Two hyperparameters, the mean Moho depth and the Moho density contrast, must be specified before the gravity inversion of Moho. Incorrect estimation will impact inversed Moho morphology. The purpose of this study is to present a new gravimetric Moho inversion method. The key improvement of the new method is to accurately estimate the Moho density contrast, based on a linear relationship between the depth of known points and gravity observations. The method is illustrated by a synthetic experiment where the estimated density contrast differs from the true value by only 0.0011 , showing a 93% improvement compared to the initial estimate. The results of processing the noise data show that, our method's accuracy is minimally affected by noise, but is sensitive to the number of known points. Nevertheless, when using only 10 known points, there is still a 50% probability of obtaining a solution with a root mean square (RMS) less than 1 km. When the number of points is greater than 64, the effect of the uniformity of the point distribution is almost negligible. In the real case, we employed the proposed method to invert the South China Sea (SCS) Moho depth. The Moho model reveals that, there is a distinct zoning feature at Moho depth in the SCS, and the 13.5 km isodepth line indicates the continent-ocean boundary. Furthermore, the RMS of the difference between the gravimetric Moho model and the seismological data is 1.64 km, which is primarily attributed to the lateral variation of the density contrast. 

An Improved Method to Moho Depth Recovery From Gravity Disturbance and Its Application in the South China Sea

Abstract This study validates the ultra-high-degree gravity field models in terms of the internal error estimate and the external precision. Internal error estimate is evaluated by geoid error degree variance and cumulative geoid height errors. The evaluation of the external precision is carried out using observed ground gravity data sets in Qinghai-Tibet Plateau and Sichuan Basin of mainland China. The results show that the geoid degree error is at the millimeter level, and the accumulated geoid error is at the centimeter level, and SGG-UGM-2 has the highest precision in terms of geoid errors. However, in terms of gravity anomaly, the GECO model has the highest precision of 37.080 mGal in the Qinghai-Tibet Plateau, and after terrain correction, its precision can reach 28.907 mGal, an improvement of 22%. In the Sichuan Basin, EGM2008 performs best with a precision of 7.202 mGal; the precision of EGM2008 becomes 6.648 mGal after terrain correction. These results mean that the terrain correction must be considered in the area where topography varies largely, while when the terrain is relatively flat, the effect of terrain on gravity can be ignored. 

/pdf/evaluation-of-ultra-high-degree-gravity-field-models-a-case-2d51b358.pdf

Evaluation of ultra-high degree gravity field models: a case study of Eastern Tibetan Plateau and Sichuan Province

The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly for data with different spectrum information in the frequency domain, has not been extensively explored. Therefore, band-limited SRBFs, which have good localization characteristics in the frequency domain, were taken as the main tool in this study. To determine the optimal expansion degree of SRBFs for gravity data, a residual and a priori accuracy comparative analysis method was proposed. Using this methodology, the expansion degrees of terrestrial and airborne data were determined to be 5200 and 1840, respectively, and then a high-resolution geoid model called ColSRBF2023 was constructed for use in Colorado. The results indicated that ColSRBF2023 had a standard deviation (STD) of 2.3 cm with respect to the GSVS17 validation data. This value was 2–6 mm lower than models obtained using different expansion degrees for gravity data and models from other institutions considered in this study. Furthermore, when comparing it with the validation geoid model on a 1′ × 1′ grid, ColSRBF2023 exhibited an STD value of 2.4 cm, which was also the best among the examined models. These findings highlight the importance of determining the optimal expansion degree of gravity data, particularly for constructing high-resolution gravity field models in rugged mountainous areas.

