Stacking One-Dimensional Arrays for Analyzing Earthquake Data with Dask in Python 2026

Stacking One-Dimensional Arrays for Analyzing Earthquake Data with Dask in Python 2026

One-dimensional arrays are frequently used in earthquake analysis for time series data such as seismic waveforms, amplitude envelopes, or feature vectors from individual stations or events. Stacking multiple 1D arrays into a higher-dimensional Dask Array allows efficient parallel processing across many events or stations.

1. Stacking 1D Waveforms from Multiple Events

import dask.array as da
import h5py

# Collect 1D waveform arrays from multiple events
waveforms = []

with h5py.File("earthquake_data.h5", "r") as f:
    for i in range(500):                     # 500 earthquake events
        # Each event has a 1D waveform array
        waveform = f[f"/events/event_{i}/waveform"][:]
        waveforms.append(waveform)

# Stack 1D arrays into a 2D Dask Array: (events, time_samples)
stacked = da.stack(waveforms, axis=0)

print("Stacked shape:", stacked.shape)       # (500, time_samples)
print("Chunks:", stacked.chunks)

2. Practical Analysis After Stacking

# Compute maximum amplitude per event
max_amplitudes = stacked.max(axis=1).compute()

# Mean amplitude per event
mean_amplitudes = stacked.mean(axis=1).compute()

# Standard deviation per event
std_amplitudes = stacked.std(axis=1).compute()

# Example: Find events with highest energy
top_events = max_amplitudes.argsort()[-10:]   # indices of 10 strongest events
print("Strongest events indices:", top_events)

3. Best Practices for Stacking 1D Arrays in Earthquake Analysis (2026)

• Stack along a new axis (usually axis=0) to create (events, time) or (stations, time) structure
• Choose chunk size along the time dimension based on waveform length and available memory
• Use da.stack() for creating a new dimension from 1D arrays
• Rechunk after stacking if the resulting chunks are unbalanced
• Persist the stacked array if you will perform multiple analyses on it

Conclusion

Stacking one-dimensional arrays is a common and powerful technique when analyzing earthquake data with Dask. By stacking waveforms or feature vectors from many events or stations, you can perform parallel computations across the entire dataset efficiently. In 2026, this pattern combined with proper chunking is a standard workflow for seismic data analysis.

Next steps:

• Try stacking 1D waveforms or extracted features from multiple earthquake events in your dataset