Machine Learning in Geosciences: Earthquakes, Ice, and XAI

Summary

• Focus: novel ML solutions to help scientists (more on physical and Earth sciences)

  • Explainable AI

  • Physic-informed learning

  • UQ

• Large-scale Earthquake detection

  • Seismic sensors have collected data over data

  • Developing new algorithms to discover small earthquakes from this data

  • P-wave: smaller wave at the start of the seismic event

  • S-wave: larger wave of the full shock

  • Challenges:

    • Heterogeneous background noise

    • Low signal to noise ratio

    • Data quality/missing data issues

    • Large data but few labels

  • Earthquake: regional shaking caused by slips along a fault (rather than trucks or explosions)

  • Traditional approach: Template matching of waveform patterns of known events

    • Correlate same pattern across many sites

    • Challenge: relies on prior knowledge of event shapes

    • Need a way to detect earthquakes without prior knowledge: find new waveforms

  • New approach: FAST

    • Scalable detection of new events, new signal shapes

    • Assumes limited waveform catalog

    • Uses domain knowledge: based on waveform similarity matching

    • https://github.com/stanford-futuredata/FAST

    • Compute similarity metric between waveforms

      • Cluster time periods with similar waveforms at similar times in correlated regions

      • Eliminated repeating signals that are not individual events (e.g. background car traffic)

      • Spectral representation of data using wavelets

      • Computationally efficient, few false detections

  • Key takeaways:

    • Scientific datasets have limitations and reflect current knowledge (e.g. instrument technology)

    • Methods in ML literature may not reflect real data/tasks

    • Opportunities for developing new methods

    • Scientific knowledge&physical models can improve the data analysis: bias model towards known physics (e.g., look for correlated patterns at different stations)

    • Interdisciplinary collaboration is critical

  • Machine learning for data-driven discovery in solid Earth geoscience (2019): https://www.science.org/doi/10.1126/science.aau0323

    • Lack of large high quality labeled datasets

    • Limited sharing of research codes and data

    • Difference in research cultures hinders collaboration (shared language, presentation venues)

    • Data analysis needs of geoscience

  • Scientific discovery in the age of artificial intelligence (2023): https://www.nature.com/articles/s41586-023-06221-2

    • Broader adoption, new uses of ML

    • Data collection/representation

    • Generating scientific hypothesis

    • ML-driven simulation and experimentation

    • ML toolbox is expanding

    • Open Science and data sharing

      • Seisbench: https://github.com/seisbench/seisbench

      • EQTransformer: https://github.com/smousavi05/EQTransformer

      • Stanford Earthquake dataset: https://github.com/smousavi05/STEAD

      • INSTANCE: https://essd.copernicus.org/articles/13/5509/2021/

    • New Conference for ML + Science

    • Challenges: 

      • Poor data quality, multimodal, multisource

      • Lack of theory for out of distribution, available constraints from domain knowledge

    • Trust/Uncertainty Quantification/Model Explainability/Insights

• Explainable AI for Scientific Data

  • Hard to gain insights from analyses of scientific data

  • Deep learning/data-driven models

    • May learn non-physical solutions

    • No uncertainty estimation

    • Black box - no explanation

  • SciAI center: interpretable ML architectures: https://sciaicenter.engineering.cornell.edu/

    • Instance-based explanations by design

    • Relate a new test observation to a set of prototypical examples

    • Prototypical examples are learned, part of neural network architecture

  • Approach:

    • Take a predictive neural network

    • Replace final fully connected layer with a prototype layer

    • Encoder or input data (e.g. image)

    • Compare input to encoded prototypes

    • Output softmax of similarity (like attention)

    • Prototypes as template waveforms

  • Prototype-based Joint Embedding Method (PB&J): 

    • Sample prototypes from the training data

    • Identifies which training instances resulted in conflicting predictions

    • Explicit representation of model confidence

    • Generates ensemble of predictions: estimate model confidence with explanations for ambiguous cases

• ML and the Cryosphere

  • A Variational LSTM Emulator of Sea Level Contribution From the Antarctic Ice Sheet (2023)

  • Question: How much will Antarctic or Greenland ice sheet contribute to sealevel rise

    • Typical approach: climate simulation

    • Can ML emulate these models (surrogates)?

    • Approach: Variational LSTM emulator of sea level contributions from Antarctica

      • Dataset: models in ISMIP6: https://climate-cryosphere.org/about-ismip6/

      • Emulators: smaller-scale approximations of fully detailed models

      • LSTM-based model outperforms Gaussian Process emulator

        • Ensemble and mean & distribution

        • Uncertainty via random dropout

        • Computational advantage allows richer set of forcings

  • How will changing climate affect navigability of Arctic ocean

    • New potential shipping routes

    • Most pass through narrow straits that are too small to resolve in climate models

    • Approach: ML-based downscaling

      • 100km -> 25km resolution

      • Superresolution GAN

      • ERA5 reanalysis data: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

      • Physics-constrained - ice volume conserved across resolutions

    • Result: outperforms standard (interpolation-based) techniques

    • Future: apply to CMIP6

  • New England community-driven Coastal Climate Research and Solutions (3CRS): https://www.3crs.org/

    • Working to downscale models to New England region