Mixed-model gravity representations for small celestial bodies using mascons and spherical harmonics

Wittick, Patrick Thomas; Russell, Ryan P.

doi:10.1007/s10569-019-9904-6

Cited by 11 publications

(9 citation statements)

References 32 publications

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“…The asteroid 433 EROS and its gravitational field have been studied by several authors using various modeling algorithms (C. Chen et al., 2019; D'Urso, 2014; Fukushima, 2017; Wittick & Russell, 2019). Several polyhedral representations of EROS models are available at https://sbn.psi.edu/pds/resource/nearbrowse.html.…”

Section: Numerical Examples and Resultsmentioning

confidence: 99%

Modified Parker's Method for Gravitational Forward and Inverse Modeling Using General Polyhedral Models

2021

JGR Solid Earth

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Parker's method (Parker, 1973), initially introduced by the author for the rapid calculation of gravitational and magnetic anomalies, of 2D and 3D complexly layered models based on the FFT algorithm, is by no doubt one of the most widely applied algorithms in potential field data processing and interpretation (Nabighian, Ander, et al., 2005;. By raising the interface functions of a layered model to the E n th power and transforming into Fourier series using the FFT algorithm, potential anomalies on a horizontal plane above the layered model can be computed much more efficiently than using space-domain numerical integration techniques. The method has been proved particularly useful in solving forward problems such as gravimetric terrain correction and isostatic correction (

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Section: Numerical Examples and Resultsmentioning

confidence: 99%

Modified Parker's Method for Gravitational Forward and Inverse Modeling Using General Polyhedral Models

2021

JGR Solid Earth

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“…Unfortunately, the accelerations can grow inaccurate at field points near the individual mascons [24]. Hybrid mascon models offer a slightly more robust alternative to the pure mascon approach by representing The Physics-Informed Neural Network Gravity Model Generation III each mascon with a low fidelity spherical harmonic model, but this incorporates additional complexity in regression and remains prone to both challenges of the mascon and spherical harmonic models [25].…”

Section: Other Analytic Modelsmentioning

confidence: 99%

Reinforcement Learning and Orbit-Discovery Enhanced by Small-Body Physics-Informed Neural Network Gravity Models

Martin

Schaub

2022

AIAA SCITECH 2022 Forum

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Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) show considerable potential in their capacity to identify solutions to complex differential equations. Over the past two years, much work has gone into the development of PINNs capable of solving the gravity field modeling problem -i.e. learning a differentiable form of the gravitational potential from position and acceleration estimates. While the past PINN gravity models (PINN-GMs) have demonstrated advantages in model compactness, robustness to noise, and sample efficiency; there remain key modeling challenges which this paper aims to address. Specifically, this paper introduces the third generation of the Physics-Informed Neural Network Gravity Model (PINN-GM-III) which solves the problems of extrapolation error, bias towards low-altitude samples, numerical instability at high-altitudes, and compliant boundary conditions through numerous modifications to the model's design. The PINN-GM-III is tested by modeling a known heterogeneous density asteroid, and its performance is evaluated using seven core metrics which showcases its strengths against its predecessors and other analytic and numerical gravity models.

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“…Unfortunately, a direct quantitative comparison with prior literature is not viable, as a common validation practice has not been established and, further, the assumptions on the information available on the bodies made in other works differ (here no knowledge on the shape model is assumed). The closest candidate for a first, preliminary, comparison is perhaps, in our case, the work by Wittick and Russell (24) who provide a detailed analysis on the performance of a state-of-the art method representing the gravity field of Eros, albeit informed by a shape model. They make use of a hybrid model joining mascon and spherical harmonics modelling.…”

Section: Learned Modelsmentioning

confidence: 99%

“…In the interior field, and specially close to the surface, the accuracy is troublesome as it becomes unclear whether a point is underneath or just over the body surface. These drawbacks can be reduced using hybridized techniques (24) and introducing knowledge on the shape and composition of the asteroid. A geodesyNet, as shown in the result section, is able to achieve comparable performances to that of mascon hybridized techniques while using no prior information on the body shape and returning a continuously differentiable representation of the internal body density.…”

Section: Mascon Modelsmentioning

confidence: 99%

“…Unfortunately this approach loses its appeal as the body irregularities become more important (21,22). Other options such as mascon models (23,24) and polyhedral gravity (25,26) can overcome some of these difficulties, but also introduce other requirements such as the need for a precise shape model or the assumption of a homogeneous internal density.…”

Section: Introductionmentioning

confidence: 99%

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Geodesy of irregular small bodies via neural density fields: geodesyNets

Izzo¹,

Gómez²

2021

Preprint

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Asteroids' and comets' geodesy is of increasing interest in a wide range of fields ranging from astronomy to potential future mining prospects, as well as being essential to successful proximity operations of spacecraft. However, the problem of inferring the internal density distribution of irregular bodies from gravity measurements is challenging and ill posed, with the added difficulty of finding compact representations of the gravitational field surrounding them. We present a novel approach based on artificial neural networks, socalled geodesyNets, and present compelling evidence of their ability to serve as accurate geodetic models of highly irregular bodies using minimal prior information on the body. The approach does not rely on the body shape information but, if available, can harness it. GeodesyNets learn a three-dimensional, differentiable, function representing the body density, which we call neural density field. The body shape, as well as other geodetic properties, can easily be recov-1

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Mixed-model gravity representations for small celestial bodies using mascons and spherical harmonics

Cited by 11 publications

References 32 publications

Modified Parker's Method for Gravitational Forward and Inverse Modeling Using General Polyhedral Models

Modified Parker's Method for Gravitational Forward and Inverse Modeling Using General Polyhedral Models

Reinforcement Learning and Orbit-Discovery Enhanced by Small-Body Physics-Informed Neural Network Gravity Models

Geodesy of irregular small bodies via neural density fields: geodesyNets

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