Paleokarst systems are found extensively in carbonate‐prone basins worldwide. They can form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can also create serious engineering geohazards. The full delineation of potentially buried paleokarst systems plays an important role for reservoir characterization, oil and gas production, and other engineering tasks. We propose a supervised convolutional neural network (CNN) to automatically and accurately characterize paleokarst and associated collapse features from 3‐D seismic images. To avoid time‐consuming manual labeling for training the CNN, we propose an efficient workflow to automatically generate numerous 3‐D training image pairs including synthetic seismic images and the corresponding label images of the collapsed paleokarst features simulated in the seismic images. With this workflow, we are able to simulate realistic and diverse geologic structures and collapsed paleokarst features in the training images from which the CNN can effectively learn to recognize the collapsed paleokarst features in real field seismic images. Two field examples from the Fort Worth Basin demonstrate that our CNN‐based method is superior to conventional automatic methods in delineating paleokarst collapse features from seismic images. From the CNN‐based paleokarst characterization, we can further automatically extract 3‐D collapsed paleokarst systems and quantitatively measure their geometric parameters. Our CNN‐based method is highly efficient and takes only seconds to classify collapsed paleokarst features a 3‐D seismic image with 320 × 1, 024 × 1, 024 samples (approximately 268 km²) by using one graphics processing unit.

中文翻译：

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深度学习用于表征3D地震图像中的古岩溶塌陷特征

古岩溶系统广泛存在于全球碳酸盐易发盆地中。它们可以形成大型油藏并为油气运移提供有效的途径，但它们也可能造成严重的工程地质灾害。潜在地埋古岩溶系统的完整描述对于储层表征，油气生产和其他工程任务具有重要作用。我们提出了一种监督卷积神经网络（CNN），以从3D地震图像中自动准确地表征古岩溶和相关的崩塌特征。为了避免在训练CNN时费时的手动标记，我们提出了一种有效的工作流程来自动生成许多3D训练图像对，包括合成地震图像和在地震图像中模拟的塌陷的古岩溶特征的相应标签图像。借助此工作流程，我们能够在训练图像中模拟逼真的多样的地质结构和塌陷的古岩溶特征，而CNN可以从中有效学习以识别真实地震图像中塌陷的古岩溶特征。沃斯堡盆地的两个现场实例表明，基于CNN的方法在描述地震图像中的古喀斯特塌陷特征方面优于传统的自动方法。从基于CNN的古岩溶刻画中，我们可以进一步自动提取3D塌陷的古岩溶系统并定量测量其几何参数。我们基于CNN的方法非常高效，仅需几秒钟即可对塌陷的古岩溶特征进行分类，并具有3D地震图像。使用一个图形处理单元处理320×1，024×1，024个样本（约268 km ²）。