# Global-scale AI-powered prediction of hydrogen seeps

V. Roche, J. Daynac, S. Hesni, U. Geymond, N. Ginzburg, J. Philippon, I. Moretti

► To cite this version:

V. Roche, J. Daynac, S. Hesni, U. Geymond, N. Ginzburg, et al. Global-scale AI-powered prediction of hydrogen seeps. Marine and Petroleum Geology, 2026, 183, pp.107617. 10.1016/j.marpetgeo.2025.107617. hal-05491561

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# Global-scale AI-powered prediction of hydrogen seeps

V. Roche<sup>a,* ID</sup>, J. Daynac<sup>a ID</sup>, S. Hesni<sup>a</sup>, U. Geymond<sup>b,c ID</sup>, N. Ginzburg<sup>b ID</sup>, J. Philippon<sup>a</sup>, I. Moretti<sup>d ID</sup>

## ARTICLE INFO

### Keywords:

- Natural hydrogen

- Artificial Intelligence

- Seepages

- Spatial and morphometric analyses

## ABSTRACT

Natural hydrogen (H₂) holds promising potential as a clean energy source, but its exploration remains challenging due to limited knowledge and a lack of quantitative tools. In this context, identifying active H₂ seepage areas is crucial for advancing exploration efforts. Here, we focus on *sub-circular depressions* (SCDs) that often mark high H₂ concentration in soils, thought to correspond to deeper fluxes seeping at the surface, making them promising targets for exploration. Coupling open-access Google Earth® images and in-field H₂ measurement data, an artificial intelligence model was trained to detect seepage zones. The model achieves an average precision of 95 %, detects and maps seepage zones in new regions like Kazakhstan and South Africa, highlighting its potential for global application. Moreover, preliminary spatial analyses show that geological features control the distribution of H₂-SCDs that can emit billions of tons of H₂ at the scale of a sedimentary basin. This study paves the way for a faster and more efficient methodology for selecting H₂ exploration targets.

### Plain Language Summary.

Natural hydrogen is a promising clean energy source, but it remains difficult to explore due to a lack of accessible tools. In this study, we used free satellite images (Google Earth®) and in-field hydrogen measurements to identify specific surface features - small *sub-circular depressions* (SCDs) - that often mark areas where hydrogen is seeping from underground. We trained an artificial intelligence model to detect these depressions, using a dataset of confirmed hydrogen-emitting SCDs collected across five countries. Thanks to this diversity in the training data, the model can be applied at a global scale, having learned to recognize a wide variety of structures associated with hydrogen seepage. To validate its effectiveness, the model was tested on two random regions - in Kazakhstan and South Africa - and successfully identified over a thousand new potential hydrogen-emitting depressions. With an average precision of 95 %, this tool offers a fast and reliable way to map natural hydrogen seepage zones, helping guide future exploration efforts worldwide.

## 1. Introduction

Hydrogen holds promise as a clean energy source, but is today manufactured from fossil fuel, and these methods are energy-intensive and environmentally harmful, often leading to greenhouse gas emissions (Lapi et al., 2022). Natural hydrogen (hereafter H₂) appears as a possible alternative, which opens the way for a new realm of exploration and exploitation that is both more cost-effective and environmentally friendly (Brandt, 2023). Although its potential is still poorly estimated (Lévy et al., 2023), in 2020, two global assessments proposed that H₂

generation in the subsurface may yield 23 million metric tons per year, roughly a quarter of the world's current hydrogen consumption (Worman et al., 2020; Zgonnik, 2020). Since numerous other H₂ leakages have been discovered and other H₂-generating rocks characterized (Lévy et al., 2023). For instance, Truche et al. (2024) estimated that over 200 tons per year of H₂ are continuously released from the mine galleries of the Bulqizë chromite mine in Albania, suggesting the presence of a faulted reservoir deeply rooted within the Jurassic ophiolite massif. More recently, considerable volumes have been estimated in several Chinese basins. In the Songliao volcanic basin, Liu et al. (2025) assessed

<footnote><sup>a</sup> Laboratoire de Planétologie et Géosciences, LPG UMR 6112, CNRS, Le Mans Université, Univ Angers, Nantes Université, Avenue Olivier Messiaen, 72085, Le Mans, France</footnote>

<footnote><sup>b</sup> Institut de physique du globe de Paris, Université Paris Cité, CNRS, 75005, Paris, France</footnote>

