A Physics-guided Generative AI Toolkit for Geophysical Monitoring. (arXiv:2401.03131v1 [cs.LG])

This article discusses the role of full-waveform inversion (FWI) in geoscience and its use of seismic waves to image the subsurface velocity map. It highlights the emergence of data-driven approaches using machine learning (ML) for FWI tasks, which offer improved accuracy and reduced computational cost compared to traditional methods. However, the challenge of unprivileged data severely limits the effectiveness of ML in geoscience, especially during model pruning. To address this challenge, the article introduces the EdGeo toolkit, which utilizes a diffusion-based model guided by physics principles to generate high-fidelity velocity maps. By using the acoustic wave equation to generate seismic waveform data, the toolkit enables fine-tuning of pruned ML models. The results demonstrate significant improvements in SSIM scores and a reduction in MAE and MSE across various pruning ratios. The fine-tuned ML model using data generated by EdGeo outperforms other existing methods, particularly in representing unprivileged features, resulting in superior quality velocity maps.

Exploring the Potential of EdGeo Toolkit in Geoscience: Enhancing ML Models for Subsurface Velocity Mapping

Full-waveform inversion (FWI) has long been a fundamental tool in geoscience for exploring the subsurface and understanding its intricate details. By utilizing seismic waves, FWI enables the generation of accurate velocity maps that assist in various applications, including oil and gas exploration, earthquake analysis, and rock formation studies.

With the advent of machine learning (ML) techniques, data-driven approaches have gained popularity in enhancing FWI tasks. These ML-based methods offer improved accuracy and reduced computational costs compared to traditional physics-based methodologies. However, ML models heavily rely on high-quality training data, which poses a significant challenge in geoscience due to the scarcity of privileged information.

Moreover, the process of model pruning, which is essential to address the environmental complexities involved in geoscience, further exacerbates the issue of limited data availability. Model pruning involves reducing the complexity of ML models while maintaining their performance. This step aims to prevent overfitting and increase the model’s efficiency. However, pruning often leads to the loss of valuable information from already limited datasets.

Recognizing the need to address these challenges, we introduce the EdGeo toolkit – a groundbreaking solution that harnesses diffusion-based models guided by physics principles to generate high-fidelity velocity maps. The integration of acoustic wave equations and diffusion-based modeling allows EdGeo to produce seismic waveform data corresponding to generated velocity maps.

The EdGeo toolkit facilitates the fine-tuning of pruned ML models by augmenting them with the high-quality data produced by its diffusion-based model. This approach overcomes the limitations of unprivileged data by supplementing it with realistic synthetic seismic waveforms. As a result, the fine-tuned ML models exhibit enhanced performance and accuracy in velocity mapping tasks.

Our experimentations and evaluations demonstrate significant improvements in structural similarity index (SSIM) scores, as well as reductions in both mean absolute error (MAE) and mean squared error (MSE) across various pruning ratios. These metrics serve as indicators of the fidelity and precision of velocity maps.

Notably, the ML models fine-tuned using data generated by the EdGeo toolkit consistently outperform existing methods in terms of quality representation of unprivileged features. Features that were previously challenging to capture due to the scarcity of data now find accurate representation in the velocity maps produced by EdGeo-informed ML models.

The EdGeo toolkit opens up new avenues for ML-based FWI tasks in geoscience by bridging the gap between limited privileged data and accurate velocity mapping. Its diffusion-based modeling approach guided by physics principles offers a robust solution that can be easily integrated into existing workflows.

In conclusion, the EdGeo toolkit revolutionizes ML-driven FWI tasks by offering a powerful solution to the challenges of unprivileged data and model pruning. By combining the strengths of diffusion-based modeling and physics-guided approaches, it generates high-fidelity velocity maps that outperform existing methods. This breakthrough opens up possibilities for more accurate subsurface exploration and paves the way for advancements in geoscience research.

Full-waveform inversion (FWI) has long been a key technique in geoscience for exploring the subsurface. By utilizing seismic waves, FWI allows us to create detailed velocity maps of the subsurface, which is crucial for various applications such as oil and gas exploration, earthquake studies, and underground imaging.

With the continuous evolution of machine learning (ML) techniques, data-driven approaches using ML for FWI tasks have emerged. These approaches have shown promise in improving the accuracy of velocity maps while reducing computational costs compared to traditional physics-based methods. However, a common challenge in geoscience is the lack of privileged data, which severely limits the effectiveness of ML algorithms.

This limitation becomes even more pronounced during model pruning, a necessary step in geoscience due to the complexities of the environment. Model pruning involves reducing the complexity of ML models to improve efficiency and generalization. However, this process often leads to loss of important information, especially when dealing with unprivileged features in the subsurface.

To address this challenge, the authors introduce the EdGeo toolkit, which employs a diffusion-based model guided by physics principles to generate high-fidelity velocity maps. The toolkit utilizes the acoustic wave equation to generate corresponding seismic waveform data, which can then be used to fine-tune pruned ML models.

The results presented in the study demonstrate significant improvements in structural similarity index (SSIM) scores and reductions in both mean absolute error (MAE) and mean squared error (MSE) across various pruning ratios. Notably, the ML model fine-tuned using data generated by EdGeo outperforms other existing methods in terms of representing unprivileged features and producing superior quality velocity maps.

This approach of combining physics-based modeling with machine learning techniques shows great promise in addressing the challenges faced by geoscientists. By leveraging physics principles to generate synthetic data for training ML models, we can overcome the limitations of unprivileged data and improve the accuracy and reliability of velocity maps.

Moving forward, further research and development in this area could focus on refining the diffusion-based model used in EdGeo to better capture the complexities of the subsurface. Additionally, exploring ways to incorporate other physics-based constraints and prior knowledge into the ML training process could further enhance the performance of the fine-tuned models.

Overall, the introduction of the EdGeo toolkit represents a significant advancement in the field of geoscience and FWI. By bridging the gap between physics-based modeling and machine learning, this approach has the potential to revolutionize subsurface imaging and exploration, leading to improved understanding and utilization of Earth’s resources.
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