Seismic facies recognition based on prestack data using deep convolutional autoencoder

Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks

The interpretation of seismic data is vital for characterizing sediments' shape in areas of geological study. In seismic interpretation, deep learning becomes useful for reducing the dependence on handcrafted facies segmentation geometry and the time required to study geological areas. This work presents a Deep Neural Network for Facies Segmentation (DNFS) to obtain state-of-the-art results for seismic facies segmentation. DNFS is trained using a combination of cross-entropy and Jaccard loss functions. Our results show that DNFS obtains highly detailed predictions for seismic facies segmentati…

CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification

Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome this challenge, we propose a semi-supervised method based on pixel-level contrastive learning, termed CONSS, which can efficiently identify seismic facies using only 1% of the original annotations. Furthermore, the absence of a unified data division and standardized metrics hinders…

基于体素的地震数据三维岩相分割：一项对比研究

Voxel-based 3D Facies Segmentation from Seismic Data: A Comparative Study

针对地震岩相分割存在的三维空间连续性缺失问题，本研究构建基于体素的三维分割可复现基准，评估三类现代三维分割架构并建立基线结果，为该领域后续研究提供参考。

AdaSemSeg：面向地震岩相的自适应小样本语义分割

AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies

针对现有小样本语义分割方法无法适配类别数量变化的缺陷，提出 AdaSemSeg 方法，采用自监督算法初始化骨干网络，在三个地震岩相数据集上训练后，其在未见过的数据集上性能优于基线方法。

Unsupervised seismic facies classification using deep convolutional autoencoder

With the increased size and complexity of seismic surveys, manual labeling of seismic facies has become a significant challenge. Application of automatic methods for seismic facies interpretation could significantly reduce the manual labor and subjectivity of a particular interpreter present in conventional methods. A recently emerged group of methods is based on deep neural networks. These approaches are data-driven and require large labeled datasets for network training. We apply a deep convolutional autoencoder for unsupervised seismic facies classification, which does not require manually …

地震相分析：一种深度域自适应方法

Seismic Facies Analysis: A Deep Domain Adaptation Approach

针对标签数据稀缺及域分布偏移下地震相分析的问题，提出EarthAdaptNet架构（用转置残差单元改进解码器），结合CORAL方法构建EAN-DDA，在F3和Penobscot数据集上实现高精度分类，少数类准确率达~70%，目标域最高类准确率~99%。