Seismic kernel matrix and the effect of a surface receiver array on the condition number

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This paper simulates how seismic condition numbers are affected by surface receiver arrays and noise to optimize seismic monitoring project planning and evaluate inversion accuracy.

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The paper studies moment tensor inversion reliability in the context of seismic monitoring, focusing on how the condition number of the seismic kernel matrix behaves near a source location. Using 3D simulations, it evaluates how real seafloor noise and receiver position errors influence inversion misfit, with the goal of estimating (or bounding) the maximum condition number needed for a reliable inversion and informing array geometry planning. A key caveat explicitly stated is that the work is presented as simulation results in a preprint and has not been peer reviewed. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Moment tensor inversion is a valuable tool for understanding what is happening below the surface during hydraulic fracturing or reservoir monitoring. The reliability of the inversion is critical to estimating the source mechanism, and the condition number is used as a measure of accuracy. The lower the condition number, the better the inversion. But how to estimate the maximum condition number to provide a reliable inversion result? I present 3D simulation results of the condition number around the source location and how real seafloor noise and the receiver position error affect the inversion misfit. These simulation results can be used for planning seismic monitoring projects to find an optimal array geometry and to evaluate the inversion accuracy at a given target area.
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Seismic kernel matrix and the effect of a surface receiver array on the condition number | 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 Seismic kernel matrix and the effect of a surface receiver array on the condition number Marcus Landschulze This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3139742/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 Moment tensor inversion is a valuable tool for understanding what is happening below the surface during hydraulic fracturing or reservoir monitoring. The reliability of the inversion is critical to estimating the source mechanism, and the condition number is used as a measure of accuracy. The lower the condition number, the better the inversion. But how to estimate the maximum condition number to provide a reliable inversion result? I present 3D simulation results of the condition number around the source location and how real seafloor noise and the receiver position error affect the inversion misfit. These simulation results can be used for planning seismic monitoring projects to find an optimal array geometry and to evaluate the inversion accuracy at a given target area. 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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