论文标题
使用深度学习直接对地球物理原木的多模式反转
Direct multi-modal inversion of geophysical logs using deep learning
论文作者
论文摘要
井的地理座谈需要快速解释地球物理日志，这是一个非唯一的反问题。当前的工作提出了一种使用人工深神经网络（DNN）的单一评估来对日志进行多模式概率反转的概念验证方法。混合物密度DNN（MDN）是使用“多个防护性预测”（MTP）损耗函数训练的，该功能避免了传统MDN的典型模式崩溃，并允许在数据之前进行多模式预测。关于伽马射线日志的实时地层反转验证了所提出的方法。多模式预测指标输出了几种可能的逆解决方案/预测，与使用DNN进行确定性回归相比，提供了更准确和更现实的解决方案。对于这些可能的地层曲线，该模型同时预测了它们的概率，这些概率是从训练地质数据中隐含的。从MDN中获得的地层预测及其概率可以在地质不确定性下实现更好的实时决策。
Geosteering of wells requires fast interpretation of geophysical logs, which is a non-unique inverse problem. Current work presents a proof-of-concept approach to multi-modal probabilistic inversion of logs using a single evaluation of an artificial deep neural network (DNN). A mixture density DNN (MDN) is trained using the "multiple-trajectory-prediction" (MTP) loss functions, which avoids mode collapse typical for traditional MDNs, and allows multi-modal prediction ahead of data. The proposed approach is verified on the real-time stratigraphic inversion of gamma-ray logs. The multi-modal predictor outputs several likely inverse solutions/predictions, providing more accurate and realistic solutions than a deterministic regression using a DNN. For these likely stratigraphic curves, the model simultaneously predicts their probabilities, which are implicitly learned from the training geological data. The stratigraphy predictions and their probabilities obtained in milliseconds from the MDN can enable better real-time decisions under geological uncertainties.
