3D geophysical image translated into photorealistic virtual outcrop geology using generative adversarial networks

A. Ramdani, A. Perbawa, Andrey Bakulin, V. Vahrenkamp
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Abstract

Outcrop analogues play a pivotal role in resolving meter-scale depositional facies heterogeneity of carbonate strata. Two-dimensional outcrops are insufficient to decipher the 3D heterogeneity of carbonate facies. Near-surface geophysical methods, notably ground-penetrating radar (GPR), can be employed to step into 3D and extend the dimensionality of the outcrops to behind the outcrop. However, interpreting geophysical images requires specific geophysical expertise, often unfamiliar to field geologists who are more familiar with the actual rock than the geophysical data. A novel generative adversarial network (GAN) application is presented that constructs a photorealistic 3D virtual outcrop behind-the-outcrop model. The method combines GPR forward modeling with a conditional generative adversarial network (CGAN) and exploits the apparent similarities between outcrop expressions of lithofacies with their radargram counterparts. We exemplified the methodology and applied it to the open-source GPR data acquired from the Late Oxfordian-Early Kimmeridgian Arabian carbonate outcrop. We interpret a 4 km long outcrop photomosaic from a digital outcrop model (DOM) for its lithofacies, populate the DOM with GPR properties, and forward model the synthetic GPR response of these lithofacies. We pair the synthetic GPR with DOM lithofacies and train them using CGAN. Similarly, we pair the DOM lithofacies with outcrop photos and train them using CGAN. We chain the two trained networks and apply them to construct an approximately 2 km long 2D and an approximately 60 m2 3D volume of photorealistic artificial outcrop model. This model operates in a visual medium familiar to outcrop geologists, providing a complementary instrument to visualize and interpret rock formation instead of geophysical signals. This virtual outcrop replicates the visual character of outcrop-scale lithofacies features, such as the intricate bedding contacts and the outline of reef geobodies.
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利用生成式对抗网络将三维地球物理图像转化为逼真的虚拟露头地质学
露头模拟在解析碳酸盐岩地层米级沉积面异质性方面发挥着关键作用。二维露头岩层不足以解读碳酸盐岩层的三维异质性。近地表地球物理方法,特别是探地雷达(GPR),可用于进入三维空间,将露头的维度扩展到露头后面。然而,解释地球物理图像需要特定的地球物理专业知识,这往往是野外地质学家所不熟悉的,因为他们更熟悉实际岩石,而不是地球物理数据。本文介绍了一种新颖的生成对抗网络 (GAN) 应用程序,它能构建逼真的三维虚拟露头后方模型。该方法将 GPR 前向建模与条件生成对抗网络 (CGAN) 相结合,利用岩性的露头表达与其雷达图对应物之间的明显相似性。我们举例说明了这一方法,并将其应用于从晚牛津世-早金美里纪阿拉伯碳酸盐岩露头获取的开源 GPR 数据。我们通过数字露头模型(DOM)对 4 公里长的露头拼图进行岩性解释,在 DOM 中填充 GPR 特性,并对这些岩性的合成 GPR 响应进行前向建模。我们将合成 GPR 与 DOM 岩性配对,并使用 CGAN 对其进行训练。同样,我们将 DOM 岩性与露头照片配对,并使用 CGAN 对其进行训练。我们将两个训练有素的网络串联起来,并将其用于构建一个长约 2 公里的二维和一个约 60 平方米的三维逼真人工露头模型。该模型在露头地质学家所熟悉的视觉媒介中运行,为可视化和解释岩层而非地球物理信号提供了补充工具。该虚拟露头复制了露头尺度岩性特征的视觉特性,如错综复杂的层理接触和礁岩地质体的轮廓。
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