FTAN (frequency-time analysis)是一种由两个台站之间互相关函数求出群、相速度频散的方法。 群到时 \tau (\omega_ {0}) 是高斯滤波器的中心频率（由 包络函数 的峰值确定中心频率）的函数，使得群速度 U (\omega_ {0})=r/\tau (\omega_ {0}) ，其中r为站间距。 定义 瞬时频率 为Analitic signal在时间τ时的相位变化的速率，使用瞬时频率代替中心频率。 \phi_ {a}=2\pi N,N=0,\pm1,\pm2..... 对于噪声互相关来说 \phi_ {a}=2\pi N-\pi/4,N=0,\pm1,\pm2.....

Ftan is a former municipality in the district of Inn in the Swiss canton of Graubünden. On 1 January 2015 the former municipalities of Ardez , Guarda , Tarasp , Ftan and Sent merged into the municipality of Scuol .

Frequency-Time ANalysis (FTAN) in python. Here we compute compute group speeds using FTAN as carried out in my 2020 thesis, Multi-Component Ambient Noise Tomography of the Auckland Volcanic Field. To test it out, run the run.py script. Requirements python 3, obspy, matplotlib.pyplot, csv, and numpy

# FTAN_Python 此软件基于Python实现了时频分析(FTAN)方法,通过互相关函数能够 求出台站对之间的频散曲线.无需安装,通过运行ftan.py即可进行计算. 系统要求: 此软件基于Python3运行,同时还需要安装Obspy,Numpy,Scipy, Matplotlib等模块.

2024年10月10日 · The seamless integration of MCFA and IAM empowers the FTAN network with enhanced capabilities to detect minor regions and edges accurately. Experiments on datasets like LEVIR-CD and DSIFN-CD demonstrate superior performance by outperforming existing models in F1 scores and IoU metrics.

The AFTAN is a modification of previously developed and widely used FTAN (e.g., [3],[4],[5]. The AFTAN converts time series of cross-correlation functions into frequency-time diagramms of signal power as a function of frequency and group time …

为了应对上述挑战，这封信提出了一种创新的 CD 频时注意力网络 （FTAN），它包含两个高级模块，包括多维卷积频率注意力模块 （MCFA） 和交互式注意力模块 （IAM）。 具体来说，MCFA 模块对于通过合并多尺度空间和频域特征来提高 CD 的灵敏度至关重要。 作为 MCFA 的补充，IAM 汇总了与类别相关的令牌，并处理来自不同时间段的交叉关注信息。 MCFA 和 IAM 的无缝集成使 FTAN 网络具有更强大的功能，可以准确检测次要区域和边缘。 在 LEVIR-CD 和 DSIFN-CD 等 …

面波频散测量的频时分析法-总结了面波频时分析 (FTAN)的主要成果,如移动窗分析法、多重滤波法、残差频散测量法、时间变量滤波法,力图阐述各方法间的联系。 对滤波参数的选择,详细介绍了常相对带宽、均等显示滤波、线性时间分辨、最佳滤波等,其中线性时间分辨是本文首次提出的新方法。 对频时分析中的显示技术作了必要的讨论,最后介绍了.

Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and incorporate transfer learning.

2024年9月1日 · This paper introduces a frame-to-frame temporal alignment network (FTAN) for few-shot action recognition. Specifically, ATA module is proposed to optimize the distance metric between actions by implementing the frame-to-frame …