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Machine Learning for the Geosciences: Challenges and Opportunities

Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, Vipin Kumar

arXiv:1711.04708v1cs.LGcs.AIcs.CVphysics.geo-ph

TL;DR

Geoscience applications pose urgent societal problems, but their physical, heterogeneous, sparsely labeled, and spatio-temporally structured data challenge traditional machine learning. This article surveys data sources, challenges, applications, and methodological directions, concluding that close collaboration between geoscientists and machine-learning researchers is central to successful applications.

  • Problem

    Geoscience problems involve physical processes, heterogeneous space-time behavior, and limited ground-truth labels, creating challenges for traditional machine-learning methods.

    Source: Property 2: Spatiotemporal Structure · p. 3, Property 4: Heterogeneity in Space and… · p. 4, Property 9: Paucity of Ground Truth · p. 5

  • Method

    The article surveys geoscience data sources, machine-learning challenges, application categories, methodological directions, and cross-cutting research themes.

    Source: §4.1 · p. 6, §4.2 · p. 6

  • Results

    The survey identifies promising machine-learning approaches for geoscience problems, including spatio-temporal pattern mining, multi-task learning, adaptive ensembles, semi-supervised learning, and active learning.

    Source: §4.1 · p. 6, §4.2 · p. 6

  • Takeaways & Limitations

    Successful geoscience machine-learning applications are generally driven by geoscience questions and close collaboration throughout research.

    Source: §6 · p. 9

    The survey is not exhaustive, and traditional deep-learning methods are limited by the paucity of labeled geoscience data.

    Source: §6 · p. 9, §5.1 · p. 9

Abstract

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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, machine learning (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 machine learning (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.

1 INTRODUCTION

Geoscience problems have major societal relevance, and expanding data availability creates opportunities for machine learning. However, physical processes and complex boundaries make these problems unlike standard commercial applications, motivating new methods and close collaboration.

  • Geosciences addresses urgent problems including climate impacts, air pollution, disasters, resource availability, and geological hazards.Source: §1 · p. 1
  • Improved sensing, large-scale Earth-system simulations, and internet-based data access have shifted geosciences from data-poor to data-rich.Source: §1 · p. 1
  • Publicly available geoscience big data offers machine learning opportunities for problems of substantial societal relevance.Source: §1 · p. 1
  • Physical laws, amorphous boundaries, and complex latent variables create challenges that differ from standard commercial data-science problems.Source: §1 · p. 1
  • Close collaboration between machine-learning researchers and geoscientists can advance both disciplines through cross-fertilization of ideas.Source: §1 · p. 1
  • The article surveys geoscience data, machine-learning challenges, application areas, cross-cutting research themes, and collaboration practices.Source: §1 · p. 2

2 SOURCES OF GEOSCIENCE DATA

Geoscience data come from observations and physics-based simulations, spanning diverse spatial and temporal scales. Their varied formats and physical structure require identifying dataset properties before selecting suitable analytics methods.

  • Geoscience processes are complex dynamic systems whose components interact through changing physical processes.Source: §2 · p. 2
  • Geoscience data are obtained primarily from observational sensors and simulations generated by physics-based Earth-system models.Source: §2 · p. 2
  • Earth observations include satellite measurements of surface and atmospheric variables collected by public agencies and private organizations.Source: §2.1 · p. 2
  • In-situ sensors provide direct measurements from ground, atmospheric, and ocean platforms, while proxy records extend some observations thousands of years into the past.Source: §2.1 · p. 2
  • Dataset representation varies by source, including geo-registered images, time series, and irregularly sampled measurements, so data properties should guide analytics choices.Source: §2.1 · p. 2
  • Physical laws govern relationships among Earth-system variables, but exact solutions are often difficult for complex real-world systems.Source: §2.2 · pp. 2–3

3 GEOSCIENCE CHALLENGES

Geoscience objects and phenomena are difficult to represent because they have amorphous boundaries and complex, multiscale forms in continuous spatio-temporal fields.

  • Geoscience objects have amorphous spatial and temporal boundaries, unlike crisply defined objects in many conventional machine-learning domains.Source: §3 · p. 3
  • Waves, flows, and coherent structures can deform dynamically and appear across multiple scales in continuous spatio-temporal fields.Source: Property 1: Objects with Amorphous… · p. 3
  • These properties make geoscience patterns substantially more complex than patterns in discrete spaces commonly handled by machine-learning algorithms.Source: Property 1: Objects with Amorphous… · p. 3, §3 · p. 3

Property 2: Spatiotemporal Structure

Geoscience data are structured across space and time, with nearby observations often related and distant regions sometimes coupled. This violates i.i.d. assumptions and creates high-dimensional modeling demands.

Property 4: Heterogeneity in Space and Time

Geoscience processes vary substantially across space and time, producing heterogeneous data that challenge globally consistent machine learning models. Local or regional models may therefore be needed for more homogeneous observations.

Property 5: Interest in Rare Phenomena

Geoscience often focuses on rare events with major societal or ecological impacts, but their infrequency and class imbalance make them difficult to model. Data also combine sources with differing resolutions and measurement characteristics.

