---
title: 'Poseidon Dataset: Multi-Domain Scientific Data'
url: https://www.emergentmind.com/topics/poseidon-dataset
type: topic
---

# Poseidon Dataset: Multi-Domain Scientific Data

The term "Poseidon Dataset" designates several major open-source scientific datasets across varied domains, most notably large-scale global earthquake catalogs for physics-informed seismology [2601.02264], simulation-based operator learning corpora for partial differential equations (PDEs) [2405.19101], and compositional aerosol optical property libraries for exoplanet atmospheric retrievals [2410.19253]. Each dataset is architected for rigorous, data-driven modeling, frequently integrated with domain-specific physical constraints or equations to maximize scientific utility and reproducibility.

## 1. Definitions and Scope

The Poseidon Dataset for seismology comprises the largest openly accessible earthquake catalog for machine learning and physics-informed analyses, aggregating 2,833,766 seismic events over 30 years (1990–2019) [2601.02264]. In computational physics and machine learning, the Poseidon Dataset refers to a collection of simulation-generated trajectories for operator learning, featuring millions of solutions for diverse PDEs, facilitating generalization across PDE classes and heterogeneous physical regimes [2405.19101]. In planetary science, the Poseidon aerosol database provides comprehensive Mie-scattering optical properties for ∼100 condensate species from laboratory measurements, enabling detailed atmospheric retrieval modeling for exoplanet spectra [2410.19253].

## 2. Structure and Contents

### a. Seismology: Global Earthquake Catalog

- **Event Coverage:** 2.8 million events, full latitude (−90° to +90°) and longitude (−180° to +180°), magnitude continuum M0.0–M9.1.
- **Temporal Resolution:** Uniform sampling, ISO 8601 timestamps.
- **Attributes (per event):** 30 fields, including core identifiers (event_id, time, location, depth, magnitude), metadata (event_type, tsunami_flag, review_status), and quantitative observation metrics (nsta, az_gap, rms, error estimates, significance).
- **Energy Features:** Pre-computed columns E and log10_E via Gutenberg–Richter scaling, $E = 10^{1.5M + 4.8}$ [Joule], physically linearizing the magnitude space for direct integration into modeling.
- **Spatial Indices:** Discretized onto 180×360 grid (1-degree resolution).
- **Format:** Apache Parquet; sub-tables for energy features and grid indices, directly loadable via Pandas/pyarrow or HuggingFace Datasets API.

### b. Physics-Informed Operator Learning for PDEs

- **Data Splits:**
  - *Pretraining Suite:* 6 fluid-dynamics solution operators (Euler and Navier–Stokes), $D=[0,1]^2$, $T=[0,1]$.
  - *Downstream Benchmarks:* 15 heterogeneous PDE tasks—fluid, wave, reaction-diffusion, elliptic, aerodynamics.
- **Size:** ~29,280 pretraining trajectories, 11–21 snapshots per trajectory, effective training pairs expanded by semigroup all-2-all augmentation to >5.1 million.
- **Governing Equations:** Full spectrum of PDEs—Navier–Stokes, Euler, Allen–Cahn, wave, Poisson, Helmholtz, airfoil flow.
- **Discretizations:** Spatial grids 128×128 (with select tasks up/downsampled), periodic/Dirichlet/freestream boundary conditions, time steps $\Delta t=0.1$ (pretrain), $\Delta t=0.05$–$0.1$ (downstream).
- **File Layout:** HDF5 or NumPy (.npy/.npz), tensors $(N_{\text{samples}},N_{\text{channels}},T+1,128,128)$ (time-dependent), with metadata for parameter labels and coordinate grids.

### c. Exoplanet Aerosol Mie-Scattering Library

- **Species:** ~100 types, seven categories (super-hot condensates, M–L/T–Y dwarf clouds, Fe/Mg/Si/oxide phases, ices, soots, hazes).
- **Optical Properties:** ASCII refractive-index files $(\lambda, n, k)$, precomputed Mie databases (HDF5), arrays for wavelength, radius, $\sigma_{\text{ext,eff}}$, single-scattering albedo $\omega$, asymmetry $g$.
- **Radius/Wavelength Grids:** $a = 10^{-3}$–$10\,\mu$m (log-linear, 1000 points), $\lambda = 0.2$–$30\,\mu$m ($R=1000$, ~29,000 points).
- **Directory Tree:** reference_data/aerosols/{database_index.json, species.ri, species.miesim.h5}; JSON metadata for coverage.
- **Integration:** Retrieval codes interpolate these properties in log–log space for stability; support slab, fuzzy-deck, hybrid cloud parameterizations.

