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An anti-noise seismic inversion method based on diffusion model
Geophysics · Submitted 8 May 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results on the Marmousi2 model and field data demonstrate that the DSIM-USSL method achieves higher accuracy in the presence of seismic data with random noise, and maintains high stability even under strong noise conditions.
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The abstract, sentence by sentence
Seismic impedance inversion is one of the most important part of geophysical exploration. However, due to random noise, the traditional semi-supervised learning (SSL) methods lack generalization and stability. To solve this problem, some authors have proposed SSL methods with anti-noise function to improve noise robustness and inversion accuracy. However, such methods are often not ideal when faced with strong noise. In addition, Low-frequency impedance models can mitigate this problem, but creating accurate low-frequency models is difficult and error-prone when well-log data is sparse and subsurface structures are complex. To address those issues, we propose a novel deep learning inversion method called DSIM-USSL (Unsupervised and Semi-supervised joint Learning for Seismic Inversion based on diffusion model). Specifically, we are the first to introduce a diffusion model with strong noise tendency and construct a diffusion seismic inversion model (DSIM). In the reverse diffusion of DSIM, we design the encoder-decoder which combines CNN for capturing local features and GRU for global sequence modeling; and we choose U-net to learn the distribution of random noise, enhancing the generalization and stability of proposed method. Furthermore, to further improve generalization of the proposed method, a two-step training approach (USSL) is utilized. First, an unsupervised trained encoder-decoder is used as the initial network model in place of the traditional low-frequency wave impedance model that is difficult to accurately acquire. Then, the SSL is employed to further optimize the encoder-decoder model. Experimental results on the Marmousi2 model and field data demonstrate that the DSIM-USSL method achieves higher accuracy in the presence of seismic data with random noise, and maintains high stability even under strong noise conditions.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Seismic impedance inversion is one of the most important part of geophysical exploration. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | However, due to random noise, the traditional semi-supervised learning (SSL) methods lack generalization and stability. | background 0.79 | 0.06 | 0.02 | asserted 1.00 |
| s2 | To solve this problem, some authors have proposed SSL methods with anti-noise function to improve noise robustness and inversion accuracy. | background 0.98 | 0.04 | 0.02 | asserted 0.99 |
| s3 | However, such methods are often not ideal when faced with strong noise. | gap 0.51 | 0.06 | 0.02 | asserted 0.88 |
| s4 | In addition, Low-frequency impedance models can mitigate this problem, but creating accurate low-frequency models is difficult and error-prone when well-log data is sparse and subsurface structures are complex. | background 0.65 | 0.06 | 0.02 | asserted 0.96 |
| s5 | To address those issues, we propose a novel deep learning inversion method called DSIM-USSL (Unsupervised and Semi-supervised joint Learning for Seismic Inversion based on diffusion model). | contribution 0.99 | 0.90 | 0.07 | asserted 0.99 |
| s6 | Specifically, we are the first to introduce a diffusion model with strong noise tendency and construct a diffusion seismic inversion model (DSIM). | contribution 0.97 | 0.94 | 0.07 | asserted 1.00 |
| s7 | In the reverse diffusion of DSIM, we design the encoder-decoder which combines CNN for capturing local features and GRU for global sequence modeling; and we choose U-net to learn the distribution of random noise, enhancing the generalization and stability of proposed method. | method 0.99 | 0.93 | 0.04 | asserted 1.00 |
| s8 | Furthermore, to further improve generalization of the proposed method, a two-step training approach (USSL) is utilized. | method 0.91 | 0.89 | 0.03 | asserted 0.99 |
| s9 | First, an unsupervised trained encoder-decoder is used as the initial network model in place of the traditional low-frequency wave impedance model that is difficult to accurately acquire. | method 0.99 | 0.85 | 0.04 | asserted 1.00 |
| s10 | Then, the SSL is employed to further optimize the encoder-decoder model. | method 1.00 | 0.85 | 0.03 | asserted 0.98 |
| s11 | Experimental results on the Marmousi2 model and field data demonstrate that the DSIM-USSL method achieves higher accuracy in the presence of seismic data with random noise, and maintains high stability even under strong noise conditions. | result 1.00 | 0.96 | 0.94 | asserted 1.00 |
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The paper
Original abstract
Seismic impedance inversion is one of the most important part of geophysical exploration. However, due to random noise, the traditional semi-supervised learning (SSL) methods lack generalization and stability. To solve this problem, some authors have proposed SSL methods with anti-noise function to improve noise robustness and inversion accuracy. However, such methods are often not ideal when faced with strong noise. In addition, Low-frequency impedance models can mitigate this problem, but creating accurate low-frequency models is difficult and error-prone when well-log data is sparse and subsurface structures are complex. To address those issues, we propose a novel deep learning inversion method called DSIM-USSL (Unsupervised and Semi-supervised joint Learning for Seismic Inversion based on diffusion model). Specifically, we are the first to introduce a diffusion model with strong noise tendency and construct a diffusion seismic inversion model (DSIM). In the reverse diffusion of DSIM, we design the encoder-decoder which combines CNN for capturing local features and GRU for global sequence modeling; and we choose U-net to learn the distribution of random noise, enhancing the generalization and stability of proposed method. Furthermore, to further improve generalization of the proposed method, a two-step training approach (USSL) is utilized. First, an unsupervised trained encoder-decoder is used as the initial network model in place of the traditional low-frequency wave impedance model that is difficult to accurately acquire. Then, the SSL is employed to further optimize the encoder-decoder model. Experimental results on the Marmousi2 model and field data demonstrate that the DSIM-USSL method achieves higher accuracy in the presence of seismic data with random noise, and maintains high stability even under strong noise conditions.
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The diffusion model (DM) is a model based on Bayesian likelihood probability, inspired by non-equilibrium thermodynamics [ref] .
Autoregressive Diffusion Models · Introduction · 0.97
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