Reconstruction of Sparsely Sampled Seismic Data via Residual U-Net

Tang, Shuhang; Ding, Yinshuai; Zhou, Hua‐wei; Zhou, Heng

doi:10.1109/lgrs.2020.3035835

Cited by 12 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the performance of the U-Net. Tang et al [100] added residual blocks to the deep U-Net, a model mainly used to process seismic data acquired through sparse 2D acquisition and named SRT2D-ResU-Net, and the reconstructed missing trajectories correlated with the true answers by more than 85\% on average. Huang et al [101] trained a nested U-Net structure with mixed loss functions (U-Net++) to automatically generate pseudo labels to simulate continuous missing scenes by randomly masking the observed data so that local and global structural information can be captured to ensure reconstruction quality.…”

Section: Seismic Data Interpolation and Denoisingmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

show abstract

Section: Seismic Data Interpolation and Denoisingmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning techniques have emerged as a powerful tool for seismic images restoration. They have been used for missing seismic traces reconstruction in pre-stack [11,12,13] and post-stack [11,13] domains, and to a less extent in missing seismic shots reconstruction [14,15]. Nevertheless, the selection of missing seismic data follows non-data-dependent sensing schemes such as uniform, random, or pseudo-random (e.g., jitter [16]), which do not evaluate the optimal location of missing sources according to the quality of the subsequent reconstructed seismic data.…”

Section: Introductionmentioning

confidence: 99%

3D Geometry Design via End-To-End Optimization for Land Seismic Acquisition

Hernandez-Rojas

Argüello

2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Seismic acquisition is important in the exploration of the subsurface to find new petroleum fields, where a regularspaced dense acquisition is critical to obtain high-quality seismic images. However, the acquisition costs and environmental impacts have motivated undersampled acquisition schemes, where several sensing points are removed to decrease the total seismic sources. After the undersampled measurements are acquired, a recovery algorithm reconstructs the missing seismic images (shots). The removed sources are currently selected using random sensing schemes, leading to suboptimal quality in the recovered seismic images. Thus, an optimal design of the removed sources is crucial as it determines the quality of the recovered shots. This work proposes an end-to-end optimization to jointly design undersampled seismic acquisition geometries while preserving the highquality of the reconstructed data. The seismic acquisition geometry is modeled as a deep binary layer to learn the optimal sensing pattern, while a deep neural network is used to recover the underlying removed shots. Extensive simulations were carried out on a realistic-synthetic Foothills model. The results obtained on the reconstructed data validate that the proposed acquisition design outperforms the state-of-the-art random, uniform, and jitter sensing schemes in 4 dB.

show abstract

Convolutional Neural Network-Assisted Least-Squares Migration

Zhou

2023

Surv Geophys

View full text Add to dashboard Cite

Reconstruction of Sparsely Sampled Seismic Data via Residual U-Net

Cited by 12 publications

References 15 publications

Recent Advances of Deep Learning in Geological Hazard Forecasting

Recent Advances of Deep Learning in Geological Hazard Forecasting

3D Geometry Design via End-To-End Optimization for Land Seismic Acquisition

Convolutional Neural Network-Assisted Least-Squares Migration

Contact Info

Product

Resources

About