DAS Noise Analysis and Modeling Based on DAS-condition Denoising Diffusion Probabilistic Model

DAS Noise Analysis and Modeling Based on DAS-condition Denoising Diffusion Probabilistic Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article DAS Noise Analysis and Modeling Based on DAS-condition Denoising Diffusion Probabilistic Model First Ning Wu, Second Guangyao Zhang, Third Yue Li, Fourth Jianhua Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8016720/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Deep learning models have shown great promise in data processing tasks due to their powerful feature extraction capabilities and efficient performance, especially for DAS (Distributed Acoustic Sensing) systems, which are characterized by high-density sampling and large volumes of data. However, the performance of these models is often limited by the quantity and quality of available training datasets. In DAS systems, the diverse noise sources and high acquisition costs make it particularly challenging to gather adequate and suitable datasets, especially in the context of extracting DAS noise.To address this challenge, this paper introduces a DAS-Conditional denoising diffusion probabilistic model(DC-DDPM) to simulate several typical noises in DAS systems. DC-DDPM is based on Denoising Diffusion Probabilistic Model (DDPM), which is capable of accurately modeling complex distributions by gradually adding noise to the data and removing it through Markov chains. Unlike standard DDPM, which uses standard Gaussian noise in the forward diffusion process, DC-DDPM incorporates DAS noise's statistical characteristics in the forward diffusion process to enhance backward sampling accuracy, ensuring precise noise generation; Additionally, in the reverse generation stage, DC-DDPM uses extracted time-frequency characteristics of DAS noise as conditional inputs, enabling the flexible generation of noise data that aligns with specific spectral properties, thus enhancing the physical consistency of the generated data. Statistical analysis of the generated data indicates that the noise produced by DC-DDPM closely aligns with real DAS noise in terms of both time-frequency domain characteristics and statistical properties. Our method offers a viable approach to in generating high-quality training sets for DAS noise modeling, which has been validated in real-world processing scenarios. diffusion models Deep generative model DAS noise Seismic data processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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