Integrated ground-air-space exploration technology of Jingpo Lake lava tubes

As the demand for constructing lunar and Martian bases continues to rise, lava tubes—with their unique advantages such as natural shielding from cosmic radiation, thermally stable conditions, and ready-to-use subsurface living spaces—have become a core consideration for deep space exploration and the selection of long-term extraterrestrial base sites. Compared to traditional methods relying solely on surface rovers or single-sensor orbital identification, future scientific exploration of lunar and Martian lava tubes requires a systematic approach to address key questions: "Where are they?", "What do they look like?", "How do we explore them?", and "How do we use them?" This necessitates the establishment of a comprehensive, multi-dimensional detection system.

Recently, a study published in the journal Space: Science & Technology focused on the Jingpo Lake lava tube as a typical terrestrial analog site. Led by China University of Geosciences (Beijing) in collaboration with domestic and international research teams, including the Aerospace Information Research Institute, Chinese Academy of Sciences; Heilongjiang Second Surveying and Mapping Engineering Institute; Peking University; Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences; Chengdu University of Technology; and the University of Padova, Italy, a comprehensive five-year scientific investigation was conducted. Leveraging the Jingpo Lake lava tube network in Heilongjiang Province and taking advantage of the environmental conditions during winter when liquid water is absent—thereby simulating lunar lava tube exploration scenarios—this study carried out multi-sensor, integrated ground-air-space surveys. For the first time, an integrated ground-air-space exploration scheme for lava tubes was proposed. This scheme integrates multi-source detection technologies, including spaceborne synthetic aperture radar (SAR), UAV-based close-range photogrammetry, airborne LiDAR, in-tube GeoSLAM, hyperspectral LiDAR, and ground-penetrating radar (GPR). A multi-platform, multi-scale collaborative survey of the Jingpo Lake lava tube area was conducted, establishing a complete technical chain from surface skylight identification and subsurface void detection to the precise acquisition of in-tube geometric and spectral information. This work provides a robust terrestrial analog validation foundation and technical reference for future comprehensive lunar lava tube exploration.

Lunar lava tubes are subterranean channels formed during volcanic activity. Their internal morphology, roof stability, wall structure, and subsurface extension characteristics not only completely record the evolutionary processes of volcanic eruptions and lava flows but also directly relate to the usable space and safety assurance of future subsurface bases (Figure 1). Currently, the exploration methods for lunar and Martian lava tubes remain relatively limited. Establishing a comprehensive, transferable, combinable, and verifiable exploration scheme using typical terrestrial lava tube sites has become a crucial prerequisite for supporting the future exploration and utilization of lava tubes on extraterrestrial bodies. The Jingpo Lake lava tubes, formed during the Holocene volcanic period, exhibit typical volcanic landforms, lava tube skylights, subsurface cavities, and lava flow structural features (Figure 2), offering significant analog value. They represent an ideal setting for conducting simulated lunar subsurface space research and testing exploration technologies. Against this backdrop, this study takes the Jingpo Lake lava tubes as the research object, attempting to integrate the exploration approach of "observing distribution from the air, examining structure from the surface, capturing morphology inside the tube, mapping extension underground, and analyzing composition on the walls" within a unified technical framework.

The authors developed an integrated ground-air-space multi-platform exploration framework and elaborated in detail on the division of labor, deployment, and collaborative working mechanisms of various sensors in lava tube investigations (Figure 3). In this study, each sensor performs its specific function while complementing the others: spaceborne SAR is responsible for the large-scale identification of potential lava tube skylights and anomalous surface morphologies; UAV-based close-range photogrammetry and airborne LiDAR work together to acquire high-resolution point clouds of the surface and skylight sidewalls; GeoSLAM focuses on the precise reconstruction of the internal geometry of the lava tube; hyperspectral LiDAR enables accurate classification of wall material composition under dark conditions; and ground-penetrating radar (GPR) is used to detect detailed information on subsurface voids, the lava tube roof, and surrounding strata. Through the joint acquisition and cross-validation of multi-source data, this study achieves a systematic, multi-directional survey of the lava tube distribution area—from surface to interior and from external to internal—providing a practical and feasible technical roadmap for the mission design and technological implementation of future lava tube exploration on extraterrestrial bodies.

