Stacking Arrays for Analyzing Earthquake Data with Dask in Python 2026

Stacking Arrays for Analyzing Earthquake Data with Dask in Python 2026

When analyzing earthquake data, you often need to stack multiple arrays (e.g., waveforms from different events or stations) into a higher-dimensional structure. Dask makes this operation efficient and scalable even for very large seismic datasets.

1. Stacking Waveforms from Multiple Events

import dask.array as da
import h5py

# Example: Stack waveforms from multiple earthquake events
events = []

with h5py.File("earthquake_data.h5", "r") as f:
    for event_id in range(1000):  # 1000 events
        dataset = f[f"/events/event_{event_id}/waveform"]
        arr = da.from_array(dataset, chunks=(500, 10000))
        events.append(arr)

# Stack along a new axis: (events, time, stations)
stacked_waveforms = da.stack(events, axis=0)

print("Stacked shape:", stacked_waveforms.shape)
print("Chunks:", stacked_waveforms.chunks)

2. Practical Analysis After Stacking

# Compute maximum amplitude per event
max_amplitudes = stacked_waveforms.max(axis=(1, 2)).compute()

# Mean waveform per event
mean_waveforms = stacked_waveforms.mean(axis=2).compute()

# Rolling statistics along time
from dask.array import map_overlap

rolling_max = map_overlap(
    lambda x: x.max(axis=1),
    stacked_waveforms,
    depth=(0, 100, 0),
    boundary='reflect'
)

3. Best Practices for Stacking Arrays in Earthquake Analysis (2026)

• Stack along a new leading axis (events) to keep time and station dimensions intact
• Choose chunk sizes carefully — typically chunk along time and stations, not events
• Use da.stack() when creating a new dimension
• Rechunk after stacking if the resulting chunks are unbalanced
• Persist the stacked array if you plan to perform multiple analyses on it

Conclusion

Stacking arrays is a fundamental operation when analyzing earthquake data with Dask. By stacking waveforms from multiple events into a higher-dimensional Dask Array, you can perform parallel computations across events, time, and stations efficiently. In 2026, this pattern combined with proper chunking and map_overlap() for rolling statistics is a standard approach for large-scale seismic analysis.

Next steps:

• Try stacking waveforms or features from multiple earthquake events in your dataset