seismic-interpretation

Seismic Interpretation Workflow

End-to-end pipeline for seismic data analysis, from loading SEG-Y files through signal processing, rock physics modelling, and 3D visualization.

Skill Chain

segyio          obspy           bruges          disba           pyvista
[SEG-Y I/O] --> [Signal Proc] --> [Rock Physics] --> [Dispersion] --> [3D Viz]
  |               |                |                 |                |
  Load traces     Filter/FFT       AVO modelling     Surface waves    Volume render
  Read headers    Instrument resp   Fluid sub         Phase velocity   Slice display
  3D geometry     Spectral anal    Synthetics        Inversion        Horizon pick

Decision Points

Question	If Yes	If No
Working with SEG-Y files?	Start with segyio	Use obspy for miniSEED/SAC
Need frequency filtering or spectral analysis?	Use obspy signal tools	Skip to rock physics
Performing AVO or fluid substitution?	Use bruges	Skip to visualization
Analysing surface waves (MASW/SASW)?	Use disba for dispersion	Skip disba
Need 3D volume rendering?	Use pyvista	Use matplotlib for 2D

Step-by-Step Orchestration

Stage 1: Data Loading (segyio)

import segyio
import numpy as np

# Load and inspect SEG-Y
with segyio.open('survey.sgy', 'r', iline=189, xline=193) as f:
    n_traces = f.tracecount
    n_samples = len(f.samples)
    dt = f.samples[1] - f.samples[0]  # ms
    cube = segyio.tools.cube(f)        # (ilines, xlines, samples)

    # Extract header info for coordinates
    cdp_x = f.attributes(segyio.TraceField.CDP_X)[:]
    cdp_y = f.attributes(segyio.TraceField.CDP_Y)[:]

Stage 2: Signal Processing (obspy)

from obspy import Trace, Stream
from obspy.signal.filter import bandpass

# Convert numpy trace to obspy for processing
tr = Trace(data=cube[50, 100, :].astype(np.float64))
tr.stats.sampling_rate = 1000.0 / dt  # Hz
tr.stats.delta = dt / 1000.0          # seconds

# Bandpass filter
tr.filter('bandpass', freqmin=5, freqmax=80, corners=4, zerophase=True)

# Spectral analysis
from obspy.signal.tf_misfit import cwt
scalogram = cwt(tr.data, dt=tr.stats.delta, w0=6, fmin=1, fmax=100, nf=50)