Quantification of Hydrocarbon Contamination in Soil Using Hyperspectral Data and Deep Learning
Bibliographic record
Abstract
Petroleum and its products undergo large scale production, transportation, and storage which make them prone to spills and leakages into the environment.Petroleum contamination in terrestrial environments, particularly soil bodies, is common and holds major consequences on food and crops, microbial communities, the atmosphere, the water sphere, public health and safety, and the soil itself, and therefore, requires immediate detection and assessment in case contamination is present.In this paper, advanced machine learning methods are used to predict petroleum hydrocarbon contamination in soil using hyperspectral image data.Hyperspectral imaging combines imaging and spectroscopy and can detect petroleum hydrocarbons using the characteristic absorption features in hydrocarbon reflectance spectra.Laboratory prepared soil samples are contaminated with crude oil and scanned with a hyperspectral camera in a laboratory setup.The data collected is used to train deep learning models to predict, quantitatively, the amount of petroleum present in soil samples.To make predictions, a first model is built to use spectral data from a single pixel while a second model is built to use spectral and spatial data by using two adjacent pixel spectra as input.The results show good performance for both models, with the twopixel model achieving a better mean square error of 0.48 on a testing dataset compared to the mean square error of 0.628 for the single pixel model on the same testing data.Therefore, hyperspectral imaging contains valid spectral and spatial information that are beneficial for assessing petroleum contamination in soil with good accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".