简单模型比复杂模型更适合地质物理相位解缠，速度更快且更准确。
When Less Is More: Simplicity Beats Complexity for Physics-Constrained InSAR Phase Unwrapping
- 用原始U-Net替代复杂注意力模型，提升解缠精度。
- 简单模型在R²达0.834，RMSE为1.01厘米，优于复杂模型34%和51%。
- 适合需要实时预警的火山地震监测系统，推理仅2.92毫秒。
基于InSAR的火山与地震监测中，相位解缠是主要计算瓶颈。我们挑战了业界盲目采用高复杂度视觉架构（如注意力机制）的趋势，未验证其在物理约束回归任务中的适用性。我们在全球LiCSAR基准上首次开展大规模架构消融实验（20帧、39,724块、651M像素）。结果表明存在显著“复杂性惩罚”：参数量776万的原始U-Net达到R²=0.834，RMSE=1.01厘米，优于参数量1137万的注意力模型34%（R²）和51%（RMSE）。功率谱密度（PSD）分析显示，注意力虽擅长捕捉自然图像的锐利语义边缘，却引入了超出0.3周期/像素的非物理高频伪影，违反弹性表面形变的平滑性原则。该模型仅需2.92毫秒推理时间（提速2.5倍），唯一满足操作级预警系统<100毫秒要求。本工作通过实证证明，卷积局部性优于现代复杂性，倡导机器学习在遥感中的物理启发式简约设计。
原文摘要 · Abstract (English)
Operational phase unwrapping is the primary computational bottleneck in InSAR-based volcanic and seismic monitoring. We challenge the industry trend of adopting high-complexity computer vision architectures, such as attention mechanisms, without validating their suitability for physics-constrained geophysical regression. We present the first large-scale architectural ablation study on a global LiCSAR benchmark (20 frames, 39,724 patches, 651M pixels). Our results reveal a significant "complexity penalty": a vanilla U-Net (7.76M parameters) achieves $R^2=0.834$ and RMSE $= 1.01$ cm, outperforming 11.37M-parameter attention-based models by 34% in $R^2$ and 51% in RMSE. Power Spectral Density (PSD) analysis provides the physical justification: while attention excels at capturing sharp semantic edges in natural images, it injects unphysical high-frequency artifacts ($>0.3$ cycles/pixel) into geophysical fields, violating the fundamental smoothness constraints of elastic surface deformation. With a 2.92ms inference latency (a $2.5\times$ speedup), the vanilla U-Net is the only candidate to comfortably meet the sub-100ms requirement for operational early-warning systems. This work bridges the "publication-to-practice" gap by proving that convolutional locality outperforms modern complexity for smooth-field regression, advocating for physics-informed simplicity in ML4RS. Code available at https://github.com/prabhjotschugh/When-Less-is-More-InSAR-Phase-Unwrapping
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