Fast geostatistical seismic inversion coupling machine learning and Fourier decomposition

Nunes, Rúben; Azevedo, Leonardo; Soares, Amílcar

doi:10.1007/s10596-019-09877-w

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…These optimization methods aim to minimize the error between synthetic and observed seismic data. These methods produce smooth models but the uncertainties about the predicted values are not assessed [8]. Most commonly used deterministic inversion methods are band limited recursive inversion [9], colored inversion [10] and sparse spike inversion methods [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network With Attention Module for Seismic Impedance Inversion

Dodda,

Kuruguntla,

Mandpura

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Seismic inversion is an approach to obtain the physical properties of the earth layers from the seismic data which aids in reservoir characterization. In seismic inversion, spatially variable physical parameters such as impedance (Z), wave velocities (Vp, Vs) and density can be determined from the seismic data. Among these, impedance is an important parameter used for lithology interpretation. However, the inversion problem is nonlinear and ill-posed due to unknown seismic wavelet, observed data band limitation, and noise. This requires complex wave equation analysis, prior assumptions, human expert effort, and time to analyze the seismic data. To address these issues, deep learning methods were deployed to solve the seismic inversion problem. In this paper, we develop a deep learning framework with an attention module for seismic impedance inversion. The relevant features from the seismic data are emphasized with the integration of the attention module into the network. First, we train the attention-based deep convolutional neural network (ADCNN) by supervised learning with pre-defined acoustic impedance (AI) labels. Next, we train the ADCNN in an unsupervised way with the physics of the forward problem. In the proposed method, the predicted AI is used to calculate the seismic data (calculated seismic) and error is minimized between the input seismic data and calculated seismic data. Unsupervised learning has an advantage when the labeled data is inadequate. The proposed network is trained with Marmousi 2 dataset, and the predicted experimental results show that the proposed method outperforms in comparison to the existing state-of-the-art method.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network With Attention Module for Seismic Impedance Inversion

Dodda,

Kuruguntla,

Mandpura

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Another viable strategy to mitigate the curse of dimensionality and to reduce the computational complexity of high‐dimensional inverse problems is to compress the model space through appropriate reparameterization techniques (Fernández‐Martínez et al ., 2011; Azevedo et al ., 2016; Aleardi, 2019; Szabó and Dobróka, 2019; Numes et al ., 2019; Aleardi 2020b). However, it should be noted that the parameterization of an inverse problem must always constitute a compromise between model resolution and model uncertainty (Grana et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Vinciguerra

Aleardi

Hojat

et al. 2021

Geophysical Prospecting

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Electrical resistivity tomography is a non‐linear and ill‐posed geophysical inverse problem that is usually solved through gradient‐descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two‐dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high‐dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non‐linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high‐frequency components of the resistivity model which are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un‐reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient‐based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification.

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“…Traditional strategies for seismic impedance inversion includes full waveform inversion, AVO inversion and functional optimization [6,21,31]. Meanwhile, seismic impedance inversion can be seen as a kind of supervised learning problem in machine learning, and a lot research has applied various machine learning algorithm in seismic impedance inversion, including linear and nonlinear regression [29] and symbolic regression [28].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training

Ge¹,

Wang²,

Zheng³

2021

Preprint

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Seismic impedance inversion can be performed with a semi-supervised learning algorithm, which only needs a few logs as labels and is less likely to get overfitted. However, classical semi-supervised learning algorithm usually leads to artifacts on the predicted impedance image. In this artical, we improve the semi-supervised learning from two aspects. First, by replacing 1-d convolutional neural network (CNN) layers in deep learning structure with 2-d CNN layers and 2-d maxpooling layers, the prediction accuracy is improved. Second, prediction uncertainty can also be estimated by embedding the network into a Bayesian inference framework. Local reparameterization trick is used during forward propagation of the network to reduce sampling cost. Tests with Marmousi2 model and SEAM model validate the feasibility of the proposed strategy.

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Fast geostatistical seismic inversion coupling machine learning and Fourier decomposition

Cited by 16 publications

References 31 publications

Deep Convolutional Neural Network With Attention Module for Seismic Impedance Inversion

Deep Convolutional Neural Network With Attention Module for Seismic Impedance Inversion

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training

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