Accurate estimation of Moho topography plays a crucial role in understanding Earth’s structure, geodynamic processes, and resource exploration. This study presents a novel approach that utilizes conditional Generative Adversarial Networks (cGAN) to reveal Moho topography based on observed gravity anomalies. Synthetic training datasets of Moho topography were generated using the FFT filtering method due to the scarcity of true datasets. Spherical prism-based forward gravity modeling was employed to evaluate the resulting gravity anomalies. We compared the performance of our developed deep learning algorithm cGAN (conditional Generative Adversarial Networks) with a traditional inversion technique using various synthetic datasets, and a real case study in southern peninsular India, a geologically diverse region comprising ancient continental tectonic blocks. Bott’s inversion scheme was employed as a verification method for the Moho surface estimation using the presented deep learning model. Using spherical prism-based forward gravity modeling, observed gravity anomalies were corrected for multiple factors such as topography, bathymetry, sediments, crustal heterogeneities, and mantle heterogeneities. By removing these effects, we isolated the gravity contribution solely related to pure Moho undulation. The mean Moho depth and density contrast between the crust and mantle were derived from seismic constraints for improving estimation accuracy. The findings demonstrate the potential of the cGAN and spherical prism-based gravity modeling approach in accurately estimating the Moho topography, offering insights into Earth’s subsurface structures and enhancing our understanding of geodynamic processes and resource exploration efforts.

中文翻译：

---

利用观测到的重力异常的条件生成对抗网络对莫霍面地形进行成像

莫霍面地形的准确估计对于了解地球结构、地球动力学过程和资源勘探起着至关重要的作用。这项研究提出了一种新颖的方法，利用条件生成对抗网络（cGAN）根据观测到的重力异常来揭示莫霍面地形。由于真实数据集的稀缺性，莫霍面地形的综合训练数据集是使用 FFT 滤波方法生成的。采用基于球棱镜的正向重力模型来评估由此产生的重力异常。我们使用各种合成数据集将我们开发的深度学习算法 cGAN（条件生成对抗网络）与传统反演技术的性能进行了比较，并以印度南部半岛（一个由古代大陆构造块组成的地质多样化地区）的真实案例研究为基础。 Bott 的反演方案被用作使用所提出的深度学习模型进行莫霍面估计的验证方法。使用基于球棱镜的正向重力模型，针对地形、测深、沉积物、地壳异质性和地幔异质性等多种因素对观测到的重力异常进行了校正。通过消除这些影响，我们分离出仅与纯莫霍面波动相关的重力贡献。平均莫霍面深度和地壳与地幔之间的密度对比是根据地震约束推导的，以提高估计精度。研究结果证明了 cGAN 和基于球面棱镜的重力建模方法在准确估计莫霍面地形、提供对地球地下结构的见解并增强我们对地球动力学过程和资源勘探工作的理解方面的潜力。