A Method for Denoising Seismic Signals With a CNN Based on an Attention Mechanism

Yan, Shuang; Long, Yun; Fu, Ronghao; Huang, Xingguo; Lin, Jun; Li, Zhe

doi:10.1109/tgrs.2022.3215819

Cited by 3 publications

(1 citation statement)

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“…Where w ij L is the connection weight from the i-th neuron in the (L-1)-th layer to the j-th neuron in the L-th layer and I represents the number of neurons in (L-1)-th layer; z j L reveals the result of activation layers. The softmax function can effectively evaluate the compounded probabilities, providing more reliable and accurate recognition results compared with traditional methods [35]. Therefore, the output layer of the model utilizes softmax function to generate the recognition results in terms of a probability distribution.…”

Section: Principle Of Variational Modal Decompositionmentioning

confidence: 99%

Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

Jiang

Chen

Xing

2023

Preprint

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Recently, the application of deep convolutional neural networks (DCNN) in underwater acoustic communication has become a growing research hotspot. However, it is hindered by the large amounts of training data required to achieve a complex communication system. In this paper, we propose a novel approach of combining variational mode decomposition (VMD) and DCNN networks to address this issue. Considering filter-bank multicarrier (FBMC) technologies offer promising performance in terms of spectral efficiency and robustness to interference, and the Bellhop model is also capable of providing effective and accurate communication data, this study adopts FBMC technologies for signal modulation and Bellhop models for communication data. The results show that the VMD-DCNN equalizer has a lower BER compared with the other seven contrast methods, thus further promoting the application and development of VMD-DCNN technologies in underwater acoustic communications.

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Section: Principle Of Variational Modal Decompositionmentioning

confidence: 99%

Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

Jiang

Chen

Xing

2023

Preprint

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Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

Chen,

Ma,

Wang

et al. 2024

Earth Sci Inform

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The integration of machine learning (ML) and deep learning (DL) into geoscience has experienced a pronounced uptick in recent years, a trend propelled by the intricate nature of geosystems and the abundance of data they produce. These computational methods have been harnessed across a spectrum of geoscientific challenges, from climate modeling to seismic analysis, exhibiting notable efficacy in extracting valuable insights from intricate geological datasets for applications such as mineral prediction. A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms. The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”. Despite these advancements, other methodologies have garnered comparatively lesser focus. While ML and DL have registered successes in the realm of mineral prediction, their amalgamation with domain-specific knowledge and symbolic reasoning could further amplify their interpretability and operational efficiency. Neuro-Symbolic AI (NSAI) emerges as a cutting-edge approach that synergizes DL’s robust capabilities with the precision of symbolic reasoning, facilitating the creation of models that are both powerful and interpretable. NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks. This literature review delves into the promising potential of NSAI, alongside ML and DL, within the geoscientific domain, spotlighting mineral prediction as a key area of focus. Despite the hurdles associated with infusing domain expertise into symbolic formats and mitigating biases inherent in symbolic reasoning, the application of NSAI in the realm of critical mineral prediction stands to catalyze a paradigm shift in the field. By bolstering prediction accuracy, enhancing decision-making processes, and fostering sustainable resource exploitation, NSAI holds the potential to significantly reshape geoscience’s future trajectory.

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Enhanced Seismic Attenuation Compensation: Integrating Attention Mechanisms With Residual Learning in Neural Networks

Wang,

Shi,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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A Method for Denoising Seismic Signals With a CNN Based on an Attention Mechanism

Cited by 3 publications

References 0 publications

Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

Enhanced Seismic Attenuation Compensation: Integrating Attention Mechanisms With Residual Learning in Neural Networks

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