Inversion of electromagnetic data finds applications in many areas of geophysics. The inverse problem is commonly solved with either deterministic optimization methods (such as the nonlinear conjugate gradient or Gauss-Newton) which are prone to getting trapped in a local minimum, or probabilistic methods which are very computationally demanding. A recently emerging alternative is to employ deep neural networks for predicting subsurface model properties from measured data. This approach is entirely data-driven, does not employ traditional gradient-based techniques and provides a guess to the model instantaneously. In this study, we apply deep convolutional neural networks for 1D inversion of marine frequency-domain controlled-source electromagnetic (CSEM) data as well as onshore time-domain electromagnetic (TEM) data. Our approach yields accurate results both on synthetic and real data and provides them instantaneously. Using several networks and combining their outputs from various training epochs can also provide insights into the uncertainty distribution, which is found to be higher in the regions where resistivity anomalies are present. The proposed method opens up possibilities to estimate the subsurface resistivity distribution in exploration scenarios in real time.

中文翻译：

---

使用卷积神经网络反演一维频域和时域电磁数据

电磁数据反演在地球物理学的许多领域都有应用。逆问题通常使用易于陷入局部最小值的确定性优化方法（例如非线性共轭梯度或高斯-牛顿）或对计算要求很高的概率方法来解决。最近出现的一种替代方法是采用深度神经网络从测量数据中预测地下模型属性。这种方法完全是数据驱动的，不采用传统的基于梯度的技术，并立即提供对模型的猜测。在这项研究中，我们将深度卷积神经网络应用于海洋频域受控源电磁 (CSEM) 数据和陆上时域电磁 (TEM) 数据的一维反演。我们的方法在合成数据和真实数据上都能产生准确的结果，并立即提供它们。使用多个网络并结合来自不同训练时期的输出也可以提供对不确定性分布的洞察，发现在存在电阻率异常的区域中不确定性分布更高。所提出的方法开辟了在勘探场景中实时估计地下电阻率分布的可能性。