Parameter Efficient Fine-Tuning for Deep Learning-Based Full-Waveform Inversion
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915081229434880 |
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| author | Ghosal, Koustav Panigrahi, Abhranta Chavan, Arnav ArunSingh Gupta, Deepak |
| author_facet | Ghosal, Koustav Panigrahi, Abhranta Chavan, Arnav ArunSingh Gupta, Deepak |
| contents | Seismic full waveform inversion (FWI) has seen promising advancements through deep learning. Existing approaches typically focus on task-specific models trained and evaluated in isolation that lead to limited generalization across different geological scenarios. In this work we introduce a task-agnostic foundational model for FWI that captures general features across tasks. We first demonstrate that full fine-tuning of this foundational model outperforms task-specific models built from scratch by delivering superior performance across multiple benchmarks. Building upon this we employ parameter-efficient fine-tuning (PEFT) to further reduce computational overhead. By fine-tuning only a small fraction of the model parameters PEFT achieves comparable results to full fine-tuning while significantly lowering memory and computational requirements. Additionally, PEFT excels in out-of-distribution tasks where it outperforms both full fine-tuning and task-specific models. These findings establish the value of foundational modeling for FWI and highlight PEFT as an effective strategy for efficient and scalable adaptation across diverse tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_19510 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parameter Efficient Fine-Tuning for Deep Learning-Based Full-Waveform Inversion Ghosal, Koustav Panigrahi, Abhranta Chavan, Arnav ArunSingh Gupta, Deepak Computational Engineering, Finance, and Science Geophysics Seismic full waveform inversion (FWI) has seen promising advancements through deep learning. Existing approaches typically focus on task-specific models trained and evaluated in isolation that lead to limited generalization across different geological scenarios. In this work we introduce a task-agnostic foundational model for FWI that captures general features across tasks. We first demonstrate that full fine-tuning of this foundational model outperforms task-specific models built from scratch by delivering superior performance across multiple benchmarks. Building upon this we employ parameter-efficient fine-tuning (PEFT) to further reduce computational overhead. By fine-tuning only a small fraction of the model parameters PEFT achieves comparable results to full fine-tuning while significantly lowering memory and computational requirements. Additionally, PEFT excels in out-of-distribution tasks where it outperforms both full fine-tuning and task-specific models. These findings establish the value of foundational modeling for FWI and highlight PEFT as an effective strategy for efficient and scalable adaptation across diverse tasks. |
| title | Parameter Efficient Fine-Tuning for Deep Learning-Based Full-Waveform Inversion |
| topic | Computational Engineering, Finance, and Science Geophysics |
| url | https://arxiv.org/abs/2412.19510 |