The high-frequency gravity forward modeling relying on the residual terrain modeling (RTM) technique is essential for gravity data processing, fine gravity field modeling, geophysical inversion, and so on. However, classical gravity forward modeling methods face challenges such as series divergence and inefficient computation. To improve the computation efficiency, a novel approach using fully connected deep neural network (FC-DNN) for RTM terrain gravity field modeling is introduced in this study. By employing mean squared error (MSE) as the loss function, the method directly learns the mapping between terrain and gravity anomaly to predict RTM terrain gravity anomaly at any elevation, significantly enhancing computational efficiency. In addition, to boost the network’s generalization capability, a novel terrain information fusion regularization method is utilized to create an Improved FC-DNN with a refined loss function. The accuracy, computational efficiency, and generalization performance of FC-DNN and Improved FC-DNN are evaluated and compared in the Wudalianchi volcanic region and the Himalayas. The findings reveal that determined RTM terrain gravity fields based on both FC-DNN and Improved FC-DNN meet the mGal-level accuracy in these regions, with a remarkable 10 $000\times $ increase in computational efficiency compared to the classical Newtonian integration method. The Improved FC-DNN exhibits superior generalization ability, with accuracy enhancements ranging from 7% to 21% compared with FC-DNN.

中文翻译：

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使用改进的全连接深度神经网络进行 RTM 重力正演建模


依托残余地形建模（RTM）技术的高频重力正演对于重力数据处理、精细重力场建模、地球物理反演等至关重要。然而，经典的重力正演建模方法面临级数发散和计算效率低等挑战。为了提高计算效率，本研究引入了一种使用全连接深度神经网络（FC-DNN）进行RTM地形重力场建模的新方法。该方法采用均方误差（MSE）作为损失函数，直接学习地形与重力异常之间的映射，以预测任意高度的RTM地形重力异常，显着提高计算效率。此外，为了提高网络的泛化能力，采用了一种新颖的地形信息融合正则化方法来创建具有细化损失函数的改进型FC-DNN。在五大连池火山区和喜马拉雅山地区对 FC-DNN 和改进型 FC-DNN 的精度、计算效率和泛化性能进行了评估和比较。结果表明，基于 FC-DNN 和改进型 FC-DNN 确定的 RTM 地形重力场在这些区域满足 mGal 级精度，与经典牛顿积分方法相比，计算效率显着提高 10 $000\times $。改进的 FC-DNN 表现出优异的泛化能力，与 FC-DNN 相比，准确率提高了 7% 至 21%。