Semi-Supervised Learning for Seismic Impedance Inversion Using Generative Adversarial Networks

Wu, Bangyu; Meng, Delin; Zhao, Hongjin

doi:10.3390/rs13050909

Cited by 67 publications

(21 citation statements)

References 38 publications

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“…erefore, the weight normalization method should be used to replace BN [29] in GAN training. A semisupervised learning workflow based on GAN for acoustic impedance inversion is proposed [30].…”

Section: Improvement Of Training Skills Several Stable Trainingmentioning

confidence: 99%

Generative Adversarial Network and Its Application in Energy Internet

Xiao

2022

Mathematical Problems in Engineering

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Energy Internet (EI) can provide consumers with flexible energy-sharing services. Recently, artificial intelligence (AI) technology has been widely used in the field of EI. Generative adversarial network (GAN) is one of the hottest research directions in the field of AI in recent years, and its excellent data generation ability has attracted wide attention. First, this paper introduces the framework, advantages, disadvantages, and improvement of classic GAN. Then, the application of GAN in the field of EI is reviewed. Finally, the paper is summarized, and the possible application of GAN in EI is prospected.

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Section: Improvement Of Training Skills Several Stable Trainingmentioning

confidence: 99%

Generative Adversarial Network and Its Application in Energy Internet

Xiao

2022

Mathematical Problems in Engineering

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“…However, it is well known that DL relies on significant amounts of data, so some of these works use semi-supervised methods. Wu et al used a semi-supervised method based on adversarial learning [26], [27], a prototype of which was first proposed by Wei [28], who trained the discriminator to distinguish between confidence maps from labeled and unlabeled data predictions. The method depends on having enough labels to ensure the stability of the Generative Adversarial Networks (GAN) [29], so the inversion of the SEAM Phase I (Fig.…”

Section: Introductionmentioning

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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“…To overcome the problems and restrictions of the machine learning methods, an alternative approach "semi-supervised" is explored in [20]. Semi-supervised learning is a special case of the machine learning methods, but cannot be completely considered under the umbrella of supervised learning [21]. Semi-supervised learning is a broad concept and has several functions in it to minimize the problems that the previous approaches to machine learning cannot overcome.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction and Analysis Using Enhanced Random Forest (extRF) Technique: A Business Process Management and Improvement Concept in IOT-Based Application Processing Environment

Alshammari

2022

Mobile Information Systems

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Software defect prediction is a thriving study area in the realm of software engineering and processing in the IOT-based environment. Defect prediction creates a list of defective source code artifacts so that quality assurance companies may successfully assign limited methods for certifying programming things by investing more effort into the bad source code. Defect prediction can assist estimate maintenance times, which can help with quality assurance, dependability, security, and cost reduction. Many predictions in IOT-based processing environment and business process management and enhancement challenges still exist in defect expectation ponders, and there are various noteworthy concerns. In addition, it is difficult to apply these methodologies practically because most of the investigations verified in open-source programming ventures with the goal that present forecast models might not work for other programming items including business programming. Investigating security issues in cross-project deformity expectation is required since if we have more accessible restrictive datasets, the assessment of forecast models will be more stable. In general, every defect is essential regarding quality, reliability, security, and cost-effectiveness. Therefore, an enhanced and improved maintenance schedule is required to acknowledge forecasting techniques. Therefore, in this article, we have evaluated different Semi-Supervised Learning (SSL) techniques, among which Extended Random Forest (extRF) technique is one for defective system prediction. The Extended Random Forest (extRF) technique is the extended form of the Random Forest (RF), which is a supervised learning technique into semi-supervised learning getting the hang of refining every arbitrary tree given an individual-training worldview. An enhancing technique is recommended, and a weighted mixture of irregular trees creates the final forecast results.

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Semi-Supervised Learning for Seismic Impedance Inversion Using Generative Adversarial Networks

Cited by 67 publications

References 38 publications

Generative Adversarial Network and Its Application in Energy Internet

Generative Adversarial Network and Its Application in Energy Internet

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

Software Defect Prediction and Analysis Using Enhanced Random Forest (extRF) Technique: A Business Process Management and Improvement Concept in IOT-Based Application Processing Environment

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