primary topic: Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data
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HTML Summary of #20681 Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data
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primary topic: Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data

Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data
abstract: Microseismic detection events are critical in monitoring subsurface activities, including hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage and reservoir characterization, to guarantee safe and efficient energy extraction. This presents significant challenges as it generates large amounts of data. Despite the recent machine learning techniques being increasingly integrated into fiber-optic distributed acoustic sensor (DAS) systems to enhance their intelligent recognition capabilities, there is still a need to solve this. In some research, it has been observed that computational speed is time-consuming and overfitting, thus necessitating a more extensive investigation of this analysis. This study proposes a novel approach using DAS data to enhance microseismic event detection's precision, overfitting issue, and interpretability. This approach utilizes a specifically designed neural network architecture. The deep learning approach is highly effective for the real-time management of the substantial amounts of data recorded by DAS equipment. Three phases of research methodology are proposed. The contribution of this research is that spiking neural network architecture for microseismic detection will bring advancements in microseismic monitoring. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
author list: authors
status: peerReviewed
status: published
volume: 1078 L
date: 2024
is part of: repository
is part of: Lecture Notes in Networks and Systems
type: AcademicArticle
type: Article
type: ArticleEPrint
type: EPrint

Springer Science and Business Media Deutschland GmbH
type: Organization

Mohd Safuwan Bin Shahabudin
family name: Shahabudin
given name: Mohd Safuwan Bin
type: Person

Farahida Hanim Mausor
family name: Mausor
given name: Farahida Hanim
type: Person

Nor Farisha Binti Muhamad Krishnan
family name: Krishnan
given name: Nor Farisha Binti Muhamad
type: Person

Lecture Notes in Networks and Systems
issn: 23673370
type: Collection