At the regional scale, this study employed the HISEA-1 spaceborne SAR for large-scale regional reconnaissance and target identification. Leveraging the all-weather, round-the-clock imaging advantages of spaceborne SAR and its strong adaptability to complex surface environments, the system systematically identified potential lava tube distribution zones, skylight locations, and associated anomalous morphological features. To further improve identification accuracy and effectiveness, the authors applied multi-looking processing and enhanced filtering to the SAR data, effectively suppressing speckle noise, enhancing target edge and morphological information, and significantly improving the interpretability of lava tube skylights (Figure 4). The validation results indicate that high-resolution SAR data possess a certain penetrating capability and can effectively highlight lava tube skylight structures. In the future, the dual-band, sub-meter resolution SAR data onboard Chang'e-7 are expected to support the large-scale detection of skylights in lunar maria. However, given the widespread presence of lunar regolith on the Moon's surface, the potential of this technology still requires further research and validation.

For the detailed investigation of surface morphology and skylight structures associated with lava tubes, this study employed UAV-based close-range photogrammetry and airborne LiDAR technology to acquire high-resolution point cloud data of the areas surrounding skylights and the surface. Targeting different types of lava tube skylights, the study adopted a differentiated acquisition mode combining automated close-range flights and manual supplementary flights. For skylights with higher walls and greater depths, a 3D flight route was first planned using initial terrain data, followed by close-range image acquisition along the skylight sidewalls. For skylights located near the surface with complex surrounding terrain and dense vegetation, manual flight operations were used to enhance the flexibility and safety of data acquisition (Figure 5). Using Metashape software, the study performed a series of processing steps including aerial triangulation, sparse point cloud generation, depth map reconstruction, and dense point cloud construction. The processed UAV data were then fused with airborne LiDAR data, ultimately obtaining the fine 3D morphology of the external and sidewall areas of the skylights. Concurrently, airborne LiDAR also acquired high-precision digital elevation models (DEMs) and topographic point cloud data, providing a solid data foundation for subsequent GPR line layout design, surface relief analysis, and precise surface-subsurface spatial registration. The validation results indicate that the lava tube distribution area exhibits complex surface relief, and this relief shows a clear geomorphic correlation with the orientation of the subsurface lava tubes. Future work should further evaluate the relationship between subsurface lava tube structures and surface topography, providing technical guidance for identifying topographic response features in remote sensing detection of lunar lava tubes.

Considering the low illumination or complete absence of light, narrow spaces, and localized severe collapses inside lunar and Martian lava tubes, this study employed the GeoSLAM handheld laser scanning system for in-tube 3D measurements. By registering these data with point clouds from the entrance and skylight areas, the spatial connection between the exterior and interior was established, resulting in a complete integrated 3D morphological model of the lava tube above and below ground (Figure 6). The authors cropped, denoised, performed feature point matching, and conducted fine registration on the point clouds acquired from close-range photogrammetry, airborne LiDAR, and GeoSLAM. Registration accuracy was ultimately assessed through common point error evaluation. The results of the integrated aboveground-underground modeling indicate that shallow-covered lava tubes and deep-inflated lava tubes exhibit significant differences in internal morphology and structure. Shallow-covered lava tubes are characterized by shallow burial depths, thin roof rock layers, limited collapsed material inside, and good traversability. In contrast, deep-inflated lava tubes have large burial depths, thick roof rock layers, abundant collapsed material inside, and poor traversability. These findings suggest that future lunar lava tube exploration and utilization should focus primarily on shallow-covered lava tubes.

