通过使用物理信息约束的深度学习,在微型电网上进行磁提前建模
Kunpeng Wang1, Chongxin Yuan2, Hongjin Zhu3
1Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu, 610059, China.
这项研究将深度学习与磁铁化 (MT) 预先建模相结合,以克服效率限制. 这种新方法利用细网来实现高精度,同时大大减少地质调查的计算时间.
科学领域:
- 地质物理学 地质物理学
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 由于计算的复杂性,传统的磁铁电流 (MT) 预先建模面临微型电网的效率瓶.
- 深度学习 (DL) 对MT建模的方法通常会损害物理准确性,并努力利用细网准确性.
研究的目的:
- 通过将DL与传统方法协同使用,开发高精度,高效的MT前建模解决方案.
- 解决现有的MT前建模技术在处理复杂的地下物质和计算需求方面的局限性.
主要方法:
- 使用立方线线插曲生成现实的合成电阻模型,以捕捉地下体积效应和渐进变化.
- 在严格的物理约束下开发和训练U形深度学习模型 (Swin-UNet与Swin变压器骨干),用于多任务MT前响应网的精细化.
主要成果:
- 综合框架有效地利用细网,在MT前建模中实现高精度.
- 与传统方法相比,显著减少了前建模时间.
- 验证了模型保留物理信息的能力,并准确地适应真实MT数据.
结论:
- 该研究为MT前建模提供了一种高效和高精度的解决方案,克服了传统的计算瓶.
- 这项工作开创了AI在精细电网前建模中的应用,为地球物理建模和解释提供了一个新的范式.
- 为跨学科的地质物理建模和数据解释提供了有价值的参考.
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