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.

A system concept for online alignment verification of millimeter-wave, corneal reflectometry is presented. The system utilizes beam scanning to generate magnitude-only reflectivity maps of the cornea at 650 GHz and compares these images to a precomputed/measured template map to confirm/reject sufficient alignment. A system utilizing five off-axis parabolic mirrors, a thin film beam splitter, and two-axis galvanometric mirror was designed, simulated, and evaluated with geometric and physical optics. Simulation results informed the construction of a demonstrator system which was tested with a reference reflector. Similarity metrics computed with the aligned template and 26 misaligned positions, distributed on a 0.5 mm x 0.5 mm x 0.5 mm mesh, demonstrated sufficient misalignment detection sensitivity in 23 out of 26 positions. The results show that positional accuracy on the order of 0.5 mm is possible using 0.462 mm wavelength radiation due to the perturbation of coupling efficiency via beam distortion and beam walk-off.

An efficient method for rapid, non-contact scanning of human cornea is presented. The optics utilize two, 101.6-mm diameter off-axis parabolic mirrors fed by a goniometrically scanned, planar mirror. An evaluation system at 650 GHz was built and demonstrated ~ 1.5 mm beam radius on target and ~ 30 ^o x 30 ^o field of view. The system was inspired by confocal laser scanning principles and represents a system design where image acquisition time is limited by SNR, not mechanical translation.