Audio magnetotelluric (AMT) is commonly used in mineral resource exploration. However, the weak energy of AMT signals makes them susceptible to being overwhelmed by noise, leading to erroneous geophysical interpretations. In recent years, deep learning has been applied to AMT denoising and has shown better denoising performance compared to traditional methods. However, current deep learning denoising methods overlook the characteristics of AMT signals, resulting in reduced denoising accuracy. To enhance the denoising performance of deep learning by better matching the features of AMT signals, we propose a convolutional block attention module (CBAM)-based method for AMT denoising. This method focuses on the features of AMT signals and improves the process from three aspects: 1) in the establishment of the sample set, we adopt a multicomponent form based on the correlation of noise to enable the neural network to explore the potential connections among the components of AMT during the training process, thus constructing a stronger network mapping relationship; 2) in the construction of the neural network, we have introduced the CBAM structure into the residual blocks of the ResNet to enhance the network's feature learning capability by focusing on the characteristics of noise; and 3) in the design of the denoising procedure, we adopt a process of identification before denoising to protect the noise-free data segments from being compromised during the denoising process. Finally, through synthetic, field data experiments, and comparative tests, we demonstrate that our proposed method achieves higher denoising accuracy than some traditional methods and conventional deep learning methods.

Using the Moon as an Earth observation platform for remote sensing offers the benefits of a high orbital altitude and vast surface area, which could provide continuous Earth observation capabilities over great temporal and spatial scales. Over the course of China’s follow-up lunar missions in the next three Five-year plans, the Earth observation instruments will be put on the Moon. However, the understanding of the characteristics of Moon-based Earth observations remains limited. Here, the observational characteristics for a moon-based platform related to the Earth ellipsoid model is studied, which advances previous studies with a spherical Earth assumption. We perform three analyses. First, an integrated coordination transformation equation, which denotes the geometric relationship between a Moon-based platform and the target on Earth is established based on numerical ephemerides and Earth orientation parameters. Second, the explicit expression for the intersection between the line of sight of the sensor and the Earth oblate spheroid is formulated, and the formulae of uncertainties are given. Lastly, a theoretical visible area on the Earth ellipsoid observed from the sensor is derived based on the geometrical relationship between the observation position and the Earth ellipsoid; two special situations are obtained via explicit expressions and series expansion. Based on this, the optimum radius for the spherical assumption of the Earth is obtained. The simulation and analyses reveal that the proposed mathematical derivation aimed at the Earth spheroid can be used to improve the accuracy of studies focused on the geometrical characteristics of Moon-based Earth observations.

Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

Interferometric SAR is an emerging earth observation technique, especially useful in cartography and surface subsidence survey, Differential SAR Interferometry (DInSAR) provides an alternative and complementary method to investigate the land subsidence phenomena, which can provide two dimensional deformations on the line of sight (LOS) of radar on areas of thousands square kilometers. In order to obtain accurate surface subsidence, precise orbit must be provided so as to compensate the reference surface phase and topographic phase. This paper proposed a new method for SAR interferometric baseline rectification specifically for SAR satellites those have no precise orbits. The method based on the imaging geometry of interferometric SAR, during the processing we setup a coordinate system called RXA, where R is a vector directing from the scene center to the corresponding position on the orbit, A is the vector of satellite velocity, X is normal to both R and A to construct a right hand coordinate system. There are two steps in our baseline rectification process, which are perpendicular baseline rectification and parallel baseline rectification, and the singular value of phase frequency was removed by the DIA method. In our study two InSAR pairs are processed, the results show the new proposed method could compensate not only the linear part of the orbit error but also nonlinear part of the orbit error.

In consideration of the importance of reference phase compensation to repeat orbit SAR interferometry and the significance of the reference phase to parameter selection and baseline reestimation in InSAR processing, the effect of reference surface difference that caused reference phase change is studied as well as the method of baseline reestimation. This paper gives out the analytical form of the reference phase and reference phase frequency in different reference surface, studies the difference of the reference phase and reference phase frequency characteristic and at last analyses the possible methods in baseline reestimation using reference phase characteristic. The paper also setup the theoretic basis for baseline reestimation based on reference phase characteristic. The study shows that the baseline reestimation could be achieved by utilizing the characteristics of reference phase, and the proper surface of reference phase must be selected since different surface of reference phase will cause different reference phase.