Stacking two-dimensional arrays for Analyzing Earthquake Data is a practical technique for combining multiple 2D seismic feature maps (e.g., magnitude grids, depth slices, or derived fields from different catalogs or processing steps) into higher-dimensional tensors — enabling joint analysis, multi-variate statistics, ML input preparation, or visualization. In Dask, da.stack() adds a new axis (e.g., time × lat × lon × feature), while da.concatenate() appends along existing dimensions (e.g., merging catalogs along time or spatial tiles). In 2026, this pattern is essential for USGS/IRIS catalogs, multi-model ensembles, real-time monitoring, and feature engineering — stacking magnitude/depth grids into (N_time, N_lat, N_lon, N_features) arrays for spatial-temporal analysis, 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 2D arrays for earthquake analysis in Dask: extracting HDF5 2D datasets as Dask arrays, stacking magnitude/depth/time slices, reshaping for ML or viz, real-world patterns (multi-catalog grids, tiled seismic maps), and modern best practices with chunk alignment, lazy evaluation, visualization, and xarray/Polars equivalents.
Extracting 2D earthquake features from HDF5 — prepare grids for stacking.
import h5py
import dask.array as da
with h5py.File('earthquakes_grids.h5', 'r') as f:
# Extract 2D grids (e.g., magnitude map at different times or models)
mag_time1 = da.from_array(f['magnitude_time1'], chunks='auto') # shape (lat, lon)
mag_time2 = da.from_array(f['magnitude_time2'], chunks='auto')
depth_slice = da.from_array(f['depth_slice'], chunks='auto')
print(mag_time1) # dask.array
Stacking 2D arrays — combine along new axis (time/feature) or existing (spatial concatenation).
# Stack multiple magnitude maps ? (N_time, lat, lon)
mag_stacked = da.stack([mag_time1, mag_time2, mag_time1 + 0.5], axis=0)
print(mag_stacked.shape) # (3, lat, lon)
# Stack magnitude & depth grids ? (lat, lon, 2) feature map
feature_grid = da.stack([mag_time1, depth_slice], axis=-1)
print(feature_grid.shape) # (lat, lon, 2)
# Concatenate along spatial axis (e.g., merge adjacent regions)
left_region = da.from_array(f['left_mag'], chunks='auto')
right_region = da.from_array(f['right_mag'], chunks='auto')
full_mag = da.concatenate([left_region, right_region], axis=1) # along longitude
print(full_mag.shape) # (lat, lon_left + lon_right)
Real-world pattern: stacking multi-time or multi-model earthquake grids for analysis/ML.
# Multiple time slices of magnitude grids
mag_slices = [da.from_array(f[f'mag_t{i:03d}'], chunks='auto') for i in range(100)]
# Stack along time axis ? (time, lat, lon)
mag_cube = da.stack(mag_slices, axis=0)
print(mag_cube.shape) # (100, lat, lon)
# Compute mean magnitude map over time
mean_mag_map = mag_cube.mean(axis=0).compute()
print(mean_mag_map.shape) # (lat, lon)
# Visualize mean map
import matplotlib.pyplot as plt
plt.imshow(mean_mag_map, cmap='viridis')
plt.colorbar(label='Mean Magnitude')
plt.title('Mean Magnitude Grid Over Time')
plt.show()
Best practices for stacking 2D earthquake arrays in Dask. Use da.stack(..., axis=0) — for adding time/feature dimension (common in catalogs). Modern tip: prefer xarray — xr.concat([da1, da2], dim='time') or xr.merge() — labeled stacking avoids manual axis errors. Ensure chunk compatibility — rechunk before stacking (.rechunk(...)) along joined axis. Visualize graph — stacked.visualize() to debug dependencies. Persist large stacks — mag_cube.persist() for repeated use. Use distributed scheduler — Client() for parallel stacking. Add type hints — def stack_grids(arrs: list[da.Array[np.float64, (None, None)]]) -> da.Array[np.float64, (None, None, None)]. Monitor dashboard — track chunk merging/memory. Avoid mismatched shapes — use padding or filtering. Use da.block() — for complex tiled layouts (e.g., geospatial mosaics). Test small stacks — stacked[:100, :100].compute(). Use np.dstack() — for depth-wise (channels) stacking in NumPy prototyping. Use da.rechunk() — align chunks along stacked axis. Profile with timeit — compare stack vs manual concatenation.
Stacking two-dimensional arrays for earthquake analysis combines magnitude/depth grids or time slices — use da.stack for new axes, da.concatenate for merging regions, xarray for labeled stacking. In 2026, align chunks, visualize graphs, persist intermediates, use xarray for labels, and monitor dashboard. Master stacking 2D arrays, and you’ll build unified seismic grids efficiently and correctly for any downstream task.
Next time you need to combine 2D earthquake maps — stack them properly. It’s Python’s cleanest way to say: “Merge these seismic grids — along the right axis with correct order.”