I also highpass filtered the signal to obtain the high-frequency part of the time series. This saves the mseed data into the script directory as well the plot of the “stream”. I will download the waveforms for the arbitrarily selected event " Mww 6.5 Nevada" located at (38.1689°N, 117.8497° W). For details on how to download waveforms using Obspy, see my previous post.
I will use the Obspy module to download the data from IRIS. In this case, I will download the seismic time series recording a major earthquake. Theoretically, we can shrink those coefficients or simply remove them.
In general, the coefficients that are smaller in value are considered noise. When we take the wavelet transform of a time series, it concentrates the signal features in a few large-magnitude wavelet coefficients. I have covered the basics of multi-resolution analysis using wavelets in the previous post. We can take advantage of that and preserve important signals, and removing nonuniform noise. It localizes features in the signal to a different scale. Wavelets look at the signals in the multi-resolution window. Here, the wavelet-based approach might have some advantages. However, when the data has high-frequency features such as spikes in a signal or edges in an image, the lowpass filter smooths these out. Also, in Fourier-based denoising, we apply a lowpass filter to remove the noise. Most of our real-world measurements are not stationary. Still, the biggest downside of this approach is that the signal needs to be stationary. Fourier Transform is often used in denoising the signals. The little modification in region refinement part according to an application may segment many other type of images such as some type of microarray images.Wavelets analysis can be thought of as a general form of Fourier Analysis. For 2D gel images, you may vary only contrast threshold for your dataset although no change is required in any parameter in case of 2D gel images. The kernel-bandwidth and contrast threshold are two parameter that may need to change according to the image. The other suitable images are quantum dot images, images of cirucular objects in noisy inhomogneous background, malaria parasite images, oil blobs on sea/river, fluroscence cell images similar to, dermoscopy images etc. The method is designed for segmenting the protein blobs from 2D gel images. It contains the methods to extract out the darker or lighter blobs (spots) of various intensities and shapes (including faint/ low intensity spots) from noisy or inhomogeneous background. This code is a part of our work "Nonseparable Wavelet Based Segmentation.
0 kommentar(er)