Regional Gravity Field Modeling Using Band-Limited SRBFs: A Case Study in Colorado

Abstract Marine gravity anomalies are crucial parameters and elements for determining coastal and ocean geoid, tectonics and crustal structures, as well as offshore studies. This study aims to derive and develop a marine gravity anomaly model over Malaysian seas from multi-mission altimetry data. Universiti Teknologi Malaysia 2020 Mean Sea Surface Model is computed based on along-track data from nine satellite missions, incorporating TOPEX, Jason-1, Jason-2, ERS-2, Geosat Follow on (GFO), Envisat-1, CryoSat-2, SARAL/AltiKa, and Sentinel-3A. The data exploited are from 1993 to 2019 (27 years). Residual gravity anomaly is computed using Gravity Software, and two-dimensional planar Fast Fourier Transformation method is applied. The evaluation, selection, blunder detection, combination, and re-gridding of the altimetry-derived gravity anomalies and Global Geopotential Model data are demonstrated. Cross-validation procedure is employed for data cleaning and quality control using the Kriging interpolation method. Then, cross-validation procedure is applied to the tapering window width 200, which adopting the GECO model denotes the optimum gravity anomaly with root mean square errors in the range of ± 4.2472 mGal to ± 6.0202 mGal. The findings suggest that the estimated marine gravity anomaly is acceptable to be implemented in the marine geoid determination and bathymetry estimation over Malaysian seas. In addition, the results of this study are valuable for geodetic and geophysical applications in marine areas. 

/pdf/optimised-gravity-anomaly-fields-from-along-track-multi-1sfatjas.pdf

Optimised gravity anomaly fields from along-track multi-mission satellite altimeter over Malaysian seas

The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly for data with different spectrum information in the frequency domain, has not been extensively explored. Therefore, band-limited SRBFs, which have good localization characteristics in the frequency domain, were taken as the main tool in this study. To determine the optimal expansion degree of SRBFs for gravity data, a residual and a priori accuracy comparative analysis method was proposed. Using this methodology, the expansion degrees of terrestrial and airborne data were determined to be 5200 and 1840, respectively, and then a high-resolution geoid model called ColSRBF2023 was constructed for use in Colorado. The results indicated that ColSRBF2023 had a standard deviation (STD) of 2.3 cm with respect to the GSVS17 validation data. This value was 2–6 mm lower than models obtained using different expansion degrees for gravity data and models from other institutions considered in this study. Furthermore, when comparing it with the validation geoid model on a 1′ × 1′ grid, ColSRBF2023 exhibited an STD value of 2.4 cm, which was also the best among the examined models. These findings highlight the importance of determining the optimal expansion degree of gravity data, particularly for constructing high-resolution gravity field models in rugged mountainous areas. 

The development of the global geopotential model (GGM) broadens its applications in ocean science, which emphasizes the importance for model assessment. We assess the recently released high-degree GGMs over the South China Sea through heterogeneous geodetic observations and synthetic/ocean reanalysis data. The comparisons with a high resolution (~ 3 km) airborne gravimetric survey over the Paracel Islands show that XGM2019e_2159 has relatively high quality, where the standard deviation (SD) of the misfits against the airborne gravity data is ~ 3.1 mGal. However, the comparisons with local airborne/shipborne gravity data hardly discriminate the qualities of other GGMs that have or truncated to the same expansion degree. Whereas, the comparisons with the synthetic/ocean reanalysis data demonstrate that the qualities of the values derived from different GGMs are not identical, and the ones derived from XGM2019e_2159 have better performances. The SD of the misfits between the mean dynamic topography (MDT) derived from XGM2019e_2159 and the ocean data is 2.5 cm; and this value changes to 7.1 cm/s (6.8 cm/s) when the associated zonal (meridian) geostrophic velocities are assessed. In contrast, the values derived from the other GGMs show deteriorated qualities compared to those derived from XGM2019e_2159. In particular, the contents computed from the widely used EGM2008 have relatively poor qualities, which is reduced by 3.9 cm when the MDT is assessed, and by 4.0 cm/s (5.5 cm/s) when the zonal (meridian) velocities are assessed, compared to the results derived from XGM2019e_2159. The results suggest that the choice of a GGM in oceanographic study is crucial, especially over coastal zones. Moreover, the synthetic/ocean data sets may be served as additional data sources for global/regional gravity field assessment, which are useful in regions that lack of high-quality geodetic data.

An assessment of recently released high-degree global geopotential models based on heterogeneous geodetic and ocean data

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yihao Wu

Author Tools

Chat about Author

Papers

An assessment of recently released high-degree global geopotential models based on heterogeneous geodetic and ocean data