Basel-Riehen Ambient Noise Tomography

Processing codes and data for ambient noise tomography in the Basel-Riehen region, Switzerland.

Overview

This repository contains the processing pipeline and results for constructing a 3D shear-wave velocity (Vs) model of the shallow subsurface in the Basel-Riehen area using ambient seismic noise recorded by a dense nodal array. The workflow follows the approach of Planès et al. (2020) for the Geneva Basin.

Repository Structure

basel-nant/
├── data/                    Processed data products
│   ├── stations/            Station coordinates and metadata
│   ├── ccf/                 Cross-correlation functions
│   ├── dispersion-curves/   Rayleigh wave dispersion picks
│   ├── vg-maps/             Group velocity maps
│   └── vs-model/            3D shear wave velocity model
│
├── scripts/                 Processing codes
│   ├── ccf-processing/      Cross-correlation (Python/NoisePy)
│   ├── vg-map-inversion/    2D group velocity tomography (MATLAB)
│   ├── vs-depth-inversion/  1D Vs depth inversion (MATLAB)
│   └── fracture-Vs-modeling/ Forward modeling (MATLAB/Python)
│
├── LICENSE                  MIT License
└── README.md                This file

Processing Workflow

Raw seismic data (miniSEED)
         │
         ▼
[1] Cross-correlation ────────► Stacked CCFs
    (ccf-processing/)             │
                                  ▼
[2] Dispersion picking ───────► Rayleigh wave group velocities
    (ccf-processing/)             │
                                  ▼
[3] 2D tomography ────────────► Group velocity maps at each period
    (vg-map-inversion/)           │
                                  ▼
[4] Depth inversion ──────────► 3D Vs(x,y,z) model
    (vs-depth-inversion/)

Methods

• Cross-correlation: NoisePy-based processing with 3-component normalization (Jiang & Denolle, 2020)
• 2D tomography: Iterative least-squares inversion (Tarantola & Valette, 1982)
• Depth inversion: Neighborhood Algorithm with Voronoi cell resampling (Sambridge, 1999)

Data Summary

Component	Count	Coverage
Stations	230	RI nodal array + CH permanent
Station pairs	25,651	All combinations
Period range	0.2–5.0 s	49 periods
Grid	107 × 102	~100 m spacing
Depth range	0–5000 m	10 m intervals

Requirements

Python

• numpy, scipy, pandas, matplotlib
• obspy, pyasdf
• mpi4py, numba
• pycwt, findpeaks

MATLAB

• Signal Processing Toolbox
• Parallel Computing Toolbox
• Geopsy/gpdc (external)

Quick Start

See detailed instructions in each subdirectory:

• scripts/README.md — Processing pipeline overview
• data/README.md — Data products and formats

Authors and Acknowledgments

Workflow assembly and modifications: Genevieve Savard (2021–2026)

Original code contributors:

• Cross-correlation processing based on NoisePy by Chengxin Jiang and Marine Denolle (Harvard/UW)
• MATLAB inversion codes by Thomas Planès (2019)

References

Jiang, C. and Denolle, M. (2020). NoisePy: A new high-performance python tool for ambient noise seismology. Seismological Research Letters, 91(3), 1853-1866. https://doi.org/10.1785/0220190364

Planès, T., Obermann, A., Antunes, V., and Lupi, M. (2020). Ambient-noise tomography of the Greater Geneva Basin in a geothermal exploration context. Geophysical Journal International, 220(1), 370-383. https://doi.org/10.1093/gji/ggz458

Sambridge, M. (1999). Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space. Geophysical Journal International, 138(2), 479-494. https://doi.org/10.1046/j.1365-246X.1999.00876.x

Tarantola, A. and Valette, B. (1982). Generalized nonlinear inverse problems solved using the least squares criterion. Reviews of Geophysics, 20(2), 219-232.

License

MIT License — see LICENSE for details.