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