Owing to its sensitivity to hydrothermal alteration and geothermal fluids, the distribution of subsurface electrical conductivity provides critical information for characterizing geothermal systems. For geothermal exploration, the controlled-source electromagnetic (CSEM) technique provides a crucial tool to image the subsurface resistivity structures, especially in areas with strong cultural noise. Usually, land-based CSEM surveys are carried out with dozens of operating frequencies to enhance spatial resolution. However, the 3D inversion of multifrequency CSEM data using the commonly used direct solver is challenging with limited computational resources. In this study, we implement a practical inversion strategy for interpreting 3D multifrequency CSEM data. By combining a hybrid direct-iterative solver with the inexact Gauss-Newton optimization, 3D inversion of CSEM data involving multiple frequencies can be performed effectively on typical workstations. First, we test the effectiveness of the developed approach using synthetic multifrequency 3D CSEM data generated for a simplified geothermal model. The inversion strategy is then applied to the multifrequency CSEM field data collected for the geothermal exploration at Tianzhen region in Shanxi Province, China. The comparison between the inversion results using the sparse data set (six frequencies) and the dense data set (12 frequencies) highlights the necessity of using data from more frequencies in the inversion to improve the resolution. The resulting 3D resistivity model clearly delineates the clay alteration layers and shallow thermal reservoirs of the potential high-temperature geothermal system within the study area, while its deep heat source is not revealed owing to the limited investigation depth of the survey.

Deep geothermal reservoirs have great potential for exploitation and are characterized by high temperatures, high stress, and strong heterogeneity. However, these reservoirs contain widely and continuously distributed dominant flow channels with high permeability, predisposing these reservoirs to the formation of dominant flow, which notably decreases the efficiency of heat extraction. Focusing on the dominant flow in fractures, this study provides a definite concept, systematically reviews current studies, and puts forward suggestions for future research. It is expected that this study will serve as a reference for the sustainable, high-quality development of deep geothermal resources.

《地球科学》(ISSN 1000-2383;CN42-1874/P) (原刊名《地球科学——中国地质大学学报》ISSN 1000-2383;CN42-1233/P)，创刊于1957年，是由教育部主管、中国地质大学主办的综合性地学学术理论期刊，月刊，2015年改名为《地球科学》,她以反映中国地质大学教学与科研最新的、高水平的基础地质、应用地质、资源与环境地质及地学工程技术研究成果为主要任务。

The central part of the Zhangjiakou area is occupied by the Yanshan orogenic basin. A large number of piedmont faults developed over time, controlling the exposure of geothermal anomalies. The fluid chemistry characteristics and their influence on the heat generation mechanism of the medium- and low-temperature convective geothermal field in the area are not fully understood. In this study, the geothermal fluid was sampled and tested, and the hydrogeological background conditions were analyzed. The results show that the sulfate in geothermal fluid originates from the dissolution of gypsum or H₂S oxidation in deep magma. The geothermal fluid in the faulted basin flows upward after deep circulation and interacts with shallow groundwater. The main source of geothermal fluid is atmospheric precipitation. The temperature of the hot reservoir is between 82 °C and 121 °C, and the depth of geothermal water circulation is more than 3200 m. It can be seen that the geothermal resources in this area are formed by the long-term contact of residual magma, geothermal heating and mechanical heating of neotectonic movement after atmospheric precipitation recharge.

Thermal infrared remote sensing offers a cost-effective means of regional geothermal reconnaissance, yet a fundamental challenge remains: isolating the weak geothermal surface signal (typically 1–3 °C) from dominant surface noise introduced by seasonal temperature cycles (annual amplitude > 20 °C), topographic variability, land cover heterogeneity, and irregular cloud-affected satellite sampling. Conventional single-scene or arithmetic-mean approaches are highly susceptible to these confounding factors and frequently produce pseudo-anomalies that obscure genuine geothermal targets. To overcome this limitation, we propose a physics-based time-series framework in which a nonlinear annual temperature variation model, T(t) = T0 + A·sin(2πt/τ + φ), is fitted to multi-temporal Landsat 8 thermal infrared data via the Levenberg–Marquardt algorithm. Applied to ~50 cloud-free scenes (2021–2022) processed on the Google Earth Engine over the Shanxi Graben System, northern China, the model simultaneously retrieves the background temperature parameter T0 and seasonal amplitude A—two physically interpretable quantities that encode distinct geothermal signatures more robustly than simple temporal statistics. Sub-regional corrections for the elevation (−4 °C/100 m above 800 m), aspect (R² > 0.95 in piecewise linear segments), and slope further suppress topographic pseudo-anomalies prior to anomaly extraction. Over known high-temperature geothermal fields (Tianzhen and Yanggao; >100 °C at 100 m depth), the method reveals clear T0 offsets of +1–2 °C (3–5% relative) and amplitude deficits of ~2 K (5–10% relative) relative to the background, with model-fitted T0 values averaging ~2 °C higher than arithmetic means due to the correction for seasonal sampling bias. Combined with 5 km fault-proximity buffers, extracted anomaly zones align well spatially with known geothermal sites and major structural corridors of the graben system. However, deeper low-temperature systems (45–50 °C at 300–500 m depth) produce ambiguous signals below the ~1.5 K detection threshold, indicating inherent limitations for deeply buried resources. The fully reproducible, training-data-free workflow is implementable via open satellite archives and cloud computing platforms, making it a transferable low-cost tool for structurally controlled geothermal reconnaissance across extensional basins worldwide.

With increasing worldwide attention to green and sustainable energy, thermal infrared remote sensing technology has gained significant popularity for detecting geothermal anomalies, as it can overcome the limitations of traditional ground surveys. This study explores the potential application of thermal infrared images in geothermal exploration within the Datong Basin. We mainly utilized Landsat-8 images to obtain the actual land surface temperature (LST), hydrothermal alteration, and linear structures of the Datong Basin. Radiative transfer equation algorithm (RTE), principal component analysis (PCA), and interactive interpretation method were applied in this study. The results show that LST retrieval through the RTE method accurately reveals geothermal anomalies in the Datong Basin. Five areas with distinct high-LST values were identified as geothermal anomaly zones based on field investigation, including Xiejiatun, Gushancun, Taipingpu, Shuitongsi, and Wenjiayao–Yuanjialiang. Effective estimation of hydrothermal alteration zones (dominated by clays, OH⁻/H₂O, and carbonates) in the basin was achieved using the PCA method and band combinations. In total, 394 linear structures were obtained through interactive interpretation, including 45 concealed structures. All of these linear structures were associated with deep-seated faults. The basin’s primary controlling structures are the Yunmen Mountain piedmont fault (F1-1) and the northern margin of Xiong’er Mountain faults (F1-2 and F1-3), with F1-1 and F1-3 playing a key role in regional thermal regulation. The high-LST premium geothermal target zones of Shuitongsi and Gushancun were identified based on remote sensing interpretations and geothermal geological conditions. Furthermore, strong consistency was verified between the remote sensing predictions and four deep drilling temperature field measurements. This study confirms that remote sensing is an effective approach for geothermal potential identification, providing a scientific basis for future sustainable resource exploration in other regions.