Machine Learning for the Geosciences: Challenges and Opportunities (1711.04708v1)

Abstract: Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, ML -- that has been widely successful in commercial domains -- offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the ML community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

• The paper introduces novel ML methodologies adapted to heterogeneous geoscience data with variable resolution and limited ground truth.
• It demonstrates advanced event detection, estimation, and long-term forecasting for intricate environmental phenomena.
• It underscores interdisciplinary collaboration by integrating deep learning with theory-guided data science for enhanced model interpretability and performance.

Machine Learning for the Geosciences: Challenges and Opportunities

The intersection of ML and geosciences offers a fascinating domain for research with significant societal relevance. The paper "Machine Learning for the Geosciences: Challenges and Opportunities" by Anuj Karpatne et al. explores the emerging role of machine learning within geosciences, emphasizing both the unique challenges posed by geoscience data and the opportunities for impactful advances in both domains.

Overview of Geoscience Data and Challenges

Geosciences have transitioned into an era characterized by abundant data, thanks to advancements in sensing technologies and computational capacities. Geoscience data is derived from numerous sources, primarily observational data from satellites, oceanic, and terrestrial sensors, and simulation data from physics-based models.

However, these data present several challenges:

• Complex and Amorphous Objects: Geoscience phenomena are inherently complex, with spatial and temporal structures that traditional ML models may struggle to capture.
• Multi-resolution and Noise: Data is often collected at varying spatial and temporal resolutions, adding complexity to data integration and interpretation.
• Non-stationarity and Heterogeneity: Geoscience systems are non-stationary and highly heterogeneous, challenging traditional data modeling approaches.
• Sample Size and Ground Truth Limitations: The limited availability of ground truth data and small sample sizes necessitate innovative ML techniques for effective analysis.

These challenges necessitate novel ML methodologies that synergize with the unique characteristics of geoscience data sets.

Role of Machine Learning

The paper identifies several broad categories of geoscience problems where ML can play a critical role:

1. Characterizing Objects and Events: ML algorithms can automate the detection and analysis of patterns corresponding to geoscience objects, such as climate events or geological features, potentially improving understanding and prediction of such occurrences.
2. Estimating Geoscience Variables: Supervised ML algorithms can estimate critical, hard-to-measure geoscience variables via indirect observations, thereby aiding resource management and policy-making decisions with more frequent and accurate data.
3. Long-term Forecasting: ML’s ability to model non-linear systems can assist in predicting long-term trends in geoscience data, such as climate variables, which is critical for planning and adaptation strategies.
4. Mining Relationships and Causal Discovery: Identifying and understanding relationships in geoscience data can elucidate processes like teleconnections, offering new insights into spatiotemporal dynamics across large geographic and temporal scales.
5. Causal Attribution: ML approaches can aid in distinguishing human influences from natural variability in geosciences, facilitating effective policy action.

Emerging Cross-cutting Themes

Two significant emerging themes in aligning ML with geosciences include:

• Deep Learning: Leveraging hierarchical models like CNNs and RNNs can unlock new capabilities for processing the intricate patterns present in geoscience data. However, these methods require adaptation to account for the often limited training data.
• Theory-Guided Data Science: Integrating domain knowledge with data-driven approaches can enhance model accuracy and interpretability. This approach can reconcile the limitations of purely data-driven or purely physics-based models, yielding more robust frameworks.

Conclusion

Collaborative research between ML experts and geoscientists is imperative for realizing the full potential of machine learning methodologies in addressing geoscience challenges. The paper highlights the importance of interdisciplinary practices and community building, which are essential for innovative solutions that advance both fields of paper. As these disciplines continue to intersect, new paradigms of data analysis and model building will emerge, propelling further scientific understanding and technological development.