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Discrete Fourier Transform01:15

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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Related Experiment Videos

A Hybrid F-K Domain Feature Extraction and Enhancement Framework for Low-Frequency DAS Production-Logging Data: A

Qiongqin Jiang1, Yichen Zhong1, Wenguang Song1

  • 1School of Computer Science and Engineering, Guangdong Ocean University, Yangjiang 529500, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces a hybrid low-frequency Distributed Optical Fiber Acoustic Sensing (DAS) processing framework for oil and gas production logging. The novel method accurately estimates flow velocity in downhole conditions, achieving low error rates in field tests.

Keywords:
OCSVMdistributed optical fiber acoustic sensingfinite impulse response filteringflow velocity estimationfrequency–wavenumber domainproduction loggingsmall-sample classification

Related Experiment Videos

Area of Science:

  • Geophysics
  • Petroleum Engineering
  • Sensor Technology

Background:

  • Distributed Optical Fiber Acoustic Sensing (DAS) is crucial for downhole production logging due to its ability to capture strain responses in harsh environments.
  • Existing DAS processing methods require enhancement for accurate flow velocity estimation in complex downhole conditions.

Purpose of the Study:

  • To investigate and validate a hybrid low-frequency DAS processing framework for enhanced production logging.
  • To accurately derive flow velocity in oil-water two-phase production wells using advanced signal processing techniques.

Main Methods:

  • A hybrid framework incorporating Finite Impulse Response (FIR) filtering, Frequency-Wavenumber (F-K) domain analysis, and Particle Swarm Optimization (PSO) for One-Class Support Vector Machine (OCSVM) parameter tuning.
  • Application of rule-based feature enhancement and a Support Vector Classifier (SVC) to suppress noise and identify wave propagation features.
  • Utilizing linear regression on enhanced F-K domain data to estimate apparent propagation velocities and subsequently derive flow velocity.

Main Results:

  • The proposed workflow demonstrated high accuracy in estimating flow velocity, with validation errors below 3.13% in a field case study of an oil-water two-phase production well.
  • Successful identification and retention of V-shaped features associated with upgoing and downgoing waves in the F-K domain.
  • Effective suppression of residual noise in the F-K domain through advanced classification and enhancement techniques.

Conclusions:

  • The developed hybrid low-frequency DAS processing framework is feasible for production logging in the investigated well.
  • The study highlights the potential of advanced signal processing, including PSO-tuned OCSVM and SVC, for robust DAS data analysis.
  • Further validation across diverse wells and acquisition settings is recommended to establish general applicability.