SeisEvo：智能体驱动的地震数据重建算法演化

中文摘要 AI 辅助

经典地震数据重建依赖人工设计的结构先验和迭代算子，其耦合设计空间远非人工试错能系统探索。深度学习方法将重建规则编码到学习得到的权重中，而非可检查和修改的显式算子。我们提出SeisEvo（地震算法演化），该方法不优化单个重建结果，而是搜索产生结果的算法。从经典重建算法出发，大语言模型（LLM）驱动的多智能体搜索仅修改用户开放编辑的组件，不预设待发现的机制；违反任务物理约束的候选会被直接剔除，剩余候选通过执行评分。输出既非智能体系统也非神经网络，而是推理时无需智能体或神经网络的独立白盒算法。对于无附加噪声的插值，该搜索发现了残差门控、相位对齐的倾角一致性投影；Evo-POCS在缺失率30%至70%的范围内，较经典POCS平均提升信噪比（SNR）3.49 dB。对于同时插值与去噪，该搜索发现了可靠性分组的奇异值收缩；Evo-MSSA较经典MSSA平均重建SNR提升超7 dB，较更强的秩缩减基准提升超3 dB。两种算子在搜索未使用的数据上均保留其增益。据我们所知，这是首个将地震重建算子的设计形式化为受约束的、LLM驱动的程序演化任务的研究；智能体驱动的算法演化可补充深度学习，用于发现显式、可检查且可部署的地震处理算法。

英文摘要

Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger than manual trial and error can explore systematically. Deep-learning methods encode the reconstruction rules in learned weights rather than in an explicit operator that can be inspected and modified. We propose SeisEvo (Seismic Algorithm Evolution), which does not optimize a single reconstruction result but searches for the algorithm that produces it. Starting from a classical reconstruction algorithm, an LLM-driven multi-agent search modifies only the components that the user has opened for editing, without prescribing the mechanism to be discovered. Candidates that violate the physical constraints of the task are rejected outright, and the remaining ones are scored by execution. The output is neither an agent system nor a neural network, but a standalone white-box algorithm that requires no agent or neural network at inference time. For interpolation without added noise, the search discovered a residual-gated, phase-aligned dip-consistency projection; Evo-POCS improves the SNR over classic POCS by 3.49 dB on average across missing ratios from 30% to 70%. For simultaneous interpolation and denoising, it discovered a reliability-grouped singular-value shrinkage; Evo-MSSA improves the average reconstruction SNR by more than 7 dB over classic MSSA and by more than 3 dB over a stronger rank-reduction baseline. Both operators retain their gains on data not used during the search. To the best of our knowledge, this is the first study to formulate the design of a seismic reconstruction operator as a constrained, LLM-driven program evolution task. Agentic algorithm evolution can thus complement deep learning in discovering explicit, inspectable, and deployable seismic processing algorithms.

发表机构

• Harbin Institute of Technology（哈尔滨工业大学）
• National University of Singapore（新加坡国立大学）
• Peking University（北京大学）

机构由 AI 辅助整理，请以论文原文为准。