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GP-GE-1HWIV61GeologyReady

Hyderabad P-Receiver Function Crustal Thickness and Vp/Vs Robustness Study

A completed geology and seismology study estimating first-order crustal thickness and Vp/Vs beneath Hyderabad from public teleseismic waveforms, with explicit data quality and robustness checks.

Hyderabad P-Receiver Function Crustal Thickness and Vp/Vs Robustness Study project visual
GP-GE-1HWIV61 · Geology
  • Python 3.14
  • ObsPy 1.5.0
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib

Software compatibility

ObsPy 1.5.0 and Python 3.14 only

The released analysis, retained results, figures, tests, and documentation use ObsPy 1.5.0 with the pinned Python 3.14 environment and the checksummed GEOSCOPE HYB dataset. Other versions, stations, data windows, or processing parameters require a new verification run.

Project definition

Problem statement

Receiver functions can estimate bulk crustal thickness and Vp/Vs from converted seismic phases, but the result depends on waveform quality, polarity, deconvolution, assumed crustal velocity, phase weights, and source direction.

The geology problem is to produce a traceable station-average estimate beneath Hyderabad while separating event-sampling precision from model and directional sensitivity.

Project objectives

  • Acquire and preserve a balanced public teleseismic dataset for GEOSCOPE station HYB.
  • Remove instrument response, rotate three-component records, and estimate radial and transverse receiver functions.
  • Apply explicit quality rules and retain every metric and rejection reason.
  • Estimate crustal thickness and Vp/Vs through H-kappa stacking.
  • Test event resampling, assumed Vp, phase weights, backazimuth sectors, and synthetic recovery.

Project structure

Project components

01

Data acquisition

Retrieves the event catalogue, StationXML, and balanced three-component waveforms with checksums and provenance.

02

Signal processing

Corrects instrument response, filters, rotates to LQT, applies the radial polarity convention, and performs water-level deconvolution.

03

Quality control

Measures signal-to-noise ratio, transverse energy, deconvolution fit, and receiver-function amplitude for every event.

04

Crustal estimator

Calculates event-specific converted-phase times and the weighted H-kappa stack over the declared search grid.

05

Robustness analysis

Runs 1,000 bootstraps, five assumption cases, four directional sectors, and four synthetic noise experiments.

Methodology

Project workflow

  1. 01
    Verify retained data

    The pipeline validates the StationXML, waveform inventory, event manifest, and declared checksums.

  2. 02
    Build receiver functions

    Each accepted triplet is corrected, filtered, rotated, deconvolved, measured, and retained with its event metadata.

  3. 03
    Search H and Vp/Vs

    Predicted Ps, PpPs, and later-multiple times are sampled for every accepted event and grid cell.

  4. 04
    Test robustness

    Bootstrap, model, weight, directional, and synthetic experiments distinguish stable evidence from assumptions.

  5. 05
    Retain evidence

    CSV, JSON, NPZ, PNG, Word, and PDF outputs preserve the complete chain from input records to conclusions.

Demonstration scenario

The retained sample accepts 48 of 55 receiver functions. With Vp fixed at 6.4 km/s, the reference stack estimates H at 32.4 km and Vp/Vs at 1.750. Event bootstraps span 32.2 to 32.6 km, while plausible fixed-Vp cases move H from 31.2 to 33.8 km, showing why model sensitivity must remain separate from sampling precision.

Engineering

Tools and method

Tools
The project uses Python 3.14, ObsPy 1.5.0, NumPy, SciPy, Pandas, Matplotlib for subject analysis, simulation, and results.
Seismic data layer
ObsPy reads MiniSEED and StationXML, calculates travel times, removes response, and performs coordinate rotation.
Numerical layer
NumPy and SciPy implement spectral deconvolution, interpolation, H-kappa contributions, resampling, and diagnostics.
Experiment layer
A configuration-controlled Python pipeline runs the reference study and every declared robustness case.
Evidence layer
Pandas, JSON, CSV, NPZ, and Matplotlib retain event decisions, numerical arrays, summaries, and labelled figures.
Verification
Automated tests, coverage, dependency audit, Docker execution, document audits, and visual review verify the delivery.

Testing

Evaluation

Evaluation measures

  • Accepted waveform count and each per-event quality-control metric
  • Reference H-kappa maximum, score, H, and Vp/Vs
  • Conditional 2.5th and 97.5th event-bootstrap percentiles
  • Sensitivity to fixed Vp and phase-family weights
  • Directional spread across four backazimuth sectors
  • Synthetic recovery at four noise levels
  • Automated tests, coverage, dependency audit, document audit, and container execution

Project boundaries

  • The result is a single-station, one-dimensional, one-layer H-kappa appraisal rather than a unique crustal velocity-depth model.
  • The bootstrap interval measures accepted-event sample stability under fixed processing and model assumptions.
  • Four broad sectors can reveal directional variation but cannot uniquely resolve dip, anisotropy, or lateral structure.
  • Geological interpretation beyond the retained evidence requires independent velocity, gravity, active-source, mapping, or multi-station constraints.

Included

  1. 01Complete Python and ObsPy source code
  2. 02Fifty-five retained public three-component GEOSCOPE waveform records
  3. 03StationXML, event manifest, checksums, and per-event quality-control evidence
  4. 04Forty-eight accepted radial and transverse receiver functions
  5. 05H-kappa surface, 1,000 bootstrap estimates, five sensitivity cases, and four backazimuth-sector estimates
  6. 06Four synthetic recovery cases and sixteen generated project figures
  7. 07Twenty-three automated tests with 97.30 percent branch-aware coverage
  8. 08Complete project files, calculations, results, and analysis material in a private GitHub repository
  9. 0999-page project documentation in PDF and editable Word formats
  10. 1012-page setup and usage guide in PDF and editable Word formats
  11. 11Forty-five annotated references and two attributed current literature figures

Project record

No information is collected on this page.

Permanent project ID
GP-GE-1HWIV61
Catalogued
21 Aug 2026
Completed
28 Aug 2026
Verified
28 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.