To address the challenge of detecting the vertical structure from the surface to the subsurface of lava tubes, the authors employed multi-frequency ground-penetrating radar (GPR) technology to systematically investigate the lava tube roof, floor, overburden rock thickness, and subsurface cavity morphology. Considering the large scale, prominent orientation characteristics, and complex internal structure of lava tubes, as well as the need to validate potential different intersection angles between future lunar orbiter detection paths and lava tube orientations, the study carefully designed a differentiated survey line layout. This included tangential survey lines along the lava tube orientation, as well as multi-angle crossing survey lines perpendicular to the orientation and along diagonal directions. Repeated observations were conducted using four central frequencies: 50, 100, 200, and 400 MHz (Figure 7). Lower-frequency antennas were primarily used to detect deeper, larger-scale cavity outlines, while higher-frequency antennas were more suitable for characterizing shallow fine structures and roof interface features, achieving precise detection of targets at different depths and scales. During data processing, the authors sequentially applied zero-line correction, background removal, Gaussian window filtering, bandpass filtering, and gain processing to the radar profiles (Figure 8). These steps effectively enhanced effective echo signals, suppressed various interference noises, and significantly improved the reliability of subsurface structure interpretation. The experimental results demonstrate that this method successfully detected vertical multi-layered structural information for surface-covered lava tubes (burial depth 2–5 m, diameter 8–12 m) and deep-inflated lava tubes (burial depth 10–20 m, diameter 25–35 m). These findings fully validate the effectiveness of multi-frequency GPR in detecting vertical layered structures of lava tubes under different diameters and burial depths, providing an important technical reference for the future development of dedicated spaceborne GPR for lava tubes on lunar orbiters and the design of detection schemes.

To address the need for collaborative detection of geometry and material composition inside lava tubes under dark conditions for future exploration, this study further introduced hyperspectral LiDAR technology to conduct simultaneous spatial-spectral detection of the inner walls of the lava tube (Figure 9). This technology can synchronously acquire three-dimensional geometric information and multi-band echo intensity data of the target in completely dark environments, providing powerful technical support for lithological identification of lava tube walls, fine classification of material composition, and coupled analysis of spatial morphology and material composition. Using an acousto-optic tunable filter (AOTF) principle prototype, the study collected hyperspectral laser data within the complex environment of the lava tube. Subsequently, preprocessing, noise removal, and band consistency checks were sequentially performed on the point cloud data from different bands, followed by unsupervised classification processing based on spectral features (Figure 10). The results demonstrate that hyperspectral LiDAR can synchronously and accurately acquire the internal geometry and hyperspectral information of lava tubes, effectively meeting the detection requirements for characterizing internal morphology and distinguishing material composition under dark conditions. This provides a valuable reference and technical guidance for the future development of technologies for internal structure detection of lunar lava tubes and the screening of high-value sampling sites.

Overall, this study successfully achieved a comprehensive, end-to-end detection chain—from large-scale identification of lava tube skylights, fine measurement of background morphology, and precise detection of subsurface structures, to in-tube morphological reconstruction and detailed analysis of wall material composition. The research not only completed a systematic survey of the Jingpo Lake lava tube complex but also innovatively proposed and validated a highly transferable integrated ground-air-space exploration scheme for lava tubes. The results demonstrate that when facing lava tube exploration targets characterized by surface anomalies, subsurface cavity structures, complex internal morphology, and unique wall environments, a single sensor is insufficient for achieving a comprehensive understanding. Only by organically integrating and synergistically leveraging the large-scale target identification capability of spaceborne SAR, the high-precision surface reconstruction capability of close-range photogrammetry and LiDAR, the in-tube spatial scanning capability of GeoSLAM, the geometric/ compositional identification capability of hyperspectral LiDAR under dark conditions, and the subsurface structural penetration capability of multi-frequency ground-penetrating radar (GPR), can a complete exploration loop be achieved—from "target discovery" to "structural reconstruction" to "environmental understanding" of lava tubes. The findings of this research provide important theoretical support and technical reference for payload combination optimization, exploration procedure design, subsurface space modeling, and extraterrestrial base site selection in future lunar and Martian lava tube exploration missions.

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