## 3. Data Generation, Augmentation, and Quality Metrics

- **Seismic Catalog:** No event filtering in public release; users commonly apply thresholds (nsta ≥ 4, az_gap ≤ 180°, rms ≤ 1.0 s, err_depth ≤ 10 km) to refine catalogs for high-precision analysis. Completeness varies by region and epoch—the southern hemisphere <M3.5 and locations with sparse network coverage are less complete.
- **PDE Solution Dataset:** Data synthesized from high-fidelity solvers (spectral, finite volume, finite difference/element). Initial conditions randomized (Fourier, Gaussians, Riemann problems, Brownian bridges). Semigroup augmentation exploits time-order pairs $(u(t_i),u(t_j))$, upscaling $KM$ sample trajectories to $KM^2$ input-output pairs.
- **Aerosol Database:** Laboratory-indexed refractive-index sources, meticulous coverage of temperature, polymorph, wavelength bounds. Each species metadata includes measurement provenance for reproducibility.

## 4. Canonical Use Cases and Modeling Protocols

- **Seismology:** Designed for aftershock sequence identification, tsunami-potential screening, and foreshock pattern detection. Standard workflow involves event quality filtering, energy feature normalization, grid-based aggregation for convolutional architectures, and weighted sampling or focal loss to address tsunami-event imbalance.
- **PDE Operator Learning:** Enables sample-efficient learning and generalization. Users preprocess via normalization to $[-1,1]$, mask unused channels, and embed parameters/coordinates for meta-learning tasks. Downstream evaluations encompass a taxonomically broad PDE suite (parabolic, hyperbolic, elliptic, steady/unsteady, non/linear).
- **Exoplanet Retrievals:** Aerosol models calibrated to physical cloud structures, matching spectral signatures in transmission/emission/reflection. Best practices include verifying refractive-index coverage, appropriate cloud parameterization (slab vs. deck), interpolation in log–log space, and explicit treatment of thermal plus starlight multiple scattering.

## 5. Known Limitations and Extension Strategies

- **Seismology:** Potential biases from early network gaps, completeness variation, and depth uncertainty (offshore events). Users should set magnitude-of-completeness thresholds (e.g., $M_c=2.5$ globally) for accurate magnitude-frequency analysis. For further refinement, cross-matching with ISC/USGS for missing arrivals and stricter event filtering for anthropogenic sources (quarry, explosions) are recommended. Augmentation with local stress-transfer or Coulomb-failure metrics is possible for higher-order physics constraints.
- **PDE Datasets:** Downsampling/interpolation may affect fine-scale dynamics, especially when transferring between mesh resolutions. Choice of normalization and channel masking impacts multi-task/transfer learning.
- **Aerosol Library:** Wavelength and particle-size grid bounds must be carefully respected; extrapolation risks modeling artifacts. Uncertainty in laboratory refractive-index data may propagate to retrievals; transparent provenance mitigates interpretational errors.

## 6. Public Access, Data Format, and Community Adoption

All Poseidon datasets are released for unrestricted scientific use. The seismology catalog is hosted at https://huggingface.co/datasets/BorisKriuk/Poseidon, supporting Parquet and full Python ecosystem compatibility [2601.02264]. The PDE operator learning suite and downstream tasks are available as part of the PDEgym benchmark collection at https://huggingface.co/camlab-ethz and via GitHub [2405.19101]. The aerosol database is distributed with self-describing files and documented metadata for fast access and integration [2410.19253].

This widespread accessibility, rigorous metadata documentation, and adherence to domain-specific physical principles have established the Poseidon datasets as reference standards for physics-informed machine learning in seismology, computational physics, and atmospheric sciences.

Source: https://www.emergentmind.com/topics/poseidon-dataset