Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin
Seismic waveform feature extraction is a critical task in seismic exploration, as it directly impacts reservoir prediction and geological interpretation. However, large-scale seismic data and nonlinear relationships between seismic signals and reservoir properties are challenging for traditional mac...
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MDPI AG
2025-06-01
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| author | Lifu Zheng Hao Yang Guichun Luo |
| author_facet | Lifu Zheng Hao Yang Guichun Luo |
| author_sort | Lifu Zheng |
| collection | DOAJ |
| description | Seismic waveform feature extraction is a critical task in seismic exploration, as it directly impacts reservoir prediction and geological interpretation. However, large-scale seismic data and nonlinear relationships between seismic signals and reservoir properties are challenging for traditional machine learning methods. To address these limitations, this paper proposes a novel framework combining Convolutional Neural Network (CNN) and Uniform Manifold Approximation and Projection (UMAP) for seismic waveform feature extraction and analysis. The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). A case study in the Ordos Basin further demonstrated that UMAP-CNN offers a high degree of accuracy in predicting coal seam thickness. Furthermore, our framework exhibited superior computational efficiency and robustness in handling large-scale datasets. |
| format | Article |
| id | doaj-art-c7ba4e44c0ef478f85a148aba4d454be |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c7ba4e44c0ef478f85a148aba4d454be2025-08-20T02:35:43ZengMDPI AGApplied Sciences2076-34172025-06-011513737710.3390/app15137377Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos BasinLifu Zheng0Hao Yang1Guichun Luo2Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 100083, ChinaBeijing Earthquake Agency, Beijing 100080, ChinaSeismic waveform feature extraction is a critical task in seismic exploration, as it directly impacts reservoir prediction and geological interpretation. However, large-scale seismic data and nonlinear relationships between seismic signals and reservoir properties are challenging for traditional machine learning methods. To address these limitations, this paper proposes a novel framework combining Convolutional Neural Network (CNN) and Uniform Manifold Approximation and Projection (UMAP) for seismic waveform feature extraction and analysis. The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). A case study in the Ordos Basin further demonstrated that UMAP-CNN offers a high degree of accuracy in predicting coal seam thickness. Furthermore, our framework exhibited superior computational efficiency and robustness in handling large-scale datasets.https://www.mdpi.com/2076-3417/15/13/7377Convolutional Neural Networkreservoir predictiondimensionality reductionseismic waveform analysisUniform Manifold Approximation and Projection |
| spellingShingle | Lifu Zheng Hao Yang Guichun Luo Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin Applied Sciences Convolutional Neural Network reservoir prediction dimensionality reduction seismic waveform analysis Uniform Manifold Approximation and Projection |
| title | Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin |
| title_full | Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin |
| title_fullStr | Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin |
| title_full_unstemmed | Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin |
| title_short | Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin |
| title_sort | seismic waveform feature extraction and reservoir prediction based on cnn and umap a case study of the ordos basin |
| topic | Convolutional Neural Network reservoir prediction dimensionality reduction seismic waveform analysis Uniform Manifold Approximation and Projection |
| url | https://www.mdpi.com/2076-3417/15/13/7377 |
| work_keys_str_mv | AT lifuzheng seismicwaveformfeatureextractionandreservoirpredictionbasedoncnnandumapacasestudyoftheordosbasin AT haoyang seismicwaveformfeatureextractionandreservoirpredictionbasedoncnnandumapacasestudyoftheordosbasin AT guichunluo seismicwaveformfeatureextractionandreservoirpredictionbasedoncnnandumapacasestudyoftheordosbasin |