<footnote><sup>c</sup> IFP énergies nouvelles, 92500, Rueil-Malmaison, France</footnote>

<footnote><sup>d</sup> Laboratoire des Fluides Complexes et leurs Réservoirs - IPRA, E2S-UPPA, TotalEnergies, CNRS, Université de Pau et des Pays de l'Adour, UMR5150, Pau, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: vincent.roche@univ-lemans.fr (V. Roche).</footnote>
that more than 100 × 10⁹ m³ of H₂ has been generated, mainly through three complementary processes: i) radiolysis of water induced by the decay of radioactive elements, ii) hydrothermal alteration of iron-rich rocks, and iii) degassing of fluids derived from the mantle. Additional accumulations of more than 20 × 10⁹ m³ have also been identified in the Bohai Bay basin, near the Tancheng-Lujiang deep fault area (Wei et al., 2025). At a global scale, Ellis and Gelman (2024) estimated natural hydrogen reserves on the Earth's subsurface endowment in-place at 5.6 × 10⁶ million tons, implying that H₂ could play a crucial role in the energy transition and modern industry. While the presence of H₂ has been identified in the oceanic domain near oceanic spreading centers (e. g., Cannat et al., 2010), exploration is currently limited onshore for economic reasons.

H₂ exploration is ongoing in many countries (e.g., USA, Australia, France), and for now, the major challenge is to find accumulations in the subsurface. The emblematic Bourabougou field in Mali, where the first H₂ production took place for more than 12 years, provides an example of continental H₂ systems (Maiga et al., 2023, 2024; Prinzhofer et al., 2018). In 2023, Gold Hydrogen also drilled two H₂ wells in South Australia, finding H₂ in the subsurface and demonstrating that

exploration can be successfully conducted for this highly attractive carbon-free energy source (Whitcombe et al., 2024). All the new data confirm the existence of H₂ accumulations beneath the Earth's surface. This suggests that H₂ systems can, at first glance, be considered similar to the petroleum systems, with source, reservoir rock, and seal. In that case, H₂ may be generated and accumulated over time (Lodhia et al., 2024; Prinzhofer and Cacas-Stentz, 2023; Roche et al., 2024; Truche et al., 2024; Liu et al., 2025; Wei et al., 2025; Nicoletti et al., 2025), potentially leading to economically viable accumulations. However, due to the size of the H₂ molecule, most rocks do not form perfect seals, resulting in numerous seepages.

Onshore, most of them are located in mountain belts and forelands, in old terrains, and/or volcanic-bearing sedimentary basin (Fig. 1). Over the past decade, various studies have focused on rounded landform seeping H₂, also called *sub-circular depression* (SCD). The first reports came from researchers in Russia, who detected H₂ concentrations along with trace amounts of methane in specific morphological depressions, suggesting that these structures may indicate subsurface processes responsible for H₂ seeps (Fig. 1A; Larin et al., 2015). Meanwhile, similar observations were made in North Carolina (USA), where H₂

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** World distribution of free H₂ occurrences. Colored dots correspond to data overview compiled by Zgonnik (2020), Truche et al. (2020) and Qingqiang et al. (2024). Red dots correspond to data compiled by Truche et al. (2020). Blue dots indicate the location of SCDs where H₂ concentrations have been measured. (A–E) All these examples are cited in the main text and used for training the algorithm.</figcaption></figure>
concentrations were detected (Fig. 1B; Zgonnik et al., 2015). More recent studies have highlighted H₂ concentration anomalies in SCDs in other countries such as Brazil (Fig. 1C; Moretti et al., 2021b; Prinzhofer et al., 2019), Namibia (Fig. 1D; Moretti et al., 2022; Roche et al., 2024), and Australia (Fig. 1E; Aimar et al., 2023; Frery et al., 2021; Moretti et al., 2021a). For example, in the Minas Gerais State (Brazil), soil gas monitoring was conducted at two SCDs in the São Francisco Basin, and results showed pulses of H₂ concentration in the soil with an average flux estimated at around 0.03–0.04 m³/day/m² equivalent to 700 kg/day (Moretti et al., 2021b; Prinzhofer et al., 2019). Although H₂ concentrations in soil can vary over time and across different locations

