用于三维重力反演的深度感知隐式神经表示先验

中文摘要 AI 辅助

重力测量可获取与地质构造、地热系统及侵入体相关的地下密度差异图像。从重力观测数据中重建三维密度模型是高度不适定的问题，原因在于其存在非唯一性、数据覆盖范围有限，以及重力场随深度衰减的特性。传统反演方法依赖显式正则化和参数调优，而监督式深度学习方法则需要代表性的重力-密度配对数据，这类数据往往难以获取。本文提出一种用于三维重力反演的无监督深度感知隐式神经表示方法：密度体积由多个分配给重叠深度板的基于坐标的神经网络表示，并通过灵敏度矩阵直接从观测到的重力测量值中优化。板特定的傅里叶特征、基于物理的深度增益以及调度正则化可提供结构先验，无需标记的密度模型。在四个合成场景上的实验表明，所提方法在RMSE、PSNR和SSIM指标上的整体性能优于评估的传统基线和神经基线；它还能恢复更紧凑、空间相干性更强的密度体，提升邻近异常的分离效果，保留内部结构，并更好地重建其垂直范围。这些结果表明，所提的深度感知公式有助于缓解重力反演固有的深度模糊性。在无真实密度模型的野外实验中，该方法生成了与观测重力模式一致的紧凑、分离且垂直相干的异常。

英文摘要

Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies. Recovering a three-dimensional density model from gravity observations is highly ill-posed because of its non-uniqueness, limited data coverage, and the attenuation of the gravity field with depth. Classical inversion methods rely on explicit regularization and parameter tuning, whereas supervised deep-learning approaches require representative gravity--density pairs that are rarely available. This paper proposes an unsupervised depth-aware implicit neural representation for 3D gravity inversion. The density volume is represented by multiple coordinate-based neural networks assigned to overlapping depth slabs and optimized directly from the observed gravity measurements through the sensitivity matrix. Slab-specific Fourier features, physics-based depth gains, and scheduled regularization provide structural priors without requiring labeled density models. Experiments on four synthetic scenarios show that the proposed method provides better overall performance in terms of RMSE, PSNR, and SSIM than the evaluated conventional and neural baselines. It also recovers more compact and spatially coherent density bodies, improves the separation of nearby anomalies, preserves internal structures, and reconstructs their vertical extent better. These results indicate that the proposed depth-aware formulation helps to mitigate the depth ambiguity inherent in gravity inversion. In the field experiment, where no ground-truth density model was available, the method produced compact, separated, and vertically coherent anomalies consistent with the observed gravity pattern.

发表机构

• Universidad Industrial de Santander（桑坦德工业大学）

机构由 AI 辅助整理，请以论文原文为准。