Seismic Volumetric Dip Estimation via Multichannel Deep Learning Model

Lou, Yihuai; Li, Shizhen; Li, Shengjun; Liu, Naihao; Zhang, Bo

doi:10.1109/tgrs.2022.3190911

Cited by 20 publications

(3 citation statements)

References 38 publications

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“…Liu et al [26] used fine-tuned FPN to achieve microseismic first-arrival pickup. Lou et al [27] proposed MCDL to achieve seismic volume dip estimation. Dou et al [28] used the "MDA GAN" of the adversarial network to realize 3D seismic data interpolation and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Zhao,

Han,

Zhang

et al. 2024

Applied Sciences

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In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent noise. These artifacts and coherent noise interfere with the valid information in the virtual shot record, making the virtual shot record a poorer presentation of subsurface information. In this paper, we utilize the powerful learning and data processing abilities of convolutional neural networks to process virtual shot recordings of sources in undesirable situations. We add an adaptive attention mechanism to the network so that it can automatically lock the positions that need special attention and processing in the virtual shot records. After testing, the trained network can eliminate coherent noise and artifacts and restore real reflected waves. Protecting valid signals means restoring valid signals with waveform anomalies to a reasonable shape.

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Section: Introductionmentioning

confidence: 99%

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Zhao,

Han,

Zhang

et al. 2024

Applied Sciences

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“…Koeshidayatullah 19 (2022) proposed an improved CNN model (through three strategies to improve model performance) for application in earth science. Lou et al 20 (2022) used deep learning to estimate the inclination of the formation and proposed a supervised deep learning model, which verified the effectiveness of the model by applying it to real seismic data. Mohammadi et al 21 (2022) used recurrent neural networks (RNNs), long short-term memory (LSTMs), deep belief networks (DBNs), and convolutional neural networks (CNNs) to estimate the solubility of nitrogen in unsaturated hydrocarbons, cyclic hydrocarbons, and aromatic hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

Echo State Neural Network Based on an Improved Gray Wolf Algorithm Predicts Porosity through Logging Data

Sun

Zhang

2023

ACS Omega

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In oil exploration and development, many reservoir parameters are very essential for reservoir description, especially porosity. The porosity obtained by indoor experiments is reliable, but human and material resources will be greatly invested. Experts have introduced machine learning into the field of porosity prediction but with the shortcomings of traditional machine learning models, such as hyperparameter abuse and poor network structure. In this paper, a meta-heuristic algorithm (Gray Wolf Optimization algorithm) is introduced to optimize the ESN (echo state neural) network for logging porosity prediction. Tent mapping, a nonlinear control parameter strategy, and PSO (particle swarm optimization) thought are introduced to optimize the Gray Wolf Optimization algorithm to improve the global search accuracy and avoid local optimal solutions. The database is constructed by using logging data and porosity values measured in the laboratory. Five logging curves are used as model input parameters, and porosity is used as the model output parameter. At the same time, three other prediction models (BP neural network, least squares support vector machine, and linear regression) are introduced to compare with the optimized models. The research results show that the improved Gray Wolf Optimization algorithm has more advantages than the ordinary Gray Wolf Optimization algorithm in terms of super parameter adjustment. The IGWO-ESN neural network is better than all machine learning models mentioned in this paper (GWO-ESN, ESN, BP neural network, least squares support vector machine, and linear regression) in terms of porosity prediction accuracy.

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“…D UE to the constraints on the acquisition condition, the cost limitations, and the dead traces, the received seismic data is often sampled [1], [2]. Sampled seismic data reconstruction is one of the key and tough tasks in seismic data processing [3], [4], which benefits further seismic data processing and interpretation, e.g., coherent and incoherent noise attenuation [5]- [9], geological structure characterization [10]- [12], attribute analysis [13]- [16], fault and horizon interpretation [17]- [19], lithology recognition [20]- [22], etc. The sampled seismic data can be mainly divided into randomly and successively sampled cases [23].…”

Section: Introductionmentioning

confidence: 99%

Fully Connected U-Net and Its Application on Reconstructing Successively Sampled Seismic Data

Gao

Gui

et al. 2023

IEEE Access

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One of the major hot topics in seismic data processing is the reconstruction of successively sampled seismic data. There are numerous traditional methods proposed for addressing this issue; however, they still have unavoidable drawbacks, such as high computational cost and sensitive tuning parameters. In this study, we suggest a deep learning model for reconstructing successively sampled seismic data, termed fully connected U-Net (FCU-Net). FCU-Net maintains the high-resolution representations by connecting the parallel different-resolution representations and repeating multi-scale fusion. Such a structure allows FCU-Net to successfully extract multi-scale information, which is beneficial for accurate seismic data reconstruction. Additionally, the extending subnetwork of FCU-Net contains a large number of feature channels and sufficient information interaction between different resolution representations via the composite cascades, which contributes to locating successively sampled traces with big gaps and then performing the seismic interpolation. To verify the effectiveness of FCU-Net, we compare it with state-of-the-art networks, i.e., U-Net and HRNet, using synthetic and field examples. The results show that FCU-Net performs best when interpolating successively sampled seismic data, proving its superiority and availability.

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Seismic Volumetric Dip Estimation via Multichannel Deep Learning Model

Cited by 20 publications

References 38 publications

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning

Echo State Neural Network Based on an Improved Gray Wolf Algorithm Predicts Porosity through Logging Data

Fully Connected U-Net and Its Application on Reconstructing Successively Sampled Seismic Data

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