within a given structure (e.g., Larin et al., 2015; Zgonnik et al., 2015), these geological structures are considered as signs of deep H₂ flux escaping from underlying reservoirs or degassing aquifers (e.g., Frery et al., 2021; Lévy et al., 2023; Moretti et al., 2021a; Liu et al., 2025; Wei et al., 2025). Interestingly, many of the discoveries of H₂-related landforms were made in areas with iron-rich, metamorphic, and igneous Precambrian basement rocks, where various processes like Fe²⁺ oxidation and/or radiolysis may occur (e.g., Geymond et al., 2022; Lollar et al., 2014; Moretti et al., 2021a). Thus, these SCDs serve as an early indicator for H₂ exploration, and to effectively tap into this resource, we require reliable tools to accurately locate them.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Protocol for prospecting with Artificial Intelligence. (A–F) Workflow of the YOLOv8 neural network architecture (A) applied to satellite imagery (B). Relying on a verité terrain mask as a geomorphological reference (C), training is carried out using 365 satellite images taken in 5 different countries, with samples featuring other non-interesting structures to enhance learning (D). After training, Yolov8 can generate model detection outputs in the form of bounding boxes accompanied by their confidence score to verify the learning results (above 70 % of confidence) (E). Once an optimal model has been achieved, the AI is deployed over a large area to generate a semantic prediction of SCDs (F).</figcaption></figure>
Due to their size, ranging from tens to several hundred meters in width, and their associated vegetation properties (Moretti et al., 2021a, 2022), these structures are easily recognized using satellite imagery. While recent studies have relied on open-source multispectral and high-resolution imagery to detect and characterize these SCDs and thereby improve H₂ exploration efficiency (Mosquera-Rivera et al., 2024; Ginzburg et al., 2025), none have applied a semantic segmentation approach to investigate their morphometric parameters. Here, we employed an artificial neural network (YOLOv8; Jocher et al., 2023) to automatically identify H₂ seeps associated with these SCDs. These techniques have gained significant popularity for automating various tasks across numerous geoscience fields (Daynac et al., 2024; Eyring et al., 2024; Radulescu et al., 2024). We trained a recognition model to locate and map potential seepage zones, and results show it effectively distinguishes SCDs from other geological and anthropogenic structures on a validation subset. This model was tested in new regions, including Kazakhstan and South Africa, and results demonstrate the power of AI in generalizing SCDs recognition on a global scale. Beyond landform detection, our methodology provides a preview of analyses that can be conducted to establish links between the circularity of depressions and the local geological context, offering insights into understanding the H₂ migration and spatial distribution of H₂ seepages.

## 2. Methods

Our objective was to automate the detection of H₂-related SCDs. Recently, Ginzburg et al. (2025) utilized open-source multispectral data and high-resolution imagery to train a deep learning model (YOLOv8) for classifying rounded landforms and detecting H₂-related SCDs. They showed that their model achieved 90 % accuracy using Google Maps© imagery, surpassing the performance of Sentinel-2 multispectral data. In this study, we also used a YOLOv8 convolutional neural network (CNN) algorithm (Jocher et al., 2023) (Fig. 2A), and we decided to focus on freely accessible high-resolution satellite Google Earth© images. This approach aims to provide a decision-making and prospecting tool that can optimize exploration target selection.

### 2.1. Image registration and collection for the training sample dataset

The input digital dataset must have global coverage with sufficient resolution to ensure workflow reproducibility and identify objects of interest. Based on high-resolution Google Earth© images (<10 m), we created an expert-annotated dataset using 365 satellite images from different countries, where morphological criteria (circularity, vegetation rings, subsidence level) combined with field measurements previously confirmed the presence of H₂ seeps (Aimar et al., 2023; Frery et al., 2021; Larin et al., 2015; Moretti et al., 2022; Prinzhofer et al., 2019; Roche et al., 2024; Zgonnik et al., 2015). The image locations are as follows: Brazil, -16.52814N, -45.29548E; Australia, -30.60372N, 115.96151E; United States of America, 34.77635N, -78.59475E; Namibia, -20.6446N, 17.495530E; and Russia, 51.14256N, 41.95181E. Each SCD has been confirmed and annotated by experts in the form of reference masks, in which all pixels correspond to areas classified as SCDs (Fig. 2B and C).

However, the images used from Google Earth© originate from various providers (e.g., Maxar, Airbus, Copernicus) and cover different time periods, with no access to the associated metadata (Ginzburg et al., 2025). This heterogeneity means that surface conditions (e.g., soil moisture, vegetation cover, cloud shadows) may vary significantly from one site to another depending on the season, year, or even time of day at which the images were captured (Ginzburg et al., 2025). Unlike standardized satellite datasets such as Sentinel-2, it is therefore not possible to control or correct for these temporal effects. This limitation should be addressed in future model improvements, particularly in regions subject to strong seasonal or environmental variability. Mosquera-Rivera et al. (2024) also highlighted the importance of these temporal variations

through remote sensing analyses based on a PCA approach.

