Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)

• URL: http://arxiv.org/abs/2504.10720v1
• Date: Mon, 14 Apr 2025 21:24:43 GMT
• Title: Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
• Authors: Kamaljyoti Nath, Khemraj Shukla, Victor C. Tsai, Umair bin Waheed, Christian Huber, Omer Alpak, Chuen-Song Chen, Ligang Lu, Amik St-Cyr,
• Abstract summary: Full Waveform Inversion (FWI) is an important technique considered in subsurface property prediction.<n>FWI solves the inverse problem of predicting high-resolution Earth interior models from seismic data.<n>In this study, we introduce a novel methodology that leverages Deep Operator Networks (DeepONet) to attempt to improve both the efficiency and accuracy of FWI.
• Score: 2.3036557956750867
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Full Waveform Inversion (FWI) is an important geophysical technique considered in subsurface property prediction. It solves the inverse problem of predicting high-resolution Earth interior models from seismic data. Traditional FWI methods are computationally demanding. Inverse problems in geophysics often face challenges of non-uniqueness due to limited data, as data are often collected only on the surface. In this study, we introduce a novel methodology that leverages Deep Operator Networks (DeepONet) to attempt to improve both the efficiency and accuracy of FWI. The proposed DeepONet methodology inverts seismic waveforms for the subsurface velocity field. This approach is able to capture some key features of the subsurface velocity field. We have shown that the architecture can be applied to noisy seismic data with an accuracy that is better than some other machine learning methods. We also test our proposed method with out-of-distribution prediction for different velocity models. The proposed DeepONet shows comparable and better accuracy in some velocity models than some other machine learning methods. To improve the FWI workflow, we propose using the DeepONet output as a starting model for conventional FWI and that it may improve FWI performance. While we have only shown that DeepONet facilitates faster convergence than starting with a homogeneous velocity field, it may have some benefits compared to other approaches to constructing starting models. This integration of DeepONet into FWI may accelerate the inversion process and may also enhance its robustness and reliability.

Related papers

• Full waveform inversion with CNN-based velocity representation extension [4.255346660147713]
  Full waveform inversion (FWI) updates the velocity model by minimizing the discrepancy between observed and simulated data. Discretization errors in numerical modeling and incomplete seismic data acquisition can introduce noise, which propagates through the adjoint operator. We employ a convolutional neural network (CNN) to refine the velocity model before performing the forward simulation. We use the same data misfit loss to update both the velocity and network parameters, thereby forming a self-supervised learning procedure.
  arXiv  Detail & Related papers  (2025-04-22T12:14:38Z)
• Theory-guided Pseudo-spectral Full Waveform Inversion via Deep Neural Networks [0.0]
  Full-Waveform Inversion seeks to achieve a high-resolution model of the subsurface.<n>Deep Learning techniques have emerged as excellent optimization frameworks.<n>This work addresses the lacuna that exists in incorporating the pseudo-spectral approach within Deep Learning.
  arXiv  Detail & Related papers  (2025-02-24T20:18:55Z)
• Data-Driven Pseudo-spectral Full Waveform Inversion via Deep Neural Networks [0.0]
  We re-formulate the pseudo-spectral FWI problem as a Deep Learning algorithm for a data-driven pseudo-spectral approach.<n>Inversion of data-driven pseudo-spectralimat was found to outperform classical FWI for deeper and over-thrust areas.
  arXiv  Detail & Related papers  (2025-02-24T19:50:36Z)
• DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional Applications [59.488352977043974]
  This study proposes DispFormer, a transformer-based neural network for inverting the $v_s$ profile from Rayleigh-wave phase and group dispersion curves. Results indicate that zero-shot DispFormer, even without any labeled data, produces inversion profiles that match well with the ground truth.
  arXiv  Detail & Related papers  (2025-01-08T09:08:24Z)
• Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning [56.45067876391473]
  Existing EEW approaches treat phase picking, location estimation, and magnitude estimation as separate tasks, lacking a unified framework. We propose a novel unified seismic neural network called Fast Information Streaming Handler (FisH) FisH is designed to process real-time streaming seismic data and generate simultaneous results for phase picking, location estimation, and magnitude estimation in an end-to-end fashion.
  arXiv  Detail & Related papers  (2024-08-13T04:33:23Z)
• RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation [50.10282876199739]
  This paper introduces RFTrans, an RGB-D-based method for surface normal estimation and manipulation of transparent objects. It integrates the RFNet, which predicts refractive flow, object mask, and boundaries, followed by the F2Net, which estimates surface normal from the refractive flow. A real-world robot grasping task witnesses an 83% success rate, proving that refractive flow can help enable direct sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-11-21T07:19:47Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Implicit Full Waveform Inversion with Deep Neural Representation [91.3755431537592]
  We propose the implicit full waveform inversion (IFWI) algorithm using continuously and implicitly defined deep neural representations. Both theoretical and experimental analyses indicates that, given a random initial model, IFWI is able to converge to the global minimum. IFWI has a certain degree of robustness and strong generalization ability that are exemplified in the experiments of various 2D geological models.
  arXiv  Detail & Related papers  (2022-09-08T01:54:50Z)
• Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks [0.0]
  Full-waveform inversion (FWI) velocity modeling is an iterative advanced technique that provides an accurate and detailed velocity field model. In this study, we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial network (cGAN) with multiple inputs.
  arXiv  Detail & Related papers  (2021-04-30T21:41:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.