Self-Attention Fully Convolutional DenseNets for Automatic Salt Segmentation

Saad, Omar M.; Chen, Wei; Zhang, Fangxue; Yang, Liuqing; Zhou, Xu; Chen, Yangkang

doi:10.1109/tnnls.2022.3175419

Cited by 15 publications

(2 citation statements)

References 36 publications

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“…1) F3 Netherlands: F3 is a very classical survey and many scholars use it for various geophysical and imaging studies [70], such as fault detection [5], [6], salt body detection [4], [71], seismic facies classification [72], seismic denoise [73] and seismic data reconstruction [74]. In which this data is presented in great detail in the work of Alaudah et al [72].…”

Section: Application Experimentsmentioning

confidence: 99%

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

Dou¹,

Li²,

Li³

et al. 2023

Preprint

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Recent studies have shown that learning theories have been very successful in hydrocarbon exploration. Inversion of seismic into various attributes through the relationship of 1D well-logs and 3D seismic is an essential step in reservoir description, among which, acoustic impedance is one of the most critical attributes, and although current deep learningbased impedance inversion obtains promising results, it relies on a large number of logs (1D labels, typically more than 30 well-logs are required per inversion), which is unacceptable in many practical explorations. In this work, we define acoustic impedance inversion as a regression task for learning sparse 1D labels from 3D volume data and propose a voxel-wise semisupervised contrastive learning framework, ContrasInver, for regression tasks under sparse labels. ConstraInver consists of several key components, including a novel pre-training method for 3D seismic data inversion, a contrastive semi-supervised strategy for diffusing well-log information to the global, and a continuous-value vectorized characterization method for a contrastive learning-based regression task, and also designed the distance TopK sampling method for improving the training efficiency. We performed a complete ablation study on SEAM Phase I synthetic data to verify the effectiveness of each component and compared our approach with the current mainstream methods on this data, and our approach demonstrated very significant advantages. In this data we achieved an SSIM of 0.92 and an MSE of 0.079 with only four well-logs. ConstraInver is the first purely data-driven approach to invert two classic field data, F3 Netherlands (only four well-logs) and Delft (only three well-logs) and achieves very reasonable and reliable results.

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Section: Application Experimentsmentioning

confidence: 99%

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

Dou¹,

Li²,

Li³

et al. 2023

Preprint

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show abstract

“…As for spatial domain features recognition, several CNN-based methods have been proposed for the recognition of spatial domain features, such as AlexNet [20], DenseNet [22], ResNet [23], and so on. Though they have achieved excellent recognition results, they were difficult to apply in practice due to the high time costs and great computing resources.…”

Section: Introductionmentioning

confidence: 99%

Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis

Fan,

Deng,

et al. 2023

Biomimetics

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Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.

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3D Salt-net: a method for salt body segmentation in seismic images based on sparse label

Xu,

Li,

et al. 2023

Appl Intell

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Self-Attention Fully Convolutional DenseNets for Automatic Salt Segmentation

Cited by 15 publications

References 36 publications

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis

3D Salt-net: a method for salt body segmentation in seismic images based on sparse label

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