Gravity prospecting is an important geophysical method for mineral resource exploration and investigating crustal structures. Based on the importance of this method, we propose a novel method that takes advantage of rock data, using a supervised deep fully convolutional neural network, that generates a sparse subsurface distribution from gravity data. During the data preparation phase, we used the random walk to synthesize diverse geological models, in which each model element has only two choices. During network training, we feed the geological model as labels and their corresponding forward modeling of gravity data as the input, after which the network parameters are learned using the Dice coefficient. During network testing, six general types of 3D models were developed, and corresponding gravity data was entered into a trained network to achieve the prediction results in less time. The statistical analysis of two evaluation metrics showed that our network was highly effective using our proposed data set, wherein the recovered models were characterized by distinct boundaries. Furthermore, our approach was validated using real data obtained from the San Nicolas deposit in central Mexico.

中文翻译：

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重力数据的深度学习 3D 稀疏反演

重力勘探是矿产资源勘探和地壳结构研究的重要地球物理方法。基于这种方法的重要性，我们提出了一种利用岩石数据的新方法，使用监督深度完全卷积神经网络，从重力数据生成稀疏的地下分布。在数据准备阶段，我们使用随机游走合成了多样化的地质模型，其中每个模型元素只有两个选择。在网络训练期间，我们将地质模型作为标签，并将其对应的重力数据正演建模作为输入，然后使用 Dice 系数学习网络参数。在网络测试期间，开发了六种通用类型的 3D 模型，并将相应的重力数据输入到经过训练的网络中，以在更短的时间内实现预测结果。两个评估指标的统计分析表明，使用我们提出的数据集，我们的网络非常有效，其中恢复的模型具有不同的边界。此外，我们使用从墨西哥中部圣尼古拉斯矿床获得的真实数据验证了我们的方法。