An enhancing precision method for downward continuation of gravity anomalies

Chen, Mengtao; Yang, Wencai

doi:10.1016/j.jappgeo.2022.104753

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…According to continuation theory in Eq. 1, if the gravity anomaly g up (x, y) caused by an unknown underground field source is known in the observation plane z up , the gravity anomaly g down (x, y) below the plane can be computed using (Peters, 1949;Chen and Yang, 2022):…”

Section: Problem For Gravitymentioning

confidence: 99%

“…It is widely used in, for example, the exploration of minerals, oil and gas basin analysis, and assisted navigation (Cordell and Grauch, 1985;Blakely, 1996;Adewumi and Salako, 2018). At present, many algorithms exist for the DC problem, which can be roughly divided into three categories: the spatial domain interpolation method (e.g., Cooper, 2004;Luo and Wu, 2016), the wavenumber domain method (e.g., Liu et al, 2009;Zhou et al, 2022), and the integral iterative method (e.g., Ma et al, 2013;Tai et al, 2016;Chen and Yang, 2022). Although many existing methods have achieved certain effects in some geophysical interpretations, two unsolved problems remain in existing methods.…”

Section: Introductionmentioning

confidence: 99%

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Stable downward continuation of the gravity potential field implemented using deep learning

Chen

et al. 2023

Front. Earth Sci.

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Downward continuation (DC) of the gravity potential field is an important approach used to understand and interpret the density structure and boundary of anomalous bodies. It is widely used to delineate and highlight local and shallow anomalous sources. However, it is well known that direct DC transformation in the frequency domain is unstable and easily affected by high-frequency noise. Recent deep learning applications have led to the development of image recognition and resolution enhancement using the convolutional neural network technique. A similar deep learning architecture is also suitable for training a model for the DC problem. In this study, to solve the problems in existing DC methods, we constructed a dedicated model called DC-Net for the DC problem. We fully trained the DC-Net model on 38,400 pairs of gravity anomaly data at different altitudes using a convolutional neural network. We conducted several experiments and implemented a real-world example. The results demonstrate the following. First, several validation data subset and test data prediction results indicate that the DC-Net model was sufficiently trained. Moreover, it performed better than the traditional strategy in refining the upscaling of low-resolution images. Second, we performed tests on test datasets with changing levels of noise and demonstrated that the DC-Net model is noise-resistant and robust. Finally, we used the proposed model in a real-world example, which demonstrates that the DC-Net model is suitable for solving the DC problem and delineating the detailed gravity anomaly feature near the field source. For real data processing, noise in the gravity anomaly should be reduced in advance. Additionally, we recommend noise quantification of the gravity anomaly before network training.

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Section: Problem For Gravitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stable downward continuation of the gravity potential field implemented using deep learning

Chen

et al. 2023

Front. Earth Sci.

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An iterative Tikhonov regularization downward continuation of magnetic anomaly

Li,

Zhao,

Zhang

2024

Journal of Applied Geophysics

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GOCE Downward Continuation to the Earth’s Surface and Improvements to Local Geoid Modeling by FFT and LSC

et al. 2023

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One of the main applications of the gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite data is their combination with local gravity anomalies for geoid and gravity field modeling purposes. The aim of the present paper was the determination of an improved geoid model for the wider Hellenic area, using original GOCE SGG data filtered to retain only useful signals inside the measurement bandwidth (MBW) of the satellite. The filtered SGGs, originally at the satellite altitude, were projected to a mean orbit (MO) and then downward continued to the Earth’s surface (ES) in order to be combined with local gravity anomalies. For the projection to an MO, grids of disturbing gravity gradients from a global geopotential model (GGM) were used, computed per 1 km from the maximum satellite altitude to that of the MO. The downward continuation process was then undertaken using an iterative Monte Carlo (MC) simulated annealing method with GGM gravity anomalies on the ES used as ground truth data. The final geoid model over the wider Hellenic area was estimated, employing the remove–compute–restore method and both Fast Fourier Transform (FFT) and Least Squares Collocation (LSC). Gravity-only, GOCE-only and combined models using local gravity and GOCE data were determined and evaluation of the results was carried out against available GNSS/levelling data in the study area. From the results achieved, it was concluded that even when FFT is used, so that a combined grid of local gravity and GOCE data is used, improvements to the differences regarding GNSS/levelling data by 14.53% to 27.78% can be achieved. The geoid determination with LSC was focused on three different areas over Greece, with different characteristics in the topography and gravity variability. From these results, improvements from 14.73%, for the well-surveyed local data of Thessaly, to 32.88%, over the mountainous area of Pindos, and 57.10% for the island of Crete for 57.10% were found.

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An enhancing precision method for downward continuation of gravity anomalies

Cited by 3 publications

References 16 publications

Stable downward continuation of the gravity potential field implemented using deep learning

Stable downward continuation of the gravity potential field implemented using deep learning

An iterative Tikhonov regularization downward continuation of magnetic anomaly

GOCE Downward Continuation to the Earth’s Surface and Improvements to Local Geoid Modeling by FFT and LSC

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