Distinct Results


  • Planetary Seismology: Mars and Earth's Moon

  • Seismic Ambient Noise: Generation and Characterization

  • Seismic Noise Interferometry: Extraction of Empirical Green's Functions

  • Ambient Noise Tomography

  • Seismic Tomography using Earthquake Body- and Surface Waves

  • Environmental Seismology

  • Mines

  • Monitoring Structural Variability with Seismic Noise

  • Mapping Earth's Discontinuities Using Earthquake and Noise Recordings

  • Adaptive Filter Design, Noise Attenuation, Signal Processing: Theory and Applications

  • Bio-Medical Rhythm Detection: Theory and Applications and Climate Reconstruction

  • Talks (videos on Youtube etc)

  • Software Packages and Selected Models

    Software Packages (links provided for code and examples):

      All scripts and programs are distributed in the hope that they will be useful, but without any warranties. Use them at your own risk for your research purposes. Please contact me if you need assistance or further information.

    • Ambient Noise Processing Tools Corr_stack_v04.3.tar: Includes Phase Cross-Correlation and time–frequency Phase-Weighted Stack as described in Schimmel et al. (2011a). Corr_stack_v04.3.tar is the latest release and contains source code, documentation, and three example datasets. The programs use SAC data and run on Linux systems. Corr_stack_v04.1.tar is equivalent to Corr_stack_v03.6.tar but adapted for local parallelization using OpenMP (Open Multi-Processing), which can be enabled during compilation. Version 04.x is significantly faster and will be the focus of ongoing development. To use, simply download the tar file and extract it with: tar xvf Corr_stack_v0x.x.tar
      (Previous versions: Corr_stack_v03.6.tar, Corr_stack_v03.5.tar, Corr_stack_v04.2.tar). DOI: 10.20350/digitalCSIC/13836)

    • Seismic Data Segmentation datsegment1C: This program segments seismic data (e.g., for ambient noise processing) based on RMS amplitude variability, as described in Schimmel et al. (2021). The tar file includes source code, a brief description, and a simple example (Fig. 1, Schimmel et al., 2021). The program uses SAC data and runs on Linux systems. To use, download the tar file and extract it with "tar xvf 20220602-datsegment1C.tar".

    • Time-Frequency Dependent Polarization Polfre_s1.66el.tar: This program measures Rayleigh wave azimuth (BAZ(t,f)) and degree of elliptical polarization (DOP_el(t,f)), among other parameters, as used in Schimmel et al. (2011b). The tar file contains source code, documentation, and four example datasets. The programs use SAC data and run on Linux systems. To use, download the tar file and extract it with "tar xvf Polfre_s1.66el.tar" (Previous version:here). DOI: 10.20350/digitalCSIC/13840)

    • Rayleigh Wave Ellipticity Measurement and Inversion DOP-E: This program determines Rayleigh wave ellipticity as a function of frequency (and BAZ) from seismic noise and event recordings, and performs inversion to constrain S-wave velocity, as described in Berbellini et al. (2019). Ellipticity measurements are based on Schimmel and Gallart (2004) and Schimmel et al. (2011b). The software works with SAC data format and includes an example dataset.

    • Time-Scale Phase Weighted Stacking Code ts-PWS: This GitHub repository provides the fast and efficient ts-PWS stacking code for processing large seismic datasets. It is based on wavelet theory and equivalent in functionality to tf-PWS0. The design, implementation, and performance are described in Ventosa et al. (2017) and Schimmel et al. (2017). The software supports SAC and binary data formats. DOI: 10.20350/digitalCSIC/13846)

    • Fast Phase Cross-Correlation PCC: This repository provides a fast and efficient phase cross-correlation code for processing large seismic datasets. The approach is described in Ventosa et al. (2019). C programs and example datasets are included. DOI: 10.20350/digitalCSIC/13837

    • Fast Phase Cross-Correlation (Python) PCC2: This Python routine, developed by Luis-Fabian Bonilla (IPGP & IFSTTAR), efficiently computes phase cross-correlation. It reduces computational complexity through an analytic simplification of the PCC equation using powers of two. Details are provided in Ventosa et al. (2019).

    • Group Velocity Determination Using Phase Coherence and Resampling TS_PWS0_UG_1.5: This code measures group velocities in seismic noise correlation studies, as described in Schimmel et al. (2017). It employs phase coherence in the wavelet domain to stack noise cross-correlations and uses resampling strategies to robustly determine group velocities. An example dataset is provided, based on Figure 5 of Schimmel et al. (2017).

      Good luck! If these programs have been useful in your research, please cite the main or corresponding publications. Thank you.

    P-Wave & S-Wave Velocity Models (plain text files):

    • P-wave and S-wave velocity models - Brazil BR_tomo.tar.bz2: This archive contains P- and S-wave velocity models (velocity perturbations) for Brazil, as published in Schimmel et al. (2003). The tar.bz2 file can be extracted with: tar xvfj 2003-MSchi_BR_tomo.tar.bz2. It includes plain text files with: P- and S-wave velocity perturbations; Reference velocity model; absolute velocities at grid points (latitude, longitude, depth). Absolute velocities should be interpreted with care. The inversion provides velocity perturbations and is invariant to constant velocity shifts (see Fig. 13 in the publication).

    • Group Velocity Maps 2021-Nuñez_CR-GroupVelocityMaps.txt and 3-D S-Wave Velocity Model 2021-Nuñez_CR-S-VelocityModel.txt - Costa Rica: This dataset includes ambient-noise fundamental-mode Rayleigh wave group velocity maps and the corresponding 3-D S-wave velocity model for Costa Rica, as published in Nuñez et al. (2020). Files and formats:
      2021-Nuñez_CR-GroupVelocityMaps.txt: Latitude, Longitude, Period, Group Velocity (units: deg, deg, s, km/s)
      2021-Nuñez_CR-S-VelocityModel.txt: Latitude, Longitude, Depth, S-wave Velocity (units: deg, deg, km, km/s)
      Note: An S-wave velocity of NaN indicates no resolution at the corresponding grid point.

    Discontinuity Depth Maps (plain text files):

    • Upper Mantle Discontinuities Beneath Iberia and N-Morocco 2015-Bonatto_Iberia_410-660.tar.gz: This archive contains the depths of the 410-km and 660-km upper-mantle discontinuities, as published in Bonatto et al. (2015). The tar file can be extracted with: tar xvf 2015-Bonatto_Iberia_410-660.tar . It includes three plain text files with: Station positions; Depths of the 410-km and 660-km discontinuities as a function of bin position. File contents and formats are explained in the included README.pdf. These tables were used to generate Figures 1b, 5–9 in Bonatto et al. (2015). See also Bonatto et al. (2013) for further details.

    • Paleozoic Ebro Basin (Spain) 2018-Romero_EbroBasin.txt: This text file provides Table S1 from Romero and Schimmel (2018). The study uses ambient noise autocorrelations to retrieve the shallow subsurface reflection response in the Ebro Basin, focusing on the Paleozoic basement. The table includes: Station positions; Two-way travel times of reflected P-waves; Final discontinuity depth values derived from the velocity models. For further details, see Romero and Schimmel (2018).

    Monitoring Structural Medium Changes (Detection & Location):

    • Monitoring the 2011 El Hierro, Submarine Eruption, Canary Islands 2018-Sanchez_ElHierro_volcan.mp4: This movie visualizes scattering cross-section density maps at various lag times as a function of recording day. It illustrates medium changes for different phases (e.g., 2011 pre-eruption, quiet periods, 2012 pre-intrusions, and intrusions). Data analyses and inversion methods are described in Sanchez-Pastor et al. (2018).

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