Focused Seismic Monitoring on a 3D Laboratory Dataset Internship

SpotLight Earth; Massy; On-site; EUR 7,800 - 12,000; Full time

Job summary

SpotLight Earth is offering an end-of-studies internship in geophysics to advance sparse CO₂ monitoring. You will develop data pipelines, standardize CO₂ lab datasets, and evaluate a focused seismic workflow against known ground truth.

The internship aims to answer key questions about performance, uncertainty, and detection errors while documenting methods and results for potential publication. Fluent English is expected; basic French is acceptable.

Qualifications

• Final-year MSc or engineering student in geophysics or related field.
• Strong Python data analysis skills and ability to handle large datasets.
• Background in active-source seismic, SNR/NRMS concepts, and basic uncertainty ideas.

Responsibilities

• Build a top-view CO₂ detection map from spots.
• Benchmark detection against ground truth using SNR and NRMS.
• Quantify uncertainty in plume forecasts and detection risk.

Skills

Python programming

Data handling

English proficiency

French basic

Seismic data understanding

Uncertainty quantification basics

Education

MSc or engineering student (final year)

Tools

NumPy

SciPy

Matplotlib

SEG-Y understanding

Job description

Ultra-sparse acquisition makes it possible to monitor CO₂ storage sites more frequently and at a fraction of the cost of conventional surveys. To support its deployment at scale, the next step is to characterize its performance in a fully controlled setting and to define best practices for survey design. 3D acoustic lab data offer a unique opportunity to do this: its 4D dataset can be used to benchmark the focused seismic approach against a known plume.

The internship aims to answer three questions. How well does sparse acquisition perform, and which parameters control where and when it performs well? How much uncertainty in the survey and in the plume forecast can be tolerated? And what are the consequences of detection errors, particularly false negatives, for CO₂ monitoring?

Missions

1. Build a top-view CO₂ detection map from spots

• Import and standardize the CO₂ lab dataset, reconciling pickle and SEG-Y formats and resolving missing-header issues. Careful data management is an integral part of this work.

• Apply the SpotLight workflow (de-migration, optimal selection, focused seismic processing and detection) to produce, for each survey, a top-down map of the lab model in which each pixel is a single focused seismic spot from one source–receiver pair.

2. Benchmark the focused seismic approach against the lab ground truth

• Evaluate the quality of each spot relative to the ground truth, using attributes such as local topography, signal-to-noise ratio (SNR) and NRMS.

• Benchmark CO₂ detection capability by quantifying how much of the plume is resolved as a function of the number of spots, comparing random, regular and optimized down-sampling strategies to establish a cost-versus-detection (value of information) relationship.

3. Assess uncertainty in focused seismic monitoring

• Introduce controlled perturbations to SNR and NRMS and analyze their impact on monitoring results, in relation to spot properties: noise level, geology and structural dip, and acquisition geometry (offset, azimuth, repositioning errors).

• Perform uncertainty quantification on plume forecasting and assess its effect on detection risk, including false positive and false negative rates.

Way forward:

• Repeat the workflow on the public Sleipner dataset, to test the method with real case scenario.

Throughout the internship, the intern will document methods and results, present progress regularly to both supervisors, and deliver a final report and presentation. Depending on results, the work may contribute to a conference abstract or publication.

We are looking for a final-year MSc or engineering student seeking an end-of-studies internship, in geophysics, geoscience, applied physics or a related field.RequiredSolid background in active-source seismic methods: acquisition geometry, seismic processing and imaging fundamentals (migration, wavelets, SNR, 4D/time-lapse concepts).Good programming skills in Python (NumPy, SciPy, Matplotlib), and comfort working with large datasets.Rigorous, organized approach to data handling and documentation.English: fluent or native, French: basic proficiency or conversational.AppreciatedKnowledge of time-lapse seismic, FWI or CO₂ storage monitoring.Familiarity with uncertainty quantification, statistics or signal detection theory.Interest in the energy transition and in bridging academic research and industrial applications.