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相关概念视频

Basic Operations on Signals01:22

Basic Operations on Signals

Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Definition of z-Transform01:26

Definition of z-Transform

The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is an essential analytical tool, analogous to the Laplace transform used in continuous-time systems. It plays a crucial role in the analysis of signals and systems, complementing the discrete-time Fourier transform. Both the z-transform and the Laplace transform convert differential or difference equations into algebraic equations, simplifying the process of solving complex problems.
Properties of the z-Transform I01:17

Properties of the z-Transform I

The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
Transformations of Functions III01:20

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...

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相关实验视频

Updated: Jun 4, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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对大规模数据驱动的全波形逆转的实证研究.

Peng Jin1,2, Yinan Feng3, Shihang Feng3

  • 1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, USA. pqj5125@psu.edu.

Scientific reports
|August 28, 2024
PubMed
概括

大数据显著增强了对地震全波形逆转 (FWI) 的深度学习模型. 对大型,多样化的数据集进行培训可以提高准确性和概括性,表明模型容量必须与数据大小相适应,以获得最佳结果.

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科学领域:

  • 地质物理学 地质物理学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 深度学习模型显示出解决复杂的地质物理问题,如完全波形逆转 (FWI) 的希望.
  • 大规模,多样化的数据集对FWI深度学习的影响仍未得到充分探索.
  • OPENFWI为调查大数据在FWI中的作用提供了宝贵的资源.

研究的目的:

  • 实证地评估大数据对应用到FWI问题上的深度学习模型的影响.
  • 使用大型,多结构合成数据集量化FWI的绩效改进.
  • 确定模型容量和数据集大小之间的关系,以获得最佳的FWI性能.

主要方法:

  • 深度学习模型在OPENFWI数据集的10个2D子集上进行了训练和评估,总计有47万个地震数据和速度图对.
  • 使用平均绝对误差 (MAE),平均平方误差 (MSE) 和结构相似度指数 (SSIM) 来评估性能.
  • 实验包括对组合数据集与单个子集的训练和离开一个的概括测试之间的比较,以及不同的模型容量.

主要成果:

  • 与分开的数据集相比,联合OPENFWI数据集的培训提高了MAE的13.03%,MSE的7.19%,SSIM的1.87%.
  • 离开一个的概括测试显示,MAE的平均改善率为28.60%,MSE的平均改善率为21.55%,SSIM的平均改善率为8.22%.
  • 增加模型容量与数据大小一起产生了显著的性能增长,最大的模型比最小的模型高20.06% (MAE),13.39% (MSE) 和0.72% (SSIM).

结论:

  • 大数据,特别是像OPENFWI这样的大型和多样化的合成数据集,可以明显提高深度学习模型的性能,从而实现完全的波形反转.
  • 在数据驱动的FWI中,最佳性能需要将模型容量与培训数据集的大小和复杂性进行协同扩展.
  • 这项研究证实了大数据在提高FWI准确性和概括性的有效性,为更强大的地质物理地下成像铺平了道路.