DeepSee: Multidimensional Visualizations of Seabed Ecosystems

Document Title

DeepSee: Multidimensional Visualizations of Seabed Ecosystems

Document Information

• Subject Area: Deep-sea ecosystem research and data visualization
• Keywords: Data visualization, scientific visualization, visual analytics, design research, deep-sea research, geovisualization, multidimensional data, deep-sea sediment samples, ecological data analysis, biogeochemical processes

Research Background and Challenges

• Problems or Challenges:

  1. The deep-sea environment is highly complex, and research relies on limited sediment samples, which are critical for understanding biogeochemical cycles.
  2. Current studies mostly use two-dimensional surface maps, lacking visualization tools capable of extending into the third dimension of sediment layers.
  3. Sample data is sparse, distributed across kilometer-scale seabeds, making it difficult to analyze connections between microbial and geochemical parameters.
  4. Deep-sea sampling is expensive, and effectively correlating with historical sample data is crucial for optimizing sampling strategies.

• Significance: The deep sea is the largest ecosystem in the global biosphere. Its associated element cycles (e.g., carbon, sulfur) contribute to understanding Earth system processes, while microbial data may offer insights for medicine, industrial science, and planetary exploration.

• Research Motivation and Related Work:

  1. Current geographic systems (e.g., ArcGIS) are powerful for laboratory analysis but lack rapid integration and 3D visualization capabilities in field environments.
  2. The data visualization field has extensive work on 2D and 3D data presentation, but few tools address on-site deep-sea data integration and real-time analysis.
  3. This study aims to design and validate a visualization tool to propose effective data management and analysis models supporting deep-sea ecological research.

Solution

• Proposed Solution: Developed an interactive workspace named DeepSee, enabling users to upload sediment core data and map images, visualize sampling history, geochemical gradients, and interpolation results between sampling points.

• Innovations:

  1. Provides cross-scale multi-view integration at regional level (2D maps), core level (1D depth bar charts), and sample level (3D interpolation models).
  2. Supports field sampling decisions, accelerates sample data analysis, and provides environmental context information (e.g., previous sampling points and seabed topography data).
  3. Operates in remote locations without internet access, featuring real-time interpolation calculations to predict data values at unsampled locations.

• Implementation Steps and Key Technologies:

  1. Data Visualization Module Design:
     • Map View: Displays 2D terrain maps and sampling history.
     • Core View: Compares physical and chemical attributes at different depths across multiple sampling points.
     • Interpolation View: Visualizes 3D spatial interpolation results of sample data, allowing users to interactively adjust model parameters.
  2. Key Technologies:
     • Built the user interface using 3D visualization libraries Three.js and D3.js.
     • Processed 3D point cloud data and value predictions using Scipy’s linear and natural neighbor interpolation modules.
     • Provided multiple data color representation methods, including uncertainty-suppressing color palettes (VSUP).

Research Results

• Specific Outcomes:

  1. DeepSee achieved unified visualization of diverse data, enabling researchers to integrate and analyze deep-sea core samples more efficiently.
  2. Field deployment aboard research vessels demonstrated the tool’s effectiveness in supporting scientists to make informed sampling decisions and establish new interdisciplinary research workflows.
  3. Proposed transferable design principles (e.g., prioritizing data context, multi-scale data integration, and direct manipulation) as references for similar designs in other fields.

• Advantages Over Existing Solutions:

  1. Compared to traditional tools like ArcGIS, DeepSee is better suited for real-time, on-site decision-making scenarios.
  2. Its intuitive multi-mode interaction design enhances data integration efficiency, user understanding of processes, and decision-making capabilities.

• Experimental and Evaluation Results:

  1. Deployed DeepSee during fieldwork in the Gulf of Mexico, where scientific teams used the tool to plan sampling and analyze existing data (e.g., predicting biogeochemical distributions at unsampled points).
  2. User feedback indicated that DeepSee significantly improved spatial understanding of the study area and facilitated team communication and data sharing.

• Limitations and Future Directions:

  1. Data preparation is complex, and real-time data input capabilities are limited, requiring more efficient toolchain support.
  2. Interpolation methods (e.g., linear or natural neighbor) are relatively simple; future work could integrate more sophisticated geological modeling tools (e.g., Kriging interpolation).
  3. The system lacks capabilities for handling more abstract or high-dimensional data (e.g., microbial classification hierarchies).
  4. Applicability to broader fields (e.g., drone-based ecological surveys or extraterrestrial exploration) remains to be validated.

Conclusion

DeepSee offers an effective solution to address technological gaps in deep-sea ecological research by providing flexible and comprehensive data integration and multidimensional visualization. It supports optimization across the entire workflow, from planning to field decision-making. Future developments will expand its functionality, such as incorporating more complex algorithms and cross-disciplinary visualization schemes, to enable broader ecological and planetary science exploration tasks.