Property 7: Noise, Incompleteness, and Uncertainty in Data

Geoscience datasets commonly contain noise, missing values, and changing measurement interpretations, while many variables and model outputs carry substantial uncertainty. These properties complicate consistent analysis and deployment.

3.3 Paucity of Samples and Ground Truth

Geoscience datasets often have few samples because observations cover limited historical periods, infrequent events, or sparse locations. Combined with many physical variables, this scarcity creates under-constrained learning problems.

Property 9: Paucity of Ground Truth

Geoscience applications often have abundant data but few labeled samples with gold-standard ground truth, making robust machine learning difficult. Methods must learn parsimonious models or exploit synthetic data when observations are scarce.

4 GEOSCIENCE PROBLEMS AND ML DIRECTIONS

The paper surveys geoscience problems where machine learning can support characterization, estimation, forecasting, relationship mining, and causal or policy analysis. It emphasizes methods that accommodate heterogeneous, non-stationary, sparsely labeled, physically constrained, and uncertain Earth-system data.

Characterizing Geoscience Objects and Events

  • Machine learning can characterize geoscience objects and events, including climate events, weather systems, and anomalous features.Spatio-temporal pattern mining has supported detection and cataloguing of mesoscale ocean eddies.Source: §4.1 · pp. 5–6

Estimating Geoscience Variables from Observations

  • Supervised learning can estimate difficult-to-monitor variables from satellite, ground-sensor, or Earth-system-model data.Multi-task learning addresses spatial and temporal heterogeneity by sharing models across similar tasks, helping regularize learning when some tasks have few samples.Source: §4.2 · p. 6
  • Adaptive online and ensemble methods address non-stationarity and poor data quality in climate estimation and surface-water mapping.A global monitoring system can detect shrinking lakes, melting glacial lakes, migrating river courses, and newly constructed dams and reservoirs.Source: §4.2 · p. 6
  • Small samples and scarce labels motivate sparsity-inducing, semi-supervised, and unsupervised methods for estimating geoscience variables.Source: §4.2 · p. 6

Long-term Forecasting of Geoscience Variables

  • Long-term forecasting requires uncertainty-aware approaches for high-dimensional, non-stationary geoscience processes, including transfer learning for future tasks with limited samples.Source: §4.3 · pp. 6–7

Mining Relationships in Geoscience Data

  • Graph representations and pattern mining can reveal spatial relationships and higher-order structures in climate data.Climate graphs have been used to study climate-system structure, hurricane activity, and communities in climate networks.Source: §4.4 · p. 7

Causal Discovery and Causal Attribution

  • Causal attribution and decision methods can connect Earth-system events to causes and support decisions under ambiguous risk.The paper identifies reinforcement learning and stochastic dynamic programming as promising approaches for decisions involving poorly resolved extreme-event risk.Source: §4.5 · p. 8

5 CROSS-CUTTING RESEARCH THEMES

Geoscience motivates cross-cutting machine learning research in deep learning and theory-guided data science. These directions address complex data and limited labels while integrating scientific knowledge into learning.

Deep Learning

  • Deep learning can automatically extract features from complex geoscience data, but scarce labeled samples limit traditional deep learning methods.Novel frameworks may use domain-specific information about physical processes to overcome label scarcity.Source: §5.1 · pp. 8–9

Theory-Guided Data Science

  • Theory-guided data science integrates scientific knowledge into data science methodologies because neither data-only nor physics-only approaches suffice for geoscience knowledge discovery.The paradigm explores a continuum between physics-based models and data science methods.Source: §5.2 · p. 9
  • Embedding scientific consistency in predictive-learning objectives can prune physically inconsistent models and help reduce variance without likely affecting bias.Anchoring learning frameworks in scientific knowledge may improve resistance to overfitting, especially when training data are limited.Source: §5.2 · p. 9
  • Theory-guided data science is being pursued across material science, hydrology, turbulence modeling, and biomedicine, with similar opportunities in geoscience.Geoscience applications can learn patterns from data while retaining knowledge encoded in physics-based process models.Source: §5.2 · p. 9
  • Scientific knowledge can complement geoscience efforts that incorporate data into physics-based models through calibration and data assimilation.Calibration learns approximation parameters from data, while data assimilation informs system-state transitions using observed variables.Source: §5.2 · p. 9

6 CONCLUSIONS

The article surveys machine learning challenges, problems, and promising directions in geosciences, while emphasizing collaboration between machine learning researchers and geoscientists. It presents future possibilities but states that the survey is not exhaustive.

6 CONCLUSIONS

  • The survey illustrates emerging possibilities for machine learning research in the Earth System, whose scientific importance affects life on and beyond Earth.The authors explicitly note that their survey of challenges, problems, and directions is not exhaustive.Source: §6 · p. 9
  • Successful geoscience machine learning is generally driven by geoscientific questions and close collaboration throughout research.Geoscientists contribute question, variable, dataset, data-collection, and preprocessing expertise, while ML researchers assess suitable methods and realistic capabilities.Source: §6 · p. 9
  • Interpretability is an important geoscience goal because understandable patterns, models, and relationships can contribute to scientific knowledge discovery.The conclusion links transparent reasoning to the use of learned results as building blocks for scientific knowledge.Source: §6 · p. 9

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