Well-Logging Constrained Seismic Inversion Based on Closed-Loop Convolutional Neural Network

Wang, Yuqing; Ge, Qiang; Lu, Wenkai; Yan, Xiangqiao

doi:10.1109/tgrs.2020.2967344

Cited by 96 publications

(11 citation statements)

References 43 publications

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“…A common deep learning algorithm, convolutional neural network (CNN), is a feed forward neural network that has great feature extract ability. Many CNN-based deep learning methods have been applied for seismic exploration fields, such as seismic event detection (Wu H. et al, 2019;Yang et al, 2021), first-arrival picking (Wu Y. et al, 2019;Yuan et al, 2020;Guo et al, 2021) and seismic inversion (Feng, 2020;Wang et al, 2020;Aleardi and Salusti, 2021). Furthermore, it is also an effective tool for seismic noise suppression.…”

Section: Introductionmentioning

confidence: 99%

Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network

Huang

Jensen³

2023

Front. Earth Sci.

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In recent years, distributed optical fiber acoustic sensing (DAS) technology has been increasingly used for vertical seismic profile (VSP) exploration. Even though this technology has the advantages of high spatial resolution, strong resistance to high temperature and pressure variations, long sensing distance, DAS seismic noise has expanded from random noise to optical abnormal noise, fading noise and horizontal noise, etc. This seriously affects the quality of the seismic data and brings huge challenges to subsequent imaging, inversion and interpretation. Moreover, the noise is more complex and more difficult to simultaneously suppress using traditional methods. Therefore, for the purpose of effectively improving the signal-to-noise ratio (SNR) of DAS seismic data, we introduce a denoising network named attention-guided denoising convolutional neural network (ADNet). The network is composed of four blocks, including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB). The network uses different kinds of convolutions alternately to enlarge the receptive field size and extract global feature of the input. Meanwhile, the attention mechanism is introduced to extract the hidden noise information in the complex background. The network predicts the noise, and denoised data are obtained by subtracting the predicted results from the noisy inputs. In addition, we uniquely construct a large number of complex forward models for pure seismic data training set to enhance the network suitability. The combination design improves the denoising performance and reduces computational cost and memory consumption. The results obtained from both synthetic- and field data illustrate that the network has the ability to denoise the seismic images and retrieve weak effective signals better than conventional methods and common networks.

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Section: Introductionmentioning

confidence: 99%

Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network

Huang

Jensen³

2023

Front. Earth Sci.

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“…To further reduce the reliance on labeled data, they use Sequential Gaussian Co-Simulation and Elastic Distortion algorithms to generate adequate and diverse pre-stack seismic inversion datasets. Similarly, Wang et al (2020) proposed a closed-loop CNN structure with a U-Net network as the main body to make CNN less dependent on the amount of labeled data in seismic inversion. The proposed closed-loop CNN can simulate both seismic forward and inversion processes from the training dataset.…”

Section: Introductionmentioning

confidence: 99%

Multichannel seismic impedance inversion based on Attention U-Net

Ning

Li²,

Ma³

et al. 2023

Front. Earth Sci.

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Recently, seismic inversion has made extensive use of supervised learning methods. The traditional deep learning inversion network can utilize the temporal correlation in the vertical direction. Still, it does not consider the spatial correlation in the horizontal direction of seismic data. Each seismic trace is inverted independently, which leads to noise and large geological variations in seismic data, thus leading to lateral discontinuity. Given this, the proposed method uses the spatial correlation of the seismic data in the horizontal direction. In the network training stage, several seismic traces centered on the well-side trace and the corresponding logging curve form a set of training sample pairs for training, to enhance the lateral continuity and anti-noise performance. Additionally, Attention U-Net is introduced in acoustic impedance inversion. Attention U-Net adds attention gate (AG) model to the skip connection between the encoding and decoding layers of the U-Net network, which can give different weights to different features, so the model can focus on the features related to the inversion task and avoid the influence of irrelevant data and noise during the inversion process. The performance of the proposed method is evaluated using the Marmousi2 model and the SEAM model and compared with other methods. The experimental results show that the proposed method has the advantages of high accuracy of acoustic impedance value inversion, good transverse continuity of inversion results, and strong anti-noise performance.

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“…Neural network (NN) and its variation forms (e.g., deep/convolutional/recurrent NN) are becoming more powerful in pattern recognition, image processing and image segmentation for large-scale data. Deep NNs have shown effectiveness in picking first arrivals from raw seismic data [27], improve seismic image resolution by approximating a Hessian matrix inverse [28] and FWI inverted velocity models by using well-log information [29], [30], [31]. Convolutional NN (CNN) has strong capabilities in extracting features from a large number of images, and it has been effectively applied to detect salt bodies [32], horizons, and faults from seismic images [33], predict low-frequency components from highfrequency data [34].…”

Section: Introductionmentioning

confidence: 99%

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

Song

Alkhalifah

2022

IEEE Trans. Geosci. Remote Sensing

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Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented by solving for the frequency-domain representation of the wavefield using the finite-difference method. The approach requires matrix inversions and affords limited flexibility to accommodating irregular model geometries. On the other hand, physics-informed neural network (PINN) uses the underlying physical laws as loss functions to train the neural network (NN) to provide flexible continuous functional approximations of the solutions without matrix inversions. By including a dataconstrained term in the loss function, the trained NN can reconstruct a wavefield that simultaneously fits the recorded data and satisfies the Helmholtz equation for a given initial velocity model. Using the predicted wavefields, we rely on a small-size NN to predict the velocity using the reconstructed wavefield. In this velocity prediction NN, spatial coordinates are used as input data to the network and the scattered Helmholtz equation is used to define the loss function. After we train this network, we are able to predict the velocity in the domain of interest. We develop this PINN-based WRI method and demonstrate its potential using a part of the Sigsbee2A model and a modified Marmousi model. The results show that the PINN-based WRI is able to invert for a reasonable velocity with very limited iterations and frequencies, which can be used in a subsequent FWI application.

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Well-Logging Constrained Seismic Inversion Based on Closed-Loop Convolutional Neural Network

Cited by 96 publications

References 43 publications

Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network

Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network

Multichannel seismic impedance inversion based on Attention U-Net

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

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