| Autor: |
Ghosal, Koustav, Panigrahi, Abhranta, Chavan, Arnav, ArunSingh, Gupta, Deepak |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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