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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Salt Delineation From Electromagnetic Data Using Convolutional Neural Networks
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Salt Delineation From Electromagnetic Data Using Convolutional Neural Networks

机译：使用卷积神经网络从电磁数据中划定盐

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With recent advances in machine learning, convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of geophysics. In this letter, we investigated the mapping of subsurface electrical resistivity distributions from electromagnetic (EM) data with CNNs. To begin imaging electrical resistivity using CNNs, we carried out precise delineation of a subsurface salt structure, which is indispensable for identification of offshore hydrocarbon reservoirs, using towed streamer EM data. For training the CNN model, an electrical resistivity model, including a salt body, and corresponding EM data calculated through numerical modeling were used as the label and input, respectively. The optimal weights and biases of the CNN were obtained minimizing the mean-square error between the predicted resistivity distribution and the target label. The final CNN model was selected using a validation data set during training. After training, we applied the trained CNN to test data sets of noisy data and simulated-SEAM data, which were not provided to the network during training. The test results demonstrate that our trained CNN model is stable, reliable, and efficient, and indicate the possibility of successful application of our CNN model to field data. Our study has shown the promising potential of CNNs for identifying defined subsurface electrical resistivity structures that are difficult to find using conventional EM inversion.
机译：随着机器学习的最新进展，卷积神经网络（CNN）已成功应用于许多领域，并且在地球物理学领域已进行了多次尝试。在这封信中，我们研究了来自具有CNN的电磁（EM）数据的地下电阻率分布的映射。为了开始使用CNN成像电阻率，我们使用拖缆EM数据精确描述了地下盐结构，这对于识别近海油气藏来说是必不可少的。为了训练CNN模型，将包含盐体的电阻率模型和通过数值建模计算的相应EM数据分别用作标签和输入。获得了CNN的最佳权重和偏差，从而使预测的电阻率分布与目标标记之间的均方误差最小。在训练过程中，使用验证数据集选择了最终的CNN模型。训练后，我们将训练后的CNN用于测试噪声数据和模拟SEAM数据的数据集，这些数据集在训练过程中没有提供给网络。测试结果表明，我们训练有素的CNN模型是稳定，可靠和高效的，并表明了将CNN模型成功应用于现场数据的可能性。我们的研究表明，CNN可以用于确定定义的地下电阻率结构，而使用常规EM反演很难找到这种结构。

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