使用深层神经网络和用于地下储能应用的分析模型预测盐岩爬行变形
Pradeep Kumar Shukla1, Tanujit Chakraborty2,3, Mustafa Sari4
1Subsurface Resource Characterization Group (sRCg), Departement of Applied Geophysics, IIT (ISM), Dhanbad, Jharkhand, India. pradeepism13@gmail.com.
Scientific reports
|October 3, 2025
概括
深度神经网络模型,特别是N-BEATS和TCN,准确地预测盐岩爬行变形. 这些先进的时间序列预测方法为地下储存设施的安全提供了更高的准确性.
科学领域:
- 地质科学 地质科学
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 盐岩中的爬行变形对于地下储存设施 (例如,核废物或) 的安全至关重要.
- 盐岩的独特特性 (孔隙性低,高可塑性,自我愈合) 使其适合于此类应用,但预测其长期行为是具有挑战性的.
- 需要准确的时间序列预测来评估在不同限制压力下爬行趋势.
研究的目的:
- 分析和比较各种时间序列预测方法来预测盐岩爬行变形.
- 评估深度神经网络 (DNN) 模型与传统统计模型的性能.
- 为安全和高效的设计和运营提供洞察力,在盐形成的地下储存.
主要方法:
- 使用了从盐岩样本中获得的多阶段三轴爬行数据,这些样本经受限压 (5-35 MPa).
- 应用统计分析 (季节趋势分解,格兰杰因果关系,自动Dickey-Fuller测试,波形连贯性).
- 将DNN模型 (N-BEATS,TCN,RNN,变压器) 与使用RMSE,MAE,MAPE和SMAPE指标的统计基线进行比较.
主要成果:
- 深度神经网络模型,特别是N-BEATS和TCN,在预测爬行变形方面表现出卓越的性能.
- 与传统分析模型相比,N-BEATS和TCN模型取得了显著的准确性改进 (高达15%).
- 统计测试证实了数据的静止性和温度对轴向应变的影响最小.
结论:
- N-BEATS和TCN模型有效地捕捉了盐岩爬行数据中的复杂时间依赖.
- 这些先进的预测技术为评估地下结构的长期稳定性提供了更高的可靠性.
- 这些发现有助于推进地质科学中的时间序列预测应用,用于关键基础设施管理.
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