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相关概念视频

Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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相关实验视频

Updated: Jan 9, 2026

Adapting Taylor Dispersion to Measure the Dispersion Coefficient of Electrolyte Solutions via an Accessible Microfluidic Setup
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雷利波分散数据的选择和基于不确定性估计的模型微调.

Xijun Feng1, Fen Zhang2, Wen Peng3

  • 1College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China. 2023020949@stu.cdut.edu.cn.

Scientific reports
|December 5, 2025
PubMed
概括

这项研究引入了雷利波逆转的新型深度学习策略,改进了地下剪切波速度分析. 该方法提高了模型的准确性和稳定性,而不需要钻井数据,这使得它在地震应用中非常有价值.

关键词:
反向问题是反向的问题.神经网络的神经网络的神经网络雷利波的分散是雷利波的分散.不确定性估计估计不确定性

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科学领域:

  • 地质物理学 地质物理学
  • 地震学 地震学
  • 机器学习 机器学习

背景情况:

  • 雷利波逆转对于地下剪切波速度 (SWV) 的确定至关重要,对于地震风险评估,资源勘探和地质工程至关重要.
  • 目前对雷利波逆转的深度学习 (DL) 方法面临挑战,包括有限的概括,严重依赖训练数据和缓慢的融合.

研究的目的:

  • 为雷利波逆转开发一个改进的深度学习策略,解决现有方法的局限性.
  • 为了提高DL模型的准确性,稳定性和适应性,用于SWV结构确定.

主要方法:

  • 一个具有代表性的数据选择策略,使用多个并行预训模型的预测识别高不确定性样本.
  • 一种自动区分驱动的反转方法,用于为选定的数据生成高可靠性伪标签.
  • 微调原始模型与生成的伪标签,一个独立于钻井信息的过程.

主要成果:

  • 在目标区域的预测准确性和模型稳定性的显著改进.
  • 通过合成和实地实验进行验证,证实性能提升.
  • 在复杂的地质环境中以最小的额外成本证明了适应能力.

结论:

  • 拟议的数据选择和模型优化策略有效地增强了基于DL的雷利波逆转.
  • 该方法为SWV结构分析提供了强大而适应性的解决方案,特别是在具有挑战性的地质环境中.
  • 这种方法提供了一种具有成本效益的方法,可以改善地震数据的解释,而不需要钻井数据.