Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Yang, Yuxin; Zhang, Xitong; Guan, Qiang; Lin, Youzuo

doi:10.1109/tgrs.2022.3144636

Cited by 16 publications

(9 citation statements)

References 44 publications

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“…A variational autoencoder with physics-informed regularization (shown in Fig. 4a) was designed to synthesize realistic velocity maps [31]. Domain adaptation is another technique that was shown to be effective to overcome the data scarcity issue.…”

Section: ) Simulations and Data Augmentationmentioning

confidence: 99%

Physics-Guided Data-Driven Seismic Inversion: Recent Progress and Future Opportunities in Full Waveform Inversion

Lin

Theiler

Wohlberg

2022

Preprint

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The goal of seismic inversion is to obtain subsurface properties from surface measurements. Seismic images have proven valuable, even crucial, for a variety of applications, including subsurface energy exploration, earthquake early warning, carbon capture and sequestration, estimating pathways of sub-surface contaminant transport, etc. These subsurface properties˜(such as wave speed, density, impedance, and reflectivity) influence the transmission of seismic waves through the subsurface media, and wellunderstood physics models (so-called "forward models") can be used to predict what surface measurements would be made, for any given subsurface configuration. Seismic inversion is the inverse problem: given actual surface measurements, infer what

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Section: ) Simulations and Data Augmentationmentioning

confidence: 99%

Physics-Guided Data-Driven Seismic Inversion: Recent Progress and Future Opportunities in Full Waveform Inversion

Lin

Theiler

Wohlberg

2022

Preprint

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“…A variational auto-encoder with physics-informed regularization (shown in Fig. 4a) was designed to synthesize realistic velocity maps [23]. Domain adaptation is another technique that was shown to be effective to overcome the data scarcity issue.…”

Section: B Machine Learning Models Incorporating Physicsmentioning

confidence: 99%

Physics-Informed Data-Driven Seismic Inversion: Recent Progress and Future Opportunities

Lin

Theiler

Wohlberg

2022

Preprint

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“…As an essential step for traffic surveillance and maritime rescue, object detection has experienced tremendous progress [1][2][3][4][5][6][7][8]. This is not only due to the powerful representation ability of deep neural networks but it is also reliant on massive training data [9][10][11][12]. Unfortunately, most training data suffer from the heavily imbalanced ratio of large objects to small objects.…”

Section: Introductionmentioning

confidence: 99%

Value-Guided Adaptive Data Augmentation for Imbalanced Small Object Detection

Wang,

Sui,

Jiang

et al. 2024

Electronics

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Data augmentation is considered a promising technique to resolve the imbalance of large and small objects. Unfortunately, most existing methods augment all small objects indiscriminately, regardless of their learnability and proportion. This tends to result in wasteful enlargement for many weak, low-information objects but under-augmentation for rare and learnable objects. To this end, we propose a value-guided adaptive data augmentation for scale- and proportion-imbalanced small object detection (ValCopy-Paste). Specifically, we first develop a non-learning object value criteria to determine whether one object should be expanded. Both scale-based learnability and quantity-based necessity are involved in this criteria. Then, the value distribution of objects in the dataset can be further constructed on the basis of the relevant object values. This helps to ensure that those uncommon, learnable objects that deserve enhancement are more likely to be enhanced. Additionally, we propose to enhance the data by pasting the sampled objects into relatively smooth portions of fresh background images, rather than arbitrary areas of any background images. This helps to boost data diversity while reducing the interference from complicated backgrounds. Evidently, our method does not require sophisticated training and just depends on the size and distribution of the objects in the dataset. Extensive experiments on MS COCO 2017 and PASCAL VOC 2012 demonstrate that our method achieves better performance than state-of-the-art methods.

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Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Cited by 16 publications

References 44 publications

Physics-Guided Data-Driven Seismic Inversion: Recent Progress and Future Opportunities in Full Waveform Inversion

Physics-Guided Data-Driven Seismic Inversion: Recent Progress and Future Opportunities in Full Waveform Inversion

Physics-Informed Data-Driven Seismic Inversion: Recent Progress and Future Opportunities

Value-Guided Adaptive Data Augmentation for Imbalanced Small Object Detection

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