波传播几何从标量走时关系中涌现
Wave-Propagation Geometry Emerges from Scalar Travel-Time Relations
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中文总结 AI 辅助
该研究证明标量走时关系无需速度或标签即可识别波传播几何，通过神经场和经典表示恢复方向与速度，并在中国和加州数据上验证结构，强调标量拟合不保证导数可靠性。
中文摘要 AI 辅助
标量走时关系可以在学习过程中无需速度输入、射线标签或方程监督的情况下识别传播几何。物理解释是在学习之后通过高频各向同性程函关系（Aki 和 Richards，2002）应用的。在此我们表明，局部值差异通过采样几何和平滑性约束导数。受控神经场以 $5.34\pm0.07^{\circ}$ 的中值角度误差恢复传播方向，并在前瞻性锁定、测试密封的实现中恢复局部 P 波和 S 波速度。经典表示也能恢复几何。标量匹配的扰动将值精度与导数可靠性分离；当直接梯度失败时，正则化反演可以恢复结构。一个在 290 万条中国走时上训练的场保留了大陆地壳-地幔边界结构：与保留参考的相关性为原始 $\rho=0.910$，二次去趋势后为 0.592（空间位移 $p=0.020$）。直接目录值产生 $\rho=0.909$，厚度对比被压缩。仅加利福尼亚的手动走时支持广泛的的速度组织，但横向保真度有限。包含相位信息的合成量子场支持概率流和轨迹读出，而无需监督这些量。这些结果确立了跨表示和物理设置的条件下识别：信息丰富的标量关系可以约束几何，而已知的物理关系提供其解释。它们并不意味着仅凭准确的标量拟合就能保证可靠的物理导数。
英文摘要
Scalar travel-time relations can identify propagation geometry without velocity inputs, ray labels or equation supervision during learning. Physical interpretation is applied after learning through the high-frequency isotropic eikonal relation (Aki and Richards, 2002). Here we show that local value differences constrain derivatives through sampling geometry and smoothness. Controlled neural fields recover propagation directions with $5.34\pm0.07^{\circ}$ median angular error, and local P- and S-wave velocities in a prospectively locked, test-sealed realization. Classical representations also recover geometry. Scalar-matched perturbations separate value accuracy from derivative reliability; regularized inversion can recover structure when direct gradients fail. A field trained on 2.90 million Chinese arrivals retains continental crust--mantle boundary structure: correlation with a withheld reference is $ρ=0.910$ raw and 0.592 after quadratic detrending (spatial-shift $p=0.020$). Direct catalogue values yield $ρ=0.909$, and thickness contrasts are compressed. Manual-only California arrivals support broad velocity organization with limited lateral fidelity. Synthetic quantum fields containing phase information support probability-flow and trajectory readouts without supervising those quantities. These results establish conditional identification across representations and physical settings: informative scalar relations can constrain geometry, while known physical relations supply its interpretation. They do not imply that accurate scalar fitting alone guarantees reliable physical derivatives.
发表机构
- Institute of Geophysics, China Earthquake Administration（中国地震局地球物理研究所）
机构由 AI 辅助整理，请以论文原文为准。
