Southwestern Tibet plays a crucial role in the protection of the ecological environment and biodiversity of Southern Asia but lacks energy in terms of both power and fuel. The widely distributed geothermal resources in this region could be considered as potential alternative sources of power and heat. However, most of the known geothermal fields in Southwestern Tibet are poorly prospected and currently almost no geothermal energy is exploited. Here we present a case study mapping the Mapamyum (QP) geothermal field of Southwestern Tibet using audio magnetotellurics (AMT) and magnetotellurics (MT) methods. AMT in the frequency range 11.5–11,500 Hz was used to map the upper part of this geothermal reservoir to a depth of 1000 m, and MT in the frequency range 0.001–320 Hz was used to map the heat source, thermal fluid path, and lower part of the geothermal reservoir to a depth greater than 1000 m. Data from 1300 MT and 680 AMT stations were acquired around the geothermal field. Bostick conversion with electromagnetic array profiling (EMAP) filtering and nonlinear conjugate gradient inversion (NLCGI) was used for data inversion. The AMT and MT results presented here elucidate the geoelectric structure of the QP geothermal field, and provide a background for understanding the reservoir, the thermal fluid path, and the heat source of the geothermal system. We identified a low resistivity anomaly characterized by resistivity in the range of 1–8 Ω∙m at a depth greater than 7 km. This feature was interpreted as a potential reflection of the partially melted magma in the upper crust, which might correlate to mantle upwelling along the Karakorum fault. It is likely that the magma is the heat source of the QP geothermal system, and potentially provides new geophysical evidence to understand the occurrence of the partially melted magmas in the upper crust in Southwestern Tibet.

Non-invasive geophysical exploration methods a play key role in the exploration of ore deposits. In the present study, the audio-frequency magnetotelluric (AMT) method was applied to metallic mineral exploration. The metallic mineral deposit targeted was the recently discovered super large lead–zinc deposit of the Zhugongtang mining area of Hezhang County in the northwestern Guizhou province in China. The main objectives of this study were to estimate the geoelectric strike and generate geoelectric models that estimate both the depth and distribution of resistivity structures across the deposit. To achieve the objectives, we deployed sixty-one (61) AMT survey sites with an interstation separation of 20 m on a 1280 m survey track perpendicular to the geological strike across the Zhugongtang deposit. We operated in fifty-three (53) frequencies in the range 1–10,400 Hz to record the resistivity distribution of subsurface to a depth of more than 1200 m. The results from the AMT data computations estimated the geoelectric strike that varies between NE285° and NE315°. This range of strikes suggested that structures across the deposit are oriented in the NW–SE direction. Obtained two-dimensional (2D) models elucidated a remarkably low resistivity body (0.50 km depth), thus extending to great depth and were interpreted as lead–zinc mineralization. Furthermore, low resistivity (1000 Ωm). Bioclastic limestone and dolomite limestone were inferred and characterized by moderate-high resistivity (>250 Ωm) and were not widely distributed. A unit of basalts was found with moderate resistivity (>63 Ωm). In addition, it was also found that regions with high number of faults tend to have low resistivity values compared to regions with a low fault number. In summary, this case study presents the results of applying an AMT approach to explore the conductivity characteristics of structures across the Zhugongtang deposit. The findings may contribute to the literature about this deposit.

Successful geophysical exploration projects in the Gucheng–Yaxi area located in Gaochun District, Jiangsu Province, China, have been limited partly due to the complex geological conditions of the area and high artificial noise in data acquired using electrical and electromagnetic methods. In this study, we deployed the new anti-interference spread-spectrum-induced polarization method (SSIP) and the audio-magnetotelluric (AMT) method to detect a copper–polymetallic deposit in the area. Two-dimensional inversion results in the Gucheng–Yaxi area revealed a high chargeability anomalous zone on the SSIP profile that coincided with a zone of moderate resistivity located between two resistor bodies on the AMT profile. A follow-up 1200 m drill hole was established at this high-chargeability, moderate-resistivity zone which encountered polymetallic (copper, lead, zinc, gold, and silver) mineralization at a depth of ≥400 m. Drill hole data analysis showed that mineralization occurred interspaced in the marble rock mass at varying depths. Furthermore, several low-resistivity, weak-chargeability sections were revealed and attributed to Cretaceous sediments and faults. These faults are thought to have played a critical role in the polymetallic mineralization genesis. In summary, this study demonstrated the successful of application of SSIP and AMT in detecting a metallic deposit in an area with high artificial noise. Hence, the geophysical prospection potential of the Gucheng–Yaxi area is great.

