The uncertainty of CRUST1.0

Author:

Sjöberg Lars E.12ORCID,Abrehdary Majid1

Affiliation:

1. Division of Mathematics, Computer and Surveying Engineering , 387497 University West (HV) , Trollhättan , Sweden

2. Division of Geodesy and Satellite Positioning , Royal Institute of Technology (KTH) , Stockholm , Sweden

Abstract

Abstract As crustal structure models based on seismic and other data are frequently used as a-priori information for further geophysical and geological studies and interpretations (e. g., for gravity inversion), it is important to accurately document their qualities. For instance, the uncertainties in published crustal structures deeply affect the accuracies of produced Moho contour maps. The qualities in seismic crustal models arise from several factors such as the survey method, the spatial resolution of the survey (for example the spacing of the shot points and the recording stations), and the analytical techniques utilized to process the data. It is difficult to determine the uncertainties associated with seismic based crustal depth/Moho depth (MD) models, and even more difficult to use such data for estimating the Moho density contrast (MDC) and its accuracy. However, there is another important observable available today, namely global satellite gravitational data, which are fairly homogeneous v. r. t. accuracy and distribution over the planet. For instance, we find by simple error propagation, using the error covariance matrix of the GOCE TIM5 gravitational model, that this model can determine the MD to a global RMS error of 0.8 km with a resolution of about 1° for a known MDC of 200  kg / m 3 \text{kg}/{\text{m}^{3}} . However, the uncertainty in the MDC will further deteriorate the result. We present a new method for estimating the MD and MDC uncertainties of one model by comparing it with another (correlated or uncorrelated) model with known uncertainty. The method is applied in estimating the uncertainty for the CRUST1.0 MD model from four global models (CRUST19, MDN07, GEMMA1.0, KTH15C), yielding mean standard errors varying between 2 and 4.9 km in ocean regions and between 3.2 and 6.0 km on land regions with overall means of 3.8±0.4 and 4.8 ± 0.6 km 4.8\pm 0.6\hspace{0.1667em}\text{km} , respectively. Also, starting from the KTH15C MDC model, the mean standard error of CRUST1.0 MDC was estimated to 47.4 and 48.3  kg / m 3 \text{kg}/{\text{m}^{3}} for ocean and land regions, respectively.

Publisher

Walter de Gruyter GmbH

Subject

Earth and Planetary Sciences (miscellaneous),Engineering (miscellaneous),Modeling and Simulation

Reference26 articles.

1. Abrehdary, M., Sjöberg, L. E., Bagherbandi, M. 2015. Combined Moho parameters determination using CRUST1.0 and Vening Meinesz-Moritz model. Journal of Earth Science, 26(4), 607–616.

2. Abrehdary, M., Sjöberg, L. E., Bagherbandi, M., Sampietro, D. 2017. Towards the Moho depth and Moho density contrast along with their uncertainties from seismic and satellite gravity observations. J. Appl, Geod., 11, 231–247.

3. Abrehdary, M., Sjöberg, L.E. 2020. Estimating a combined Moho model for marine areas via satellite altimetric-gravity and seismic crustal models. Studia Geophysica et Geodaetica, 64(1), 1–25.

4. Amante, C., Eakins, B.W. (2009). ETOPO1 1 Arc-Minute global relief model: Procedures, data sources and analysis – NOAA technical memorandum NESDIS NGDC-24.

5. Aitken, A. R. A., Salmon, M. L., Kennett, B. L. N. 2013. Australia’s Moho: a test of the usefulness of gravity modelling for the determination of Moho depth. Tectonophysics, 609, 468–479.

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