Yuan Yuan

Euler deconvolution is widely used to estimate the three-dimensional locations of geological sources from magnetic anomaly data. However, traditional Euler deconvolution is commonly performed on planar gridded data, whereas magnetic surveys in mountainous and hilly areas are often acquired over undulating terrain. Reducing such data to a horizontal plane before derivative calculation can introduce transformation errors, and derivative calculation by the conventional FFT-based (wavenumber-domain) method becomes less suitable under variable topographic conditions. To address these limitations, this study proposes an equivalent source method based on correlation imaging for calculating the spatial derivatives required by Euler deconvolution directly from magnetic anomaly data acquired over undulating terrain. An improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is further introduced to suppress spurious Euler solutions and retain valid source location estimates. Synthetic model experiments show that the proposed equivalent source method yields more accurate derivatives than the conventional FFT-based method under undulating terrain conditions. The improved DBSCAN algorithm effectively removes spurious solutions while preserving clustered solutions associated with geological sources. The proposed workflow was further applied to magnetic data from a coal fire zone in Shenmu, Shaanxi Province, China, to estimate the 3D locations of underground magnetic sources related to underground coal fires. The interpreted source locations are consistent with surface validation evidence, demonstrating the applicability of the proposed method for magnetic anomaly interpretation in complex topographic settings.

Characterizing the spectral composition of artificial light at night (ALAN) within urban green spaces (UGS) is vital for ecological conservation, yet traditional sensors often lack the requisite spatial and spectral resolution for fine-scale analysis. To address this gap, this study leverages high-resolution multispectral nighttime light (NTL) data from the SDGSAT-1 to perform a fine-scale characterization of lighting across diverse UGS typologies. We developed UGS-STUNet, a semantic segmentation framework based on Swin Transformer architecture, to accurately extract five UGS categories from Google Earth imagery. Two specialized spectral indices, blue-to-green (B/G) and green-to-red (G/R) ratios, were derived from SDGSAT-1 NTL data to quantify the lighting’s spectral composition. Application in Shanghai demonstrated that UGS-STUNet achieved a precision of 85.72%, significantly outperforming existing methods. Our findings reveal that street trees are subjected to the highest red-light intensity and the lowest B/G and G/R ratios due to their proximity to roadway illumination. In contrast, forest patches and belts exhibit higher spectral ratios, indicating a relatively higher exposure to blue and green wavelengths. This study provides a robust and scalable method for monitoring the spectral quality of urban nightscapes, offering critical insights for sustainable urban planning and lighting mitigation strategies to safeguard global biodiversity and public health.

High-precision gravity observation is key to studying Earth’s gravity field, which reflects matter distribution and movement of matter within the Earth’s interior, on its surface, and in outer space. Compared to traditional mobile observations, continuous gravity array observations can effectively improve both accuracy and temporal resolution. The China Earthquake Administration has deployed a total of 115 continuous gravimeters, including superconducting gravimeters (SG), atom gravimeters (AG), and spring gravimeters, preliminarily establishing a continuous gravity observation network. However, the accuracy differences among different types of instruments constrain the comprehensive application of the data. To address this, it is imperative to conduct research on the accuracy evaluation of various gravimeters. This study innovatively introduces the Complementary Ensemble Empirical Mode Decomposition (CEEMD) algorithm to decompose and reconstruct the measured data from continuous gravimeters, extracting the solid Earth tide signal. Subsequently, an external conformity accuracy evaluation is performed by comparing it with theoretical tidal models, thereby achieving instrument accuracy assessment. To verify the feasibility and superiority of the CEEMD algorithm, this paper first designs a combined simulation signal experiment, in which the performance of Bessel filtering, Kalman filtering, Variational Mode Decomposition (VMD), Empirical Mode Decomposition (EMD), and CEEMD in extracting tidal frequency band signals is comparatively analyzed. Finally, these methods are applied to process observational data from three gravimeters (SG, AG, and DZW relative gravimeter (DZW)) at the Yilan station, extracting the solid Earth tide signal and comparing its external conformity accuracy with theoretical models. The results show that these algorithms can effectively extract the solid Earth tide signal, with the evaluated accuracy of the three instruments consistently ranked as follows: SG > AG > DZW. However,The CEEMD method demonstrated optimal performance in the accuracy evaluation of the three gravimeters, with the following external conformity accuracies achieved: SG: 1.83 μGal, AG: 2.59 μGal, DZW: 3.83 μGal.

Marine gravity anomalies play an important role in geophysics applications. To observe high-precision and high-resolution gravity anomalies, a high-performance marine gravimeter is key. At present, the marine gravimeters widely used in the world have different measurement principles, such as the two-axis stable platform gravimeter, gimbaled inertial navigation gravimeter, and strapdown gravimeter. However, the performances of marine gravimeters with different measurement principles show different precision levels in real applications. A synchronized comparison test on the same platform is the most direct method for evaluating their performance, which is a relative analysis method. To absolutely evaluate the performance of different kinds of marine gravimeters, a new method is presented to remove the residual noise from the measured free air gravity anomaly to establish an “air truth” free air gravity anomaly. Synchronous measurements with different gravimeters were carried out in the north area of the South China Sea, measured three times over a round trip, and the highest-precision free air gravity anomaly measured by GT-2M, SAG-2M, and ZL11-1A was chosen to establish the “air truth” free air gravity anomaly. The external consistency of the free air gravity anomaly upon the removal of residual noise of frequencies 0.03 Hz to 0.06 Hz improved, and the three separate standard free air gravity anomalies of each gravimeter were the same with no deviation. The weighted result of the three average values of GT-2M, SAG-2M, and ZL11-1A is the established “air truth” free air gravity anomaly, which can be used as a standard to estimate the performance of marine gravimeters with different kinds of principles.

Gravity and gravity gradient data are widely used in geodesy, geodynamics, oil and mineral exploration, aided navigation, etc. The measured gravity and gravity gradient data include high-frequency noise caused by instrument system error, environmental conditions, and human factors. Separating noise from the measured gravity and gravity gradient data is one of the most challenging tasks in processing the measured data. Traditional low-pass digital filters can remove the noise of an individual component in real-time, which cannot realize the joint filtering of gravity and gravity gradient data. As a post-processing method, the inversion-based methods can combine gravity and all the gradient components to remove the noise constrained by the Laplace equation. However, a real-time filter method that combines gravity and all gradient components is needed for some special applications, such as submarine gravity and gravity gradient aided navigation. In this study, gravity and gravity gradient data are combined in establishing system equation and measurement equation of the standard Kalman filter, and denoised in real-time by the improved Kalman filter. Based on the model test, this method can simultaneously remove the noise in gravity and gravity gradient data in real-time, and ensure denoising performance. Finally, we apply this method to real gravity and gravity gradient data in St. George’s Bay, Canada, acquired by Bell Geospace, and compared the denoised results by Full Tensor Noise Reduction and Gaussian low-pass filter, which verified that the performance of improved Kalman filter is well in real-time joint filtering of gravity and gravity gradient data.