Successful geophysical exploration works in Gucheng-Yaxi area located in Gaochun District, Jiangsu province, China, have been limited partly due to complex geological condition of the area and high artificial noise in data acquired using electrical and electromagnetic methods. In this work, we deployed the new anti-interference spread spectrum induced polarization method (SSIP) and audio magnetotellurics (AMT) method to detect a copper-polymetallic deposit in the area. Two-dimensional (2D) inversion results at Gucheng-Yaxi Area revealed a high chargeability anomalous zone on SSIP profile that coincided with a zone of moderate resistivity located between two resistor bodies on AMT profile. A follow up 1200 meters drill hole was established at this high chargeability-moderate resistivity zone which encountered polymetallic (copper, lead, zinc, gold and silver) mineralization at the depth ≥ 400 m. Drill hole data analysis showed that mineralization occurs interspaced in marble rock mass at varies depth. Furthermore, several low resistivity-weak chargeability sections were revealed and inferred attributed to Cretaceous sediments and faults. These faults are thought to have played critical role in the polymetallic mineralization genesis. In summary, this work demonstrated the successful of application of SSIP and AMT at detecting a metallic deposit in an area with high artificial noise. Hence the geophysical prospection potential of Gucheng-Yaxi area is great.

Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving acoustic seismic wave equations in geophysical modeling. However, they often encounter challenges such as spectral bias, gradient instability, and slow convergence when applied to complex media, high-frequency wavefields, and singular source terms. To address these limitations, we propose Adaptive SIREN-PINNs, an enhanced framework that integrates learnable amplitude and frequency modulation parameters into the Sinusoidal Representation Networks (SIRENs), which employs periodic activation functions to naturally represent continuous signals and their derivatives, along with a principled weight initialization strategy. This enhancement improves the model’s frequency-aware representation capability, enabling it to better capture both low- and high-frequency components while enhancing gradient propagation stability during training. Furthermore, to mitigate the singularity associated with point sources, we adopt a Gaussian approximation of the Dirac function and reformulate the wave equation in terms of the scattered wavefield, following established regularization practices in seismic wave modeling. Additionally, we conduct a spectral analysis of the Neural Tangent Kernel (NTK) to gain insights into the model’s learning dynamics across different frequency components during training. Numerical experiments conducted on isotropic homogeneous medium, the Marmousi model, and anisotropic TTI/VTI medium demonstrate that the proposed method significantly outperforms conventional PINNs and baseline SIREN-PINNs in terms of wavefield reconstruction accuracy and convergence efficiency. The results validate the effectiveness of the adaptive SIREN mechanism in improving both the expressive power and training stability of PINNs, offering a mesh-free and solver-free alternative to traditional numerical methods.

This study designed and implemented an magnetotelluric signal acquisition and monitoring system (CMT) based on an Internet of Things (IoT) cloud platform. By integrating magnetotelluric monitoring stations, control terminals, and cloud servers, a real-time and efficient monitoring network was constructed. The hardware part of the system adopts a multi-module collaborative design, including signal conditioning circuits, FPGA control modules, DSP processing units, and embedded subsystems, achieving high-precision acquisition and processing of magnetotelluric signals. The software part employs a layered architecture, developing acquisition software, terminal control software, and a cloud platform monitoring system, which support multi-protocol communication, data parsing, and remote interaction. Through server stress testing, consistency testing, and cloud platform functional verification, the results showed that the system performs well under pressure even with limited server hardware bandwidth, with controllable consistency errors compared to the commercial device MTU-5A, and has stable field acquisition performance. The study validated the system’s advantages in real-time performance, reliability, and scalability, providing a feasible technical solution for the field of magnetotelluric monitoring. In the future, the system will be applied to geothermal monitoring.