Stacking arrays for Analyzing Earthquake Data

Stacking arrays for Analyzing Earthquake Data is a powerful technique for combining multi-dimensional seismic features (latitude, longitude, depth, magnitude, time) into unified arrays — enabling joint analysis, feature engineering for ML models, spatial/temporal visualizations, or batch processing in Dask. Stacking along new axes (da.stack()) creates higher-dimensional tensors (e.g., events × features), while concatenation (da.concatenate()) appends along existing dimensions (e.g., combining catalogs). In 2026, this is essential for USGS/IRIS catalogs, real-time monitoring, and research — stacking location + magnitude + depth into (N_events, 3) or (N_events, 4) arrays for clustering, anomaly detection, or geospatial plotting, with Dask handling large/out-of-core data lazily and in parallel.

Here’s a complete, practical guide to stacking arrays for earthquake analysis in Dask: extracting HDF5 datasets as Dask arrays, stacking location/magnitude/depth, reshaping for ML or viz, real-world patterns (event tensors, multi-catalog merge), and modern best practices with chunk alignment, lazy evaluation, visualization, and xarray/Polars equivalents.

Extracting & stacking earthquake features from HDF5 — build event tensors lazily.


import h5py
import dask.array as da

with h5py.File('earthquakes.h5', 'r') as f:
    # Extract core features as Dask arrays
    lat = da.from_array(f['latitude'], chunks='auto')
    lon = da.from_array(f['longitude'], chunks='auto')
    depth = da.from_array(f['depth'], chunks='auto')
    mag = da.from_array(f['magnitude'], chunks='auto')
    time = da.from_array(f['time'], chunks='auto')  # datetime64[ns]

# Stack location coordinates (lat, lon, depth) ? (N_events, 3)
locations = da.stack([lat, lon, depth], axis=-1)  # axis=-1 adds feature dim
print(locations)  # dask.array

# Stack all features (lat, lon, depth, mag) ? (N_events, 4)
event_features = da.stack([lat, lon, depth, mag], axis=-1)
print(event_features.shape)  # (N_events, 4)

Stacking for ML-ready tensors — events × features (or time × events × features).


# Example: add time as first feature (time, lat, lon, depth, mag)
event_tensor = da.stack([time.astype(float), lat, lon, depth, mag], axis=-1)
print(event_tensor.shape)  # (N_events, 5)

# For batched ML (batch × time_steps × features)
# Assume pre-windowed data (e.g., sliding windows)
batched = event_tensor.reshape(-1, window_size, 5)  # if pre-processed
print(batched.shape)  # (n_batches, window_size, 5)

Real-world pattern: stacking multi-catalog or multi-feature earthquake data for analysis/viz.


# Multiple catalogs (e.g., different magnitude thresholds)
cat_high = da.from_array(f['magnitude_high'], chunks='auto')
cat_medium = da.from_array(f['magnitude_medium'], chunks='auto')

# Concatenate along event axis (combine catalogs)
all_events = da.concatenate([cat_high, cat_medium], axis=0)

# Stack with locations (assume same order)
all_locations = da.stack([lat_all, lon_all, depth_all, all_events], axis=-1)
print(all_locations.shape)  # (N_total, 4)

# Visualize stacked magnitudes (histogram)
plt.hist(all_events.compute(), bins=50, edgecolor='black')
plt.title('Combined Magnitude Distribution')
plt.xlabel('Magnitude')
plt.ylabel('Count')
plt.show()

Best practices for stacking earthquake arrays in Dask. Use da.stack(..., axis=-1) — for adding feature dimension (common in ML). Modern tip: prefer xarray — xr.concat([da1, da2], dim='event') or xr.merge() — labeled stacking avoids manual axis errors. Ensure chunk compatibility — rechunk arrays to match before stacking (.rechunk(...)). Visualize graph — stacked.visualize() to debug dependencies. Persist large stacks — event_features.persist() for repeated use. Use distributed scheduler — Client() for parallel stacking. Add type hints — def stack_eq(arrs: list[da.Array[np.float64, (None,)]]) -> da.Array[np.float64, (None, None)]. Monitor dashboard — track chunk merging/memory. Avoid mismatched shapes — use da.concatenate only when dimensions match. Use da.block() — for complex tiled layouts (rare in event data). Test small stacks — stacked[:1000].compute(). Use np.column_stack — for NumPy prototyping. Use da.rechunk() — align chunks along stacked axis. Profile with timeit — compare stack vs manual concatenation.

Stacking arrays for earthquake analysis combines location, magnitude, depth, and time features — use da.stack for new axes, da.concatenate for merging catalogs, xarray for labeled stacking. In 2026, align chunks, visualize graphs, persist intermediates, use xarray for labels, and monitor dashboard. Master stacking, and you’ll build unified seismic tensors efficiently and correctly for any downstream task.

Next time you need to combine earthquake features — stack them properly. It’s Python’s cleanest way to say: “Merge these seismic dimensions — into one powerful array.”

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