Each training sample measures 1600 × 1600 pixels and is resized to 640 × 640 pixels, as required by the Yolo model. The majority of the images contains, in addition to the structures of interest, other natural (fluvial systems, salt lakes, rock outcrops) or anthropogenic (farm circles, roads, buildings, cultivated fields) landscape features, considered insignificant for H₂ exploration (Fig. 2D). This strategy, which incorporates target objects (SCDs) and off-target features into the samples, aims to effectively train the YOLO algorithm to distinguish SCDs from their surroundings and reduce false positives linked to morphologically similar structures. In addition, the SCDs selected come from a wide range of geographical regions, offering significant diversity in shape, size, and morphological characteristics (presence or absence of vegetation rings, water-filled structures, totally vegetated or dry structures). This heterogeneity guarantees greater model robustness and facilitates the ability to generalize detection to other regions of the globe with different geological and environmental contexts.

### 2.2. Deep learning architecture

In this study, we used the YOLOv8 deep learning model (Jocher et al., 2023). This end-to-end architecture is specifically adapted to semantic object detection, producing bounding boxes or semantic mapping of interest objects (Fig. 2E and F). Its architecture is based on three main components: the backbone (feature extraction), the neck (multiscale fusion), and the head (final predictions). The backbone extracts visual features from RGB images. It relies on two modules: C2f (CSP Bottleneck with two convolutions), which combines two convolution bottlenecks to fuse contextual and morphological information useful for SCD detection, and SPPF (Spatial Pyramid Pooling Fast), which allows extracted features to be pooled into a fixed-size output, providing greater robustness to size variations and improving detection of small objects (Xu et al., 2025). Activation in Yolo is by a SiLU (Sigmoid Linear Unit) function, to introduce non-linearity in SCD identification and thus better capture diffuse and low-contrast SCDs. The neck of the network implements a PAN-FPN structure, combining a Path Aggregation Network (PAN) and a Feature Pyramid Network (FPN), to efficiently merge features at different resolutions, while improving the spatial localization of SCDs. This bidirectional structure (top-down and bottom-up) ensures better representation of objects, whatever their scale and environmental context. The model head produces the final predictions using a decoupled structure, composed of two branches for classification and segmentation tasks, allowing for faster convergence and improved accuracy detection of SCDs. YOLOv8 operates on three output scales, enabling it to efficiently detect objects of various sizes.

### 2.3. Generating model training and validation metrics

The dataset was divided into two subsets: 80 % of the images were used for model training, while the remaining 20 % were reserved for validation and were never seen by the network during training. The model was trained for 400 epochs using a batch size of 16. We used the AdamW optimizer with a classical learning rate of 0.001 and a weight decay of 0.01. The learning rate was progressively reduced during training using a scheduler (lrf = 0.1). Performance was monitored using the Binary Cross-Entropy (BCE) loss function applied to the training data.

To enhance the model's generalization capabilities, data augmentation was applied with default hyperparameters to each training sample through a combination of deterministic and probabilistic transformations. These included random horizontal flipping (p = 0.5), isotropic scaling (0.5), affine translations up (0.1) of the image dimensions, and random erasing (p = 0.4). Additionally, RandAugment was employed as an automated augmentation feature to apply a stochastic combination of intensity and geometry-based transformations, dynamically increasing input variability without the need for manual tuning of augmentation parameters. The optimal model weights were
saved based on the performance calculated by the loss function on the training subset.

After training, the model was applied to the validation subset to assess its ability to accurately predict SCDs. This evaluation is based on precision (Eq. (1)), which corresponds to the proportion of SCDs detected by the model that are correct, and recall (Eq. (2)), which measures the proportion of existing SCDs that were successfully detected (Fig. 2F; e.g. Hesni et al., 2025):

$$precision = \frac{TP}{TP + FP} \qquad (Eq.1)$$

$$recall = \frac{TP}{TP + FN} \qquad (Eq.2)$$

where TP, FP, and FN represent the number of true positive, false positive, and false negative predictions, respectively, between the expert annotation masks and automatic predictions.

