3D Detection and Morphological Analysis of Extraterrestrial Lava Tubes: A Case Study in Haikou, China

Highlights

• Novel Application of Handheld LiDAR: First application of SLAM100 handheld laser scanning technology for detailed 3D documentation of terrestrial lava tubes as analogs for extraterrestrial exploration, offering high flexibility in narrow underground spaces.
• Comprehensive 3D Reconstruction Workflow: Integration of statistical filtering, manual denoising, and ContextCapture software for generating high-precision triangular mesh models from point cloud data, effectively preserving surface textures and flow patterns.
• Skylight Detection Algorithm: Development of a grid-based method for automatic detection and morphological analysis of skylights (circular openings), critical entry points for future robotic missions to lunar/Martian lava tubes.
• Geomorphological Analysis: Detailed documentation of internal lava tube features including side terraces, lava benches, rope-like flow textures, and collapse structures, providing analog data for planetary cave studies.
• Technical Validation: Successfully mapped Xianren Cave (~40m surveyed) and Wolong Cave (3km total length, 40m surveyed section) with 5cm absolute accuracy, demonstrating feasibility for future extraterrestrial applications.

Methodology

The study establishes a complete workflow for lava tube 3D detection and analysis:

1. Data Acquisition:

   • Sensor: SLAM100 handheld laser scanner (270°×360° FOV, 5cm absolute accuracy, Class 1 laser)
   • Study Sites: Xianren Cave (仙人洞) and Wolong Cave (卧龙洞) in Haikou, Hainan Province
   • Characteristics:
     • Xianren Cave: Large space, sediment blockage, side terraces, lava benches, rope-like flow textures, ceiling air pockets, bat habitats, local collapse with columnar jointed basalt
     • Wolong Cave: ~3km total length, 6m width, <3m height in surveyed section
   • Operational Parameters: 2.5-hour battery life, -10°C to +45°C working temperature

2. Point Cloud Processing:

   • Denoising: Statistical outlier filtering combined with manual cropping to remove noise points while preserving geometric details
   • Registration: Automatic point cloud alignment and stitching using SLAM technology
   • Segmentation: Separation of wall points, floor deposits, and ceiling structures

3. 3D Modeling:

   • Software: ContextCapture (Bentley Systems) for automated mesh generation
   • Process:
     • Automatic point cloud partitioning into blocks
     • Triangular mesh generation for surface modeling
     • Model stitching and texture mapping
   • Output: High-fidelity 3D mesh models preserving surface roughness and geological features

4. Skylight Detection:

   • Grid-based Method:
     • Voxelization of point cloud data
     • Detection of circular openings in ceiling structures
     • Morphological parameter extraction (diameter, depth, shape irregularity)
   • Geomorphological Mapping: Identification of collapse features, flow textures, and structural weaknesses

Study Sites

Xianren Cave (仙人洞):

• Location: Haikou, Hainan Province, China
• Morphology: Large interior space with sediment accumulation blocking parts of the passage
• Features:
  • Side terraces and lava benches (flow residue)
  • Rope-like flow textures on walls
  • Air pockets in ceiling (current bat habitats)
  • Local collapse revealing columnar jointed basalt
• Survey Length: ~40m

Wolong Cave (卧龙洞):

• Location: <1km from Xianren Cave
• Total Length: ~3km
• Surveyed Section: ~40m length, ~6m width, <3m height
• Characteristics: Large continuous pipe structure with intact flow features

Experimental Results

3D Modeling Accuracy:

• Absolute Accuracy: 5cm (as per SLAM100 specifications)
• Resolution: Millimeter-level detail capture for flow textures
• Coverage: Complete 360° spherical scanning coverage in narrow passages

Geomorphological Findings:

Skylight Characteristics:

• Distribution: Sparse and scattered along pipe length
• Morphology: Approximately circular openings in point cloud cross-sections
• Detection Rate: Successful identification of all major openings in surveyed sections

Key Findings

1. Operational Feasibility:

   • Handheld SLAM LiDAR proves highly operable in narrow, confined underground spaces (tunnels/caves) where traditional tripod-mounted TLS is impractical
   • Lightweight design (372×163×106mm) enables single-operator deployment in rough terrain

2. Data Quality:

   • Successfully captured fine-scale geological features (rope textures, cm-scale roughness) despite 5cm absolute accuracy specification
   • Point cloud density sufficient for detecting small-scale collapse precursors (cracks, thin ceilings)

3. Morphological Insights:

   • Lava tube cross-sections show elliptical to rectangular variations related to flow regime and cooling history
   • Skylights form preferentially at structural weak points where ceiling thickness is minimal

4. Extraterrestrial Analog Value:

   • Haikou basaltic lava tubes provide excellent analogs for lunar sinuous rilles and Martian lava tubes
   • Documented 3D morphological database supports design of exploration robots and habitat modules
   • Skylight detection method applicable to orbital LiDAR data for preliminary lunar/Martian cave identification

Limitations and Future Work

• Survey Coverage: Current study limited to accessible portions (~40m) of longer cave systems; complete mapping requires multiple survey campaigns
• Lighting Constraints: Dark underground environments require artificial illumination, potentially affecting visual texture mapping quality
• Data Gaps: Highly irregular surfaces (rough walls, piles) cause localized point cloud gaps due to occlusion
• Future Directions:
  • Integration with photogrammetry for true-color texture mapping
  • Development of automated classification algorithms for lava tube features using machine learning
  • Application to rover-mounted LiDAR systems for autonomous planetary cave exploration