Facies Classification Benchmark

The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach. <br> To address these issues, we open-source a fully-annotated 3D geological model of the Netherlands F3 Block. This model is based on the study of the 3D seismic data in addition to 26 well logs, and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work helps advance research in this area by creating an objective benchmark for comparing the results of different machine learning approaches for facies classification.

近年来，学界针对将深度学习应用于地震解释（如相分类（facies classification））这类任务的研究兴趣日益浓厚，但却面临着一项显著阻碍：缺乏用于模型训练与测试的大规模公开标注数据集。为此，研究者往往只能自行标注训练与测试数据。然而，不同研究者所标注的类别可能存在差异，或是采用了不同的训练集与测试集划分方式。此外，在将机器学习应用于相分类的相关论文中，常有仅依赖对结果的目视检验、未给出定量分析结果的情况。上述种种做法均导致研究结果带有主观性，极大地阻碍了不同机器学习模型之间的横向对比，也使得人们难以明晰各类方法的优劣得失。
为解决上述问题，我们开源了荷兰F3区块（Netherlands F3 Block）的全标注三维地质模型。该模型基于对三维地震数据与26条测井曲线（well logs）的研究，并依托对该区域地质特征的细致剖析构建而成。进一步地，我们提出了两款基于反卷积网络（deconvolution network）架构的相分类基准模型，并公开了对应的代码。最后，我们设计了一套用于在该数据集上评估不同模型的方案，并公布了两款基准模型的测试结果。本工作除公开数据集与代码外，还通过构建客观的评估基准来对比不同机器学习相分类方法的实验结果，从而推动该领域的研究进展。