earthquake_tidal_seasonal_loading

Python code to calculate solid-earth tidal stresses and water loading stresses on faults, and analyze their correlation with seismicity.

Requirements

• Python 3.7+
• Required packages: numpy, pandas, matplotlib, scipy, xarray
• SPOTL (Solid-Earth Tide Program): https://igppweb.ucsd.edu/~agnew/Spotl/spotlmain.html
• GRACE water loading data (https://podaac.github.io/tutorials/quarto_text/DataSubscriberDownloader.html and then downloaded with this: podaac-data-downloader -c TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4 -d ./data --start-date 2000-01-01T00:00:00Z --end-date 2022-01-01T00:00:00Z -b="-95,29,-87,37")

Install Python dependencies:

pip install numpy pandas matplotlib scipy xarray

Workflow

1. Preprocess Earthquake Catalog

Convert earthquake catalog to standardized format with required columns: time, longitude, latitude, magnitude, depth.

python preprocess_catalog.py input_catalog.csv output_processed.csv

Options:

• --min-mag: Minimum magnitude filter (default: 0.0)
• --max-depth: Maximum depth in km (default: 20.0)
• --plot: Create a quick plot of the catalog
• --interactive: Use Matplotlib interactive selector for spatial filtering
• --interactive-web: Use browser-based interactive selector

Example:

python preprocess_catalog.py nm_usgs.csv nm_usgs_processed.csv --min-mag 1.0 --max-depth 15.0

2. Calculate Tidal Strain

Use SPOTL to calculate tidal strain at earthquake locations. You need to provide:

• Processed earthquake catalog
• Date range for strain calculation
• Path to SPOTL executable (typically ertid)

python spotl_helper.py --catalog nm_usgs_processed.csv \
    --start-date 2000-01-01 \
    --end-date 2022-01-01 \
    --spotl-executable /path/to/spotl/bin/ertid \
    --output strain_output.csv

The script automatically:

• Determines the centroid location from the catalog
• Generates SPOTL input files
• Runs the strain calculation
• Converts output to CSV format with columns: time, strain_N_nanostrain, strain_E_nanostrain, strain_NE_nanostrain

3. Tidal Sensitivity Analysis

Analyze tidal stress modulation of seismicity, optionally including GRACE water loading.

python tidal_seasonal.py \
    --catalog nm_usgs_processed.csv \
    --spotl-output strain_output.csv \
    --projection fault_plane \
    --strike 173 \
    --dip 73 \
    --rake 90 \
    --grace-file GRCTellus.JPL.200204_202506.GLO.RL06.3M.MSCNv04.nc \
    --grace-lon-min -92 --grace-lon-max -88 \
    --grace-lat-min 34 --grace-lat-max 38 \
    --output-prefix results \
    --n-bootstrap 1000 \
    --output-alpha-timeseries alpha_timeseries.csv

Parameters:

• --projection: fault_plane (requires strike/dip/rake) or optimally_oriented
• --strike, --dip, --rake: Fault orientation in degrees
• --grace-file: Optional GRACE NetCDF file for water loading analysis
• --grace-lon-min/max, --grace-lat-min/max: Region for GRACE data averaging
• --n-bootstrap: Number of bootstrap iterations for significance testing
• --output-alpha-timeseries: Save time-varying phase modulation parameter
• --decluster: Apply Gardner-Knopoff declustering to remove aftershocks/foreshocks

Outputs:

• Phase-amplitude plots
• Sliding window analysis
• Bootstrap significance tests
• Alpha (phase modulation) vs time plots

4. Coherence Analysis (Optional)

Analyze coherence between tidal stress, GRACE water loading stress, and seismicity.

python coherence.py \
    --catalog nm_usgs_processed.csv \
    --spotl-output strain_output.csv \
    --grace-file GRCTellus.JPL.200204_202506.GLO.RL06.3M.MSCNv04.nc \
    --projection fault_plane \
    --strike 173 --dip 73 --rake 90 \
    --output-prefix coherence_results

Outputs:

• Time series plots of tidal and GRACE stresses
• Sliding window seismicity counts
• Coherence spectra
• Cross-correlation analysis

Additional Scripts

seasonal.py

An alternative implementation for seasonal analysis with built-in declustering. This script provides:

• Class-based earthquake catalog management
• Optimized Gardner-Knopoff declustering algorithm (source of the declustering function used in tidal_seasonal.py)
• Seasonal analysis with jackknife resampling
• Magnitude residual calculations
• Various plotting utilities

The declustering implementation in seasonal.py uses the standard Gardner & Knopoff (1974) formulas:

• Time window: T = 10^(0.032*M + 2.7389) seconds
• Distance window: D = 10^(0.1238*M + 0.983) km

This is the reference implementation that has been adapted for use in tidal_seasonal.py.

Data Files

Example data files included:

• nm_usgs.csv, nm_usgs_longer.csv: New Madrid earthquake catalogs
• GRCTellus.JPL.200204_202506.GLO.RL06.3M.MSCNv04.nc: GRACE water loading data

Adapting to Other Datasets

The scripts are designed to be generalizable:

1. Earthquake Catalog: Any CSV with columns for time, location (lon/lat), magnitude, and depth
2. SPOTL Calculation: Works for any geographic location; the script auto-detects catalog centroid
3. Fault Parameters: Specify strike/dip/rake for your fault of interest, or use optimally_oriented mode
4. GRACE Data: Adjust --grace-lon/lat-min/max to match your study region
5. Declustering: Use --decluster flag to apply Gardner-Knopoff algorithm if your catalog includes aftershocks

Citations

This code reproduces Beauce et al. (2023) and it applies it to a new region. Citing is appropriate if you use the code or results/interpretations in the following papers.

Method:

Beaucé, E., Poli, P., Waldhauser, F., Holtzman, B., and Scholz, C. (2023). Enhanced tidal sensitivity of seismicity before the 2019 magnitude 7.1 Ridgecrest, California earthquake. Geophysical Research Letters, 50, e2023GL104375, https://doi.org/10.1029/2023GL104375

This code:

Walter, J. I., H. DeShon, and P. Neupane (in review), Solid earth tides modulate earthquake activity in the New Madrid Seismic Zone, Seismological Research 
Letters

Notes

• All times should be in UTC
• Depths should be in kilometers
• Longitudes can be -180 to 180 or 0 to 360 (automatically handled)
• GRACE data download instructions (not included): https://podaac.github.io/tutorials/
• SPOTL executable must be compiled separately from source