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Deconvolution01:20

Deconvolution

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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...
236

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DeepSeg: Deep Segmental Denoising Neural Network for Seismic Data.

Naveed Iqbal

    IEEE Transactions on Neural Networks and Learning Systems
    |September 23, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning model, DeepSeg, effectively reduces noise in seismic data by analyzing time-frequency segments. This intelligent framework enhances signal-to-noise ratio (SNR) for better seismic data analysis and interpretation.

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    Area of Science:

    • Geophysics and Signal Processing
    • Artificial Intelligence and Machine Learning

    Background:

    • Noise attenuation is critical for seismic data processing, impacting analysis and interpretation.
    • Enhancing signal-to-noise ratio (SNR) is essential for improving seismic data quality.

    Purpose of the Study:

    • To introduce DeepSeg, a novel deep convolutional neural network for seismic noise reduction.
    • To demonstrate DeepSeg's effectiveness in enhancing seismic signals corrupted by various noise types.

    Main Methods:

    • Developed DeepSeg, a deep convolutional neural network operating on time-frequency domain segments.
    • Trained the network using synthetic seismic data, enabling adaptive noise capture and sparse data representation.

    Main Results:

    • DeepSeg significantly boosts SNR in noisy seismic data, even with overlapping signal and noise frequencies.
    • The method effectively handles correlated and uncorrelated noise, preserving the signal of interest.
    • Achieved superior performance in passive seismic event denoising compared to state-of-the-art methods.

    Conclusions:

    • DeepSeg offers a powerful and versatile solution for seismic noise reduction.
    • The framework demonstrates broad applicability beyond seismic data, including medical imaging and radar signals.
    • The model's ability to train on synthetic data negates the need for real-world training data.