18171551. APPARATUS AND METHOD FOR DEEP SEGMENTAL DENOISING NEURAL NETWORK FOR SEISMIC DATA simplified abstract (KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS)

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APPARATUS AND METHOD FOR DEEP SEGMENTAL DENOISING NEURAL NETWORK FOR SEISMIC DATA

Organization Name

KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS

Inventor(s)

Naveed Iqbal of Dhahran (SA)

APPARATUS AND METHOD FOR DEEP SEGMENTAL DENOISING NEURAL NETWORK FOR SEISMIC DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 18171551 titled 'APPARATUS AND METHOD FOR DEEP SEGMENTAL DENOISING NEURAL NETWORK FOR SEISMIC DATA

Simplified Explanation

The patent application describes an apparatus, method, and computer readable storage medium for denoising microseismic data using a deep segmental denoising neural network.

  • The apparatus includes a seismic data recording network with geophones to record microseismic waves, a preprocessing stage, and a deep neural network.
  • The preprocessing stage transforms the recorded signal trace into a time-frequency representation.
  • The deep neural network generates a denoised signal from the time-frequency representation by learning a mapping function from noisy spectra to clean spectra segments.

Key Features and Innovation

  • Utilizes a deep segmental denoising neural network for denoising microseismic data.
  • Preprocessing stage transforms signal trace into time-frequency representation.
  • Deep neural network learns mapping function from noisy to clean spectra segments.

Potential Applications

  • Oil and gas exploration
  • Seismic monitoring for natural disasters
  • Structural health monitoring in civil engineering

Problems Solved

  • Noise reduction in microseismic data
  • Improved accuracy in seismic data analysis
  • Enhanced signal quality for geological studies

Benefits

  • Increased precision in detecting seismic events
  • Better understanding of subsurface geological formations
  • Enhanced safety measures in seismic monitoring

Commercial Applications

      1. Deep Segmental Denoising Neural Network for Microseismic Data

This technology can be applied in various industries such as oil and gas exploration, civil engineering, and natural disaster monitoring. By denoising microseismic data, companies can improve their seismic analysis accuracy, leading to better decision-making processes and increased safety measures.

Prior Art

For prior art related to deep neural networks in seismic data analysis, researchers can explore academic journals, conferences, and patent databases for similar technologies and methodologies.

Frequently Updated Research

Researchers are constantly exploring new techniques and algorithms to enhance the denoising capabilities of neural networks in microseismic data analysis. Stay updated with the latest research in deep learning and seismic data processing for potential advancements in this field.

Questions about Deep Segmental Denoising Neural Network for Microseismic Data

How does the deep segmental denoising neural network improve the accuracy of seismic data analysis?

The deep segmental denoising neural network learns a mapping function from noisy to clean spectra segments, resulting in enhanced signal quality and reduced noise in microseismic data, leading to improved accuracy in seismic data analysis.

What are the potential applications of this technology beyond oil and gas exploration?

This technology can also be applied in civil engineering for structural health monitoring and in natural disaster monitoring for early detection of seismic events, showcasing its versatility and wide range of potential applications.


Original Abstract Submitted

An apparatus, computer readable storage medium, and method for deep segmental denoising neural network for microseismic data is described. The apparatus includes a seismic data recording network with geophones each having a seismic data receiver and configured to record microseismic waves as a seismic trace received from a geological formation, a preprocessing stage and a deep neural network. The preprocessing stage transforms the recorded signal trace to a time-frequency representation as real number values. The deep neural network generates a denoised signal from the time-frequency representation. The deep neural network is trained based on a segment of noisy spectra and a clean spectra segment to learn a mapping function that generates the segment of the denoised microseismic signal.

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