034.1

Cited by 29 publications

(6 citation statements)

References 33 publications

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“…In the formula: u ji is the oscillation coefcient of the resonant tank; w i is the jump parameter; and s i is the switching radian period [12,13]. Assuming that the two-way resonance class transformation feature sample set of the robot electrical equipment is d k , the load range Ψ(ω) of the faulty node of the robot electrical equipment is the following formula :…”

Section: Sample Data Collectionmentioning

confidence: 99%

Robot Fault Detection Based on Big Data

Luo

2023

Journal of Control Science and Engineering

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In order to improve the reliability of robot electrical fault detection and diagnosis, the author proposes a robot electrical fault detection and diagnosis method based on deep learning. Taking the return power and active power as constraints, the electrical fault data collection of the robot is carried out. Taking the resonant inductance and resonant capacitance of the robot electrical equipment as identification parameters, we conduct electrical fault differential feature mining. The fault features are extracted according to the time-delay distribution sequence of the electrical fault data of the robot, and the electrical fault detection and diagnosis results are output by using the deep learning function. Simulation results show that the author's method has a high accuracy probability for robot electrical fault diagnosis. The author's method is on average 14.7% higher than the neural network-based method and 24.5% higher than the expert system-based method. The accuracy rate of the author's method for robot electrical fault diagnosis is high. The author’s method is 16.6% higher than the neural network-based method on average and 34.2% higher than the expert system-based method. It is proved that the robot electrical fault detection and diagnosis based on deep learning has high accuracy and short time.

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Section: Sample Data Collectionmentioning

confidence: 99%

Robot Fault Detection Based on Big Data

Luo

2023

Journal of Control Science and Engineering

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“…It still needs to be further adjusted to actually match the needs of residents. Chen proposed an optimization-inspired deep learning inversion solver to quickly solve various problems encountered in analyzing public service data [ 7 ]. However, in terms of the actual layout of social facilities for sports, his method lacks certain guidance, and it needs to cooperate with the algorithms studied by other predecessors to achieve better results.…”

Section: Related Workmentioning

confidence: 99%

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Huang

2022

Computational Intelligence and Neuroscience

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The spatial layout and optimization of social facilities for sports are related to many factors such as urban economy, transportation, population, and urban planning. With the rapid development of artificial intelligence today, deep learning came into being. It also provides a lot of methods for our in-depth research on it. To solve the problem of unbalanced layout of urban social facilities for sports and the difficulty in meeting the needs of residents under the current background, this paper adopts the methods of M-P model, loss function, and activation function. Taking Hangzhou, a sports city that is about to host the Asian Games, as an example, it carried out an experiment to optimize the layout of urban public sports services. Through various tests and data, it divides the accessibility level of social facilities for sports in Hangzhou into four levels: high accessibility point (1.0-1.5), high accessibility point (0.5-1.0), the accessibility is average (0.2-0.5), and the accessibility is poor (<0.2). It also concluded that the areas with high accessibility (1.0-1.5) are Shangcheng District and Binjiang District, and the layout of sports facilities is optimal. According to the minimum facility point model, Xiaoshan District and Yuhang District need to add 6 large-scale social facilities for sports. Through this experiment, this paper can also provide a reference for other urban layouts. This has significance for the optimization of sports facilities and the optimization of public facilities.

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“…Finally, Chen et al. (2021) and Chen et al. (2021) propose supervised and semi‐supervised blind methods, respectively, in which they simultaneously invert the seismic wavelet and a high‐resolution post‐stack model.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Deep decomposition learning for reflectivity inversion

Torres

Sacchi

2023

Geophysical Prospecting

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We report a combination of classical regularization theory with a null space neural network approach based on deep decomposition learning, paying particular attention to the solution of one ubiquitous problem in seismic exploration: the recovery of full‐band reflectivity from band‐limited seismic traces. The method extends the popular post‐processing approach by learning how to improve an initial reconstruction with estimated missing components from the null space of the forward operator, which in our case, are the missing frequency components of the reflectivity. We integrate the null space element prediction to act in conjunction with convolutional neural network based denoising and a data‐consistent algorithm. The proposed framework honours the input measurements while enforcing generalization. Numerical experiments on synthetic and real datasets show that the proposed method naturally enforces a high‐resolution prediction consistent with the low‐resolution input seismic traces. We compare its performance with state‐of‐the‐art thin‐bed reflectivity estimation methods.

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Optimization-inspired deep learning high-resolution inversion for seismic data

Cited by 29 publications

References 33 publications

Robot Fault Detection Based on Big Data

Robot Fault Detection Based on Big Data

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning

Deep decomposition learning for reflectivity inversion

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