论文标题
使用地震图像分割的盐检测
Salt Detection Using Segmentation of Seismic Image
论文作者
论文摘要
在这个项目中，将最新的深卷卷卷神经网络（DCNN）提出为段地震图像，以在地球表面以下的盐检测。检测盐位置对于开始开采非常重要。因此，使用地震图像来检测地球表面下的确切盐位置。但是，精确地检测出盐沉积的确切位置是困难的。因此，专业的地震成像仍然需要人类对盐体的专家解释。这导致非常主观，高度可变的效果图。因此，要创建最准确的地震图像和3D渲染，我们需要一种可靠的算法，该算法自动准确地识别表面目标是否是盐。由于DCNN的性能是众所周知的，并且已建立了图像中的对象识别，因此DCNN是这个特定问题的一个很好的选择，并成功地应用于地震图像数据集，其中每个像素都标记为盐。该算法的结果是有希望的。
In this project, a state-of-the-art deep convolution neural network (DCNN) is presented to segment seismic images for salt detection below the earth's surface. Detection of salt location is very important for starting mining. Hence, a seismic image is used to detect the exact salt location under the earth's surface. However, precisely detecting the exact location of salt deposits is difficult. Therefore, professional seismic imaging still requires expert human interpretation of salt bodies. This leads to very subjective, highly variable renderings. Hence, to create the most accurate seismic images and 3D renderings, we need a robust algorithm that automatically and accurately identifies if a surface target is a salt or not. Since the performance of DCNN is well-known and well-established for object recognition in images, DCNN is a very good choice for this particular problem and being successfully applied to a dataset of seismic images in which each pixel is labeled as salt or not. The result of this algorithm is promising.
