Magnetic resonance sounding (MRS) is a novel geophysical method to detect groundwater directly. By applying this method to underground projects in mines and tunnels, warning information can be provided on water bodies that are hidden in front prior to excavation and thus reduce the risk of casualties and accidents. However, unlike its application to ground surfaces, the application of MRS to underground environments is constrained by the narrow space, quite weak MRS signal, and complex electromagnetic interferences with high intensities in mines. Focusing on the special requirements of underground MRS (UMRS) detection, this study proposes the use of an antenna with different turn numbers, which employs a separated transmitter and receiver. We designed a stationary coil with stable performance parameters and with a side length of 2 m, a matching circuit based on a Q-switch and a multi-stage broad/narrowband mixed filter that can cancel out most electromagnetic noise. In addition, noises in the pass-band are further eliminated by adopting statistical criteria and harmonic modeling and stacking, all of which together allow weak UMRS signals to be reliably detected. Finally, we conducted a field case study of the UMRS measurement in the Wujiagou Mine in Shanxi Province, China, with known water bodies. Our results show that the method proposed in this study can be used to obtain UMRS signals in narrow mine environments, and the inverted hydrological information generally agrees with the actual situation. Thus, we conclude that the UMRS method proposed in this study can be used for predicting hazardous water bodies at a distance of 7–9 m in front of the wall for underground mining projects.

Magnetic resonance sounding (MRS) using the Earth’s magnetic field is a noninvasive and on-site geophysical technique providing quantitative characteristics of aquifers in the subsurface. When the MRS technology is applied in a mine or tunnel for advance detecting the source of water that may cause disastrous accident, spatial constraints limit the size of coil sensor and thus lower the detection capability. In this paper, a coil sensor for detecting the weak MRS signal is designed and the signal to noise (SNR) for the coil sensor is analyzed and optimized. The coil sensor has a rigid structure and square size of 1 m for deploying in a narrow underground space and is cooled at a low temperature of 77 K for improving the SNR. A theoretical calculation and an experimental test in an electromagnetically shielded room (EMSR) show that the optimal design of coil sensor consists of an 80-turn coil and a low-current-noise preamplifier AD745. It has a field sensitivity of 0.17 $\mathrm{fT}/\sqrt{\mathrm{Hz}}$ in the EMSR at 77 K, which is superior to the low temperature Superconducting Quantum Interference Device (LT SQUID) that is the latest application in MRS and the cooled coil with a diameter of 9 cm when detecting the laboratory NMR signal in kHz range. In the field experiment above the Taipingchi Reservoir near Changchun in China, the cooled coil sensor (CCS) developed in this paper has successfully obtained a valid weak MRS signal in high noise environment. The field results showed that the quality of measured MRS signal at 77 K is significantly superior to that at 298 K and the SNR is improved up to three times. This property of CCS makes the MRS instrument more convenient and reliable in a constricted space underground engineering environment (e.g., a mine or a tunnel).

Water damage seriously threatens the safe production of coal mines, so it is necessary to carry out advanced detection to determine the hydrogeological situation, and the preliminary survey often involves the drilling of on-site drill holes in the tunnel. The use of directional drill holes, combined with advanced geophysical prospecting technology, enables advanced water disaster detection with long distance and high precision and is independent of the tunnel environment influence. The transient electromagnetic method (TEM) is highly sensitive to low-resistivity anomalies and plays a crucial role in water damage detection. To address the size limitation of borehole detection, in this study, a small rectangular multi-turn loop borehole advanced detection method was developed (borehole TEM, BTEM) to detect low-resistivity anomalies within 10 m of the borehole in the radial direction. To satisfy the size requirements for borehole detection and the detection distance, a small rectangular multi-turn loop device with a width of 6 cm and a length of 50 cm was designed. To resolve the issue of self-inductance and mutual inductance enhancement caused by multi-turn coils, a uniform full-space low-resistivity abnormal body model was established using the Ansys Maxwell software, and we analyzed the vertical magnetic field component of the rectangular multi-turn small loop at different time points and the transient electromagnetic response of the different turns. Then, we determined the appropriate parameters for the transmitting and receiving device. The developed method was applied to several different experimental scenarios to obtain the electrical distribution of the anomalous body in front of the device, and the measured data were inverted and interpreted to obtain the apparent resistivity-depth profile. The results demonstrate that the inversion results align well with the actual situation, confirming the effectiveness of the BTEM. This research offers a potential solution for borehole advance detection and provides a solid theoretical foundation for further studies.

Optical Coherence Tomography (OCT) is a high-resolution 3D imaging technique widely used in medical diagnostics. However, its increasing resolution leads to massive data volumes, creating challenges in storage, transmission, and real-time access—issues that are further intensified in Internet of Things (IoT) environment. In such settings, where portable OCT devices operate at the edge in remote or resource-limited environments, efficient and secure data handling is critical for real-time diagnostics, remote consultations, and cloud integration. Existing compression methods often struggle to balance efficiency, image quality, and the preservation of diagnostic features, limiting their applicability in constrained IoT infrastructures. To address these challenges, we propose OCT-CSNet, a deep compressed sensing (CS) model tailored for OCT images. By integrating deep learning with traditional CS theory, our model leverages non-parametric prior features to guide both compression and reconstruction stages. In the compression stage, the dual-path compression module efficiently captures the inherent sparsity and structural coherence of OCT images while preserving critical differential information. In the reconstruction stage, we fuse the Proximal Gradient Descent (PGD) algorithm with a deep neural network, incorporating data-driven priors into the iterative optimization process. Extensive experiments show that OCT-CSNet can improve the reconstruction quality under different compression ratios while retaining semantic features that are critical to downstream tasks. In addition, its high computational efficiency and low memory footprint also provide potential for its application in IoT-driven imaging systems.