论文标题
SeismiQB-一个具有地震数据的深度学习框架
SeismiQB -- a novel framework for deep learning with seismic data
论文作者
论文摘要
近年来，在众多领域中成功采用了深层神经网络，以解决与图像相关的各种任务，从简单分类到良好的边界注释。自然，许多研究建议将其用于解决地质问题。不幸的是，许多地震处理工具都是在机器学习时代之前几年开发的，其中包括用于存储地震立方体的最流行的SEG-Y数据格式。它的缓慢加载速度大大缩小了实验速度，这对于获得可接受的结果至关重要。更糟糕的是，没有广泛使用的格式可以在体积内部存储表面（例如，地震范围）。为了解决这些问题，我们开发了一个开源的Python框架，重点是使用神经网络，该框架为（i）以多种数据格式的快速加载地震立方体提供了方便的工具，并在它们之间进行转换，（ii）生成所需形状的农作物，并通过多种转换与（iii）配对数据与其他类型的geobod或其他类型的geobod sopecod sope norkod sope nordies或其他类型的geobod sopecod sopecod sope norkod sopecod sopecod sopecod sopecod sop of geobod或其他类型的geobod。
In recent years, Deep Neural Networks were successfully adopted in numerous domains to solve various image-related tasks, ranging from simple classification to fine borders annotation. Naturally, many researches proposed to use it to solve geological problems. Unfortunately, many of the seismic processing tools were developed years before the era of machine learning, including the most popular SEG-Y data format for storing seismic cubes. Its slow loading speed heavily hampers experimentation speed, which is essential for getting acceptable results. Worse yet, there is no widely-used format for storing surfaces inside the volume (for example, seismic horizons). To address these problems, we've developed an open-sourced Python framework with emphasis on working with neural networks, that provides convenient tools for (i) fast loading seismic cubes in multiple data formats and converting between them, (ii) generating crops of desired shape and augmenting them with various transformations, and (iii) pairing cube data with labeled horizons or other types of geobodies.
