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Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Hybrid multi-resolution network for DAS data denoising.

Li Ding¹, Haoran Sun², Haoliang Chen³

¹School of Software Engineering, Jilin Technology College of Electronic Information, Jilin, China.

|June 10, 2025

Summary

This summary is machine-generated.

Distributed Acoustic Sensing (DAS) seismic data often suffers from noise, obscuring valuable information. This study introduces a Hybrid Multi-Resolution Network (HMR-Net) to effectively denoise DAS records and improve signal quality for seismic exploration.

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

Geophysics
Seismic Exploration
Signal Processing

Background:

Distributed Acoustic Sensing (DAS) offers significant potential in seismic exploration.
Real-world DAS seismic data is often contaminated by significant noise, degrading data quality and hindering subsequent analysis.
Existing denoising methods struggle to effectively preserve subtle seismic signals within noisy DAS records.

Purpose of the Study:

To develop an effective denoising approach for Distributed Acoustic Sensing (DAS) seismic data.
To address the challenge of noise obscuring valuable information and reducing the signal-to-noise ratio (SNR) in DAS records.
To enhance the performance of seismic data processing and interpretation tasks.

Main Methods:

Proposes a Hybrid Multi-Resolution Network (HMR-Net) for DAS data denoising.
HMR-Net extracts features across multiple resolutions to capture complex seismic characteristics.
Employs error-resilient up-sampling and down-sampling to optimize feature extraction and minimize sampling-induced losses.
Utilizes a synthesized dataset combining real DAS noise and simulated seismic records for training and validation.

Main Results:

Demonstrates substantial noise suppression in DAS seismic records.
Significantly enhances the signal-to-noise ratio (SNR) of processed seismic data.
Validated effectiveness on both synthetic and actual field seismic records.

Conclusions:

The proposed HMR-Net is an effective method for denoising DAS seismic data.
The network successfully preserves and enhances underlying seismic signals.
This approach improves the reliability of seismic exploration using DAS technology.