Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Zhou, Hao-Miao; Guo, Yangqin; Guo, Kun-Yi

doi:10.3390/min11101089

Cited by 4 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, x is the original seismic signal, b is the added Gaussian white noise, sigma is the variance, also known as noise level, and y is the noisy seismic signal. Because the noise contained in seismic signals is mostly random noise, the method of adding noise is to add Gaussian white noise with different noise levels (variances), just as papers [47,48] also adopt the same strategy.…”

Section: Interpolation and Denoising Experiments Simultaneouslymentioning

confidence: 99%

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Ding,

Zhou,

Chi

2024

Remote Sensing

View full text Add to dashboard Cite

In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data loss. The unprocessed state of these seismic signals significantly jeopardizes the interpretative phase. Evidently, the integration of attention mechanisms and the utilization of generative adversarial networks (GANs) have emerged as prominent techniques within signal processing owing to their adeptness in discerning intricate global dependencies. Our research introduces a pioneering approach for reconstructing and denoising seismic signals, amalgamating the principles of self-attention and generative adversarial networks—hereafter referred to as SAGAN. Notably, the incorporation of the self-attention mechanism into the GAN framework facilitates an enhanced capacity for both the generator and discriminator to emulate meaningful spatial interactions. Subsequently, leveraging the feature map generated by the self-attention mechanism within the GAN structure enables the interpolation and denoising of seismic signals. Rigorous experimentation substantiates the efficacy of SAGAN in simultaneous signal interpolation and denoising. Initially, we benchmarked SAGAN against prominent methods such as UNet, CNN, and Wavelet for the concurrent interpolation and denoising of two-dimensional seismic signals manifesting varying levels of damage. Subsequently, this methodology was extended to encompass three-dimensional seismic data. Notably, performance metrics reveal SAGAN’s superiority over comparative methods. Specifically, the quantitative tables exhibit SAGAN’s pronounced advantage, with a 3.46% increase in PSNR value over UNet and an impressive 11.90% surge compared to Wavelet. Moreover, the RMSE values affirm SAGAN’s robust performance, showcasing an 11.54% reduction in comparison to UNet and an impressive 29.27% decrement relative to Wavelet, hence unequivocally establishing the SAGAN method as a preeminent choice for seismic signal recovery.

show abstract

Section: Interpolation and Denoising Experiments Simultaneouslymentioning

confidence: 99%

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Ding,

Zhou,

Chi

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Dong et al [134] proposed a multiscale spatial attention denoising network to tell weak reflected signals apart from strong seismic background noise. Zhou et al [135] developed a deep denoising convolutional autoencoder network based on self-supervised learning to attenuate seismic random noise. In terms of waveform extraction, Xu et al [40] proposed an automatic P-wave onset time picking method for mining-induced microseismic data based on a long short-term memory deep neural network.…”

Section: Seismic Data Processingmentioning

confidence: 99%

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Jia,

2023

Remote Sensing

View full text Add to dashboard Cite

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields.

show abstract

Reinforcement Learning-Based Denoising Model for Seismic Random Noise Attenuation

Liang

Lin

Haitao

2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Cited by 4 publications

References 47 publications

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Reinforcement Learning-Based Denoising Model for Seismic Random Noise Attenuation

Contact Info

Product

Resources

About