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Machine Learning for the Geosciences: Challenges and Opportunities
Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, Vipin Kumar
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.
Method
The article surveys geoscience data sources, machine-learning challenges, application categories, methodological directions, and cross-cutting research themes.
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.
Takeaways & Limitations
Successful geoscience machine-learning applications are generally driven by geoscience questions and close collaboration throughout research.
Takeaways & Limitations
The survey is not exhaustive, and traditional deep-learning methods are limited by the paucity of labeled geoscience data.
Abstract
from arXiv · showhide
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.
- Improved sensing, large-scale Earth-system simulations, and internet-based data access have shifted geosciences from data-poor to data-rich.
- Publicly available geoscience big data offers machine learning opportunities for problems of substantial societal relevance.
- Physical laws, amorphous boundaries, and complex latent variables create challenges that differ from standard commercial data-science problems.
- Close collaboration between machine-learning researchers and geoscientists can advance both disciplines through cross-fertilization of ideas.
- The article surveys geoscience data, machine-learning challenges, application areas, cross-cutting research themes, and collaboration practices.
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.
- Geoscience data are obtained primarily from observational sensors and simulations generated by physics-based Earth-system models.
- Earth observations include satellite measurements of surface and atmospheric variables collected by public agencies and private organizations.
- In-situ sensors provide direct measurements from ground, atmospheric, and ocean platforms, while proxy records extend some observations thousands of years into the past.
- Dataset representation varies by source, including geo-registered images, time series, and irregularly sampled measurements, so data properties should guide analytics choices.
- Physical laws govern relationships among Earth-system variables, but exact solutions are often difficult for complex real-world systems.
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.
- Waves, flows, and coherent structures can deform dynamically and appear across multiple scales in continuous spatio-temporal fields.
- These properties make geoscience patterns substantially more complex than patterns in discrete spaces commonly handled by machine-learning algorithms.
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.
- Geoscience observations are generally autocorrelated across space and time at appropriate resolutions.
- Climate variables can exhibit long-range spatial dependencies called teleconnections and long-memory temporal effects.
- Spatio-temporal autocorrelation violates the independent-and-identically-distributed assumption underlying many machine-learning methods.
- Effective geophysical modeling therefore requires accounting for structural relationships across space and time.
- Geoscience analyses may require millions of dimensions because many variables must be considered simultaneously at fine spatial and temporal resolutions.
- Even 2.5o surface-resolution data can contain more than 10,000 spatial grid points, with additional dimensions from time, depth, and atmospheric or mantle layers.
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.
- Spatial differences in geography, vegetation, rock formations, and climate make geoscience variables vary significantly between locations.
- Seasonal, decadal, geological, and climate-change cycles make Earth-system processes nonstationary over time.
- This space-time heterogeneity makes it difficult to learn a joint distribution across all locations and time steps.
- Models trained across all regions and time steps may perform poorly, motivating local or regional models for homogeneous observation groups.
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.
- Rare events such as cyclones, flash floods, and heat waves can cause major losses, making monitoring important for adaptation and mitigation.
- Class imbalance leaves too few samples from rare classes, hindering their modeling and characterization.
- Geoscience datasets come from satellites, in-situ measurements, and simulations with varying spatial and temporal resolutions.
- These sources can differ in sampling rate, accuracy, and uncertainty, while in-situ sensors may be irregularly spaced.
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.
- Sensor malfunctions, severe weather, and equipment changes can create missing data or alter the interpretation of measurements over time.
- Some variables must be inferred indirectly, such as methane plumes detected from sunlight absorption and interpreted using transport-related assumptions or measurements.
- Model outputs also contain uncertainty because initial and boundary conditions are imperfectly known and model parameterizations are approximate.
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.
- Most satellite products begin in the 1970s, yielding fewer than 600 monthly or 50 yearly samples for corresponding processes.
- Important geoscience events may occur very infrequently, further limiting available observations.
- Paleoclimate proxies and early precipitation records are available only from a small number of locations.
- Unlike Internet-scale commercial datasets, geoscience applications combine limited samples with many physical variables, producing under-constrained 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.
- Many geoscience applications have large datasets but few labeled samples with gold-standard ground truth.Ground truth may require expensive airborne measurements or time-consuming field surveys, and some processes lack exact ground truth entirely.
- Limited representative training samples can cause either underfitting or overfitting, depending on model complexity relative to feature dimensionality.
- Parsimonious machine learning models are needed when labeled data are scarce.
- Synthetic datasets generated through simulation or perturbation can supplement the few available observations for training.
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.
- 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.
- Estimating Geoscience Variables from Observations: 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.
- Estimating Geoscience Variables from Observations: Small samples and scarce labels motivate sparsity-inducing, semi-supervised, and unsupervised methods for estimating geoscience variables.
- 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.
- 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.
- 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.
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.
- 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.
- Theory-Guided Data Science: 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.
- Theory-Guided Data Science: 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.
- Theory-Guided Data Science: 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.
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.
- 6 CONCLUSIONS: 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.
- 6 CONCLUSIONS: 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.