If the assessment shows good ability to identify and map SCDs, then the final YOLOv8 predictions were exported as binary raster masks, representing the presence or absence of SCDs at the pixel level. These binary outputs can be directly imported into any GIS environment for spatial analysis. All subsequent geospatial operations were conducted in

QGIS© version 3.22. The binary rasters were vectorized using the GRASS GIS tool r.to.vect, and obvious incoherent predictions (artefacts or misdetections, see Appendix A. Fig. S1) were manually removed. To improve the geometric fidelity of SCD outlines and reduce aliasing introduced during raster-to-vector conversion, the resulting polygons were smoothed using the v.generalize function with the snake algorithm (Burghardt, 2005). This procedure ensures more accurate and topologically coherent boundaries for subsequent morphometric and spatial analyses.

## 3. Results & discussion

### 3.1. Neural network training performances

After training, a mean precision of 95 % with a mean recall of 93 % was achieved in predicting SCDs (Fig. 3A). These results demonstrate a high correspondence between the model predictions and the reference masks established by experts, with a high percentage of pixels correctly classified as H₂ SCDs. Visual validation of the samples (Fig. 3B) shows a consistent match in the detected areas, with confidence levels represented by box plots generally above 70 %, confirming the robustness and reliability of our approach.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Metric and visual performances of SCD segmentation predictions by Yolov8 on validation subset. (A) Training metrics show 95 % precision and 93 % recall after 400 epochs. (B) Visual validation illustrates a high correspondence between expert references and model predictions. (C) Validation map in Minas Gerais State (Brazil) demonstrates accurate detection of SCDs while avoiding irrelevant structures (e.g., farm circles). Note that these structures were first mapped by Moretti et al. (2021a) and Freitas et al. (2024).</figcaption></figure>
The validation map (Fig. 3C) reveals effective coverage of key areas of interest, such as the Minas Gerais State, Brazil, where numerous H₂-related landforms have been mapped historically (Moretti et al., 2021a; Freitas et al., 2024). The SCDs identified by the model (green polygons) closely align with expert references (pink polygons), while avoiding non-relevant circular structures such as farm circles, illustrating excellent contextual precision, which is critical for minimizing false detections. However, a few SCDs were not detected, likely due to the low contrast and/or the resolution of the input images. This primary limitation could be easily addressed by enriching the training samples with smaller landforms for which H₂ seeps were confirmed during fieldwork, ensuring more exhaustive detection. Such an optimization would further enhance the reliability of our approach for large-scale H₂ exploration across diverse geological contexts.

Previous efforts in this domain were limited to image-level classification approaches, in particular the recent study by Ginzburg et al. (2025), which trained YOLOv8 models to distinguish between different

types of rounded landforms, including SCDs, impact craters, salt lakes, and karstic depressions, using both multispectral indices and high-resolution Google Maps© imagery. While Ginzburg et al. (2025) demonstrated that a classification model could recognize circular landforms such as SCDs with a 90 % true positive rate over predefined image tiles, their model did not delineate the full extent or outline of the landforms, making it unsuitable for direct cartographic use for integration into geospatial and geostatistical workflows.

In contrast, our semantic segmentation approach provides a pixel-level delineation of each SCD, offering a much finer spatial map. This enables the direct integration of the vector model predictions into GIS, the calculation of precise areas, and the extraction of morphometric descriptors. These spatially explicit data are essential not only for targeting in the field, but also for understanding the spatial distribution of SCDs and their geometry, which could reveal and provide new and complementary information relating to the source of natural hydrogen at depth and preferential migration paths to the surface (Langhi and

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Automatic exploration and statistical characteristics of H₂-predicting SCDs. (A) Prediction of 5,378 SCDs in the Turgay Depression (Kazakhstan). Geological data are from Moseley and Tsimmer (2000). (B) Focus on detected H₂ SCDs in the North Turgay Basin. The map shows the spatial distribution of H₂ seeps (blue polygons) and missed detections (dotted outlines). (C) Prediction of 401 SCDs near Barberton Mountain (South Africa). The geological map is modified from Westraat et al. (2005). (D) Focus on detected H₂ SCDs in South Africa, showing their spatial distribution and missed detections. NNI = Near Neighbor Index.</figcaption></figure>
Strand, 2023; Rezaee, 2021; Larin et al., 2015).

