地震相分析和层位解释的深度学习方法研究

地震相分析和层位解释的深度学习方法研究地震相分析和层位解释的深度学习方法研究摘要地震相分析和层位解释是地球物理学研究中的重要课题，对于地质构造解释和油气勘探具有重要的意义。

本文提出了一种基于深度学习的地震相分析和层位解释方法，结合卷积神经网络和循环神经网络实现了从地震道数据中自动提取特征和解释地层信息。

首先，提取地震道数据的时间序列特征；其次，使用卷积神经网络进行特征提取，得到抽象的高层次特征表示；然后，使用循环神经网络对特征进行序列建模，进一步提高准确性和鲁棒性。

通过在公开数据集上的测试，验证了本方法的有效性，与传统方法相比，本方法能够更加准确地识别地震相，提升层位解释的精度和效率。

关键词: 地震相分析，层位解释，深度学习，卷积神经网络，循环神经网络AbstractSeismic phase analysis and stratigraphic interpretation are important topics in geophysical research and have significant implications forgeological structure interpretation and oil and gas exploration. This paper proposes a deep learning-based method for seismic phase analysis and stratigraphic interpretation, which combines convolutional neural networks and recurrent neural networks to automatically extract features from seismic data and interpret stratigraphic information. First, the time series features of seismic data are extracted. Second, convolutional neural networks are used for feature extraction to obtain abstract high-level feature representations. Then, the recurrent neural network is used for sequence modeling of features to further improve accuracy and robustness. Through tests on public datasets, the effectiveness of this method has been validated, and compared with traditional methods, this approach can more accurately identify seismic phases and improve the precision and efficiency of stratigraphic interpretation.Keywords: Seismic phase analysis, Stratigraphic interpretation, Deep learning, Convolutional neural networks, Recurrent neural networks。

Seismic phase analysis is a critical component of seismic data processing, which involves identifying different seismic phases from recorded data to understand the geological structure of the subsurface.However, this task can be challenging due to the complexity of the data and the presence of noise and other factors that may interfere with the signals. To address these challenges, researchers have explored various methods of analyzing seismic phases, including traditional techniques such as manual interpretation and hand-crafted feature extraction, as well as more advanced machine learning approaches.Recently, deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise for improving the accuracy and efficiency of seismic phase analysis. CNNs are commonly used for image recognition, but they can also be applied to the analysis of time-series data such as seismic signals. By using a series of convolutional layers to automatically learnfeatures from the data, CNNs can effectively identify different seismic phases and improve the accuracy of seismic interpretation.RNNs, on the other hand, are specialized for the analysis of sequential data, making them well-suitedfor sequence modeling in seismic analysis. Byanalyzing the temporal relationships among different features, RNNs can improve the accuracy of seismic phase identification and enable more precisestratigraphic interpretation.Through tests on public datasets, researchers have demonstrated the effectiveness of deep learning for seismic phase analysis and stratigraphic interpretation. Compared to traditional techniques, deep learning approaches have shown higher accuracyand better performance in identifying seismic phases and interpreting stratigraphic data. As a result,these methods have the potential to significantly improve the efficiency and accuracy of seismic data analysis, making it easier to extract valuableinsights from these complex datasets。

Furthermore, deep learning methods have also been applied to seismic waveform inversion, a key component of subsurface imaging and reservoir characterization. By training a neural network to learn the relationship between input waveforms and the corresponding subsurface properties, waveform inversion can becarried out much more efficiently and accurately than traditional methods such as full waveform inversion. This has the potential to greatly reduce computation time and increase the reliability of subsurface models, leading to better decision making in oil and gas exploration and production.In addition, deep learning approaches have also been used for fault detection and prediction. Faults are geological structures that can lead to oil and gas traps, but can also pose hazards for drilling and production operations. By analyzing seismic data for patterns that are indicative of fault zones, deep learning algorithms can help identify potential locations of faults and assess their likelihood of being active or inactive. This can greatly assist in optimizing drilling locations and minimizing the risk of drilling into hazardous zones.Overall, the application of deep learning in the field of seismic data analysis holds great promise for improving our understanding of the subsurface and making more informed decisions in the exploration and production of oil and gas resources. However, there are still challenges that need to be addressed, such as the need for large amounts of high-quality training data and the potential for overfitting. Nevertheless, as technology continues to advance and more data becomes available, it is likely that deep learning methods will become increasingly important in the oil and gas industry。