Mastering Pytomo: A Step-by-Step Guide to Seismic Data Processing in Python
Seismic tomography is a powerful method for imaging the Earth’s subsurface. Pytomo is a specialized Python workflow designed to streamline this complex process. This guide provides a clear, step-by-step walkthrough to help you master seismic data processing using Pytomo. Prerequisites and Installation
Before starting, ensure you have a Python environment ready. Pytomo relies heavily on standard scientific libraries like NumPy, SciPy, and ObsPy. Install the necessary dependencies using your terminal: pip install numpy scipy obspy matplotlib Use code with caution. Step 1: Data Preparation and Quality Control
Successful tomography requires clean, well-formatted input data. You must organize your seismic waveforms, station coordinates, and event locations.
Load waveforms: Use ObsPy to read standard formats like SEGY or SAC.
Remove trends: Apply detrending to eliminate baseline drifts.
Filter noise: Apply a bandpass filter to isolate target frequencies.
Pick arrivals: Identify and save precise P-wave or S-wave arrival times. Step 2: Setting Up the Initial Velocity Model
Tomography is an iterative inversion process that requires a starting guess. Your initial model defines the baseline velocity structure of the target region.
Define grid: Establish a 3D grid spacing based on your station density.
Assign velocities: Populate the grid using standard 1D global models (e.g., AK135 or IASP91).
Format input: Convert this model into the specific matrix format required by Pytomo. Step 3: Forward Modeling (Ray Tracing)
Forward modeling calculates the theoretical travel times from sources to receivers through your initial model.
Select method: Pytomo utilizes ray-tracing algorithms to map seismic paths.
Compute paths: Calculate the exact geometric path for every source-receiver pair.
Generate residuals: Subtract the calculated travel times from your observed travel times. Step 4: Running the Inversion
The core of Pytomo is solving the inverse problem. This step updates the initial velocity model to minimize the travel-time residuals.
Build kernel: Construct the sensitivity matrix (G-matrix) from the ray paths.
Apply regularization: Add damping and smoothing parameters to prevent unstable solutions.
Solve system: Run the inversion solver (such as LSQR) to calculate velocity perturbations.
Update model: Add the perturbations back to your baseline model to create a new 3D model. Step 5: Visualization and Interpretation
The final step is converting your numerical matrices into interpretable geological images.
Plot slices: Generate horizontal and vertical cross-sections of the subsurface.
Check resolution: Run checkerboard tests to evaluate which areas of your model are reliable.
Export results: Save the final velocity grid for interpretation in external 3D software.
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