## 3.2. New areas prospected: Kazakhstan and South Africa metrics

With the model fully trained and validated, we applied it to new test territories to evaluate its generalization capacity and practical relevance for H₂ exploration. We used our model in two different countries, Kazakhstan and South Africa. Although the occurrence of H₂ systems has not yet been demonstrated in these countries, several elements suggest considerable potential that is worth targeting and evaluating in the future. For example, various H₂-generating lithologies are present (e.g., ultramafic rocks, Banded Iron Formations, intrusions, coal), that could source H₂ generation through oxidation-reduction and/or radiolysis and/or organic matter cracking. Studies have also reported the presence of H₂ in (i) deep mines (the Witwatersrand basin in South Africa; Lin et al., 2005), (ii) fluid inclusions (Kempirsai Chromite Deposit, Kazakhstan; Melcher et al., 1997) or (iii) SCDs (Barberton Mountain, South Africa; Geymond et al., 2022) even though no field measurements have been conducted yet. In Kazakhstan, we investigated the Turgay Basin located in the northwestern part of the country (Fig. 4A). Here, the model detected and mapped 5378 depressions over an exploration area of 123,527 km² (Fig. 4A and B). In South Africa, near Barberton Mountain, the model detected and mapped 401 depressions over an exploration area of 1,436 km² (Fig. 4C and D). These results demonstrate that the model can be transferred to novel regions and still identify morphologically coherent structures, although the training set primarily reflects Precambrian basement terrains.

This generalization ability is an essential feature of our approach. Although the training data were dominated by SCDs from other well-documented regions, we deliberately included morphologically diverse examples in our samples to encourage model robustness. The structures predicted in Kazakhstan and South Africa were not only numerous, they also exhibited visual and geometric properties consistent with known SCDs, suggesting that the model captures transferable morphological cues. This spatial generalization ability illustrates a fundamental strength of deep neural networks, which is their ability to recombine learned features into new configurations that are not explicitly present in the training data (LeCun et al., 2015). Our model, by learning generic morphological patterns associated with SCDs, can extrapolate and identify potential analogues in entirely new geological contexts with minimal human intervention.

However, we still advise a degree of caution regarding the predicted SCDs in these two regions. First, without field validation and measurements, it is not possible to confirm whether these predictions correspond to actual SCDs linked to H₂ surface seepage or to geomorphological analogues unrelated to migration and seepage processes. While the visual consistency is encouraging, the risk of over-prediction and misidentification, or under-prediction, cannot be excluded for the automatic predictions (Daynac et al., 2024). In fact, in Kazakhstan, the high number of predicted depressions raises questions about possible false positives or about the model's sensitivity to small circular features. In contrast, the lower number of detections in South Africa may reflect either a true spatial lack of SCDs or a limitation of the model in handling specific environmental conditions (e.g., dense vegetation cover). Moreover, the model was configured with a confidence threshold of 70 %, which we consider a reasonable trade-off between inclusivity and accuracy. By retaining predictions with lower confidence levels (e.g., between 70 % and 80 %), the model inevitably increases the number of exploratory candidates, which may include more ambiguous cases. This broadens the detection scope but also increases the overall uncertainty of the maps in new geological contexts, where field validation is still lacking. Therefore, this set of results, though still promising, should be interpreted as exploratory hypotheses rather than conclusions. They provide a shortlist of morphologically anomalous targets, not definitive detections of H₂-related structures. The model's added value lies in its ability to screen large areas and prioritize zones that require subsequent

field validation or measurement.

## 3.3. Post-processing analysis

### 3.3.1. Morphometric parameters

Our approach provides access to statistical analyses of several thousand H₂-predicting rounded landforms on a large scale. It thus opens the way for future exploration of the genetic processes that may link their spatial distribution to underground geology. Furthermore, the morphology of SCDs can also be used as a relevant criterion to detect other similar structures (Moretti et al., 2021a). For example, simple parameters such as depth, width, or their ratio can help differentiate H₂ seeps from other rounded landforms. However, these parameters are constrained by the resolution of the digital elevation model or numerical data. The statistical analyses presented here in section 3.3 should be considered preliminary results; further investigation will be necessary to draw full conclusions, which are beyond the scope of this article.

In this study, we used two main indicators: i) the SCDs area, calculated using the QGIS® field calculator tool and expressed in km², and ii) their aspect ratio, defined as the ratio between the maximum length (L) and width (W) of each structure (Fig. 5). This second parameter, directly related to circularity, varies from 1 (perfectly circular shape) to infinity (very elongated shape). First, area values indicate some variations, ranging from $10^{-3}$ to $10$ km², depending on the localities, both for structures used to train the model and for the newly prospected areas (Fig. 5A). Interestingly, