View source for King fahd university of petroleum and minerals (20240281651). APPARATUS AND METHOD FOR DEEP SEGMENTAL DENOISING NEURAL NETWORK FOR SEISMIC DATA simplified abstract

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


==Organization Name==

[[:Category:king fahd university of petroleum and minerals|king fahd university of petroleum and minerals]]


[[Category:king fahd university of petroleum and minerals]]

==Inventor(s)==

[[:Category:Naveed Iqbal of Dhahran (SA)|Naveed Iqbal of Dhahran (SA)]][[Category: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 20240281651 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 a deep segmental denoising neural network for microseismic data. This technology aims to improve the quality of seismic data recorded from geological formations by removing noise.

* Seismic data recording network with geophones
* Preprocessing stage transforms signal trace to time-frequency representation
* Deep neural network generates denoised signal
* Training based on noisy and clean spectra segments
* Mapping function learned to denoise microseismic signal

'''Key Features and Innovation:'''
* Utilizes deep neural network for denoising microseismic data
* Training based on noisy and clean spectra segments
* Improves the quality of seismic data recorded from geological formations
* Removes noise from recorded signal traces
* Enhances the accuracy and reliability of microseismic data analysis

'''Potential Applications:'''
* Oil and gas exploration
* Geothermal energy exploration
* Earthquake monitoring and analysis
* Structural health monitoring
* Environmental monitoring

'''Problems Solved:'''
* Noise reduction in microseismic data
* Improved accuracy of seismic data analysis
* Enhanced reliability of geological formation monitoring
* Better understanding of subsurface activities
* Increased efficiency in data interpretation

'''Benefits:'''
* Higher quality seismic data
* More accurate analysis of geological formations
* Improved decision-making in resource exploration
* Enhanced safety measures in earthquake monitoring
* Increased efficiency in data processing

'''Commercial Applications:'''
The technology can be applied in industries such as oil and gas exploration, geothermal energy production, and environmental monitoring. It can improve the accuracy and reliability of data analysis, leading to better decision-making and increased efficiency in resource exploration and monitoring processes.

'''Questions about Deep Segmental Denoising Neural Network for Microseismic Data:'''
1. How does the deep neural network in this technology differ from traditional denoising methods?
2. What are the potential limitations of using this technology in real-world applications?

'''Frequently Updated Research:'''
Researchers are continually exploring new ways to enhance the denoising capabilities of neural networks for microseismic data analysis. Stay updated on the latest advancements in deep learning algorithms and their applications in geophysical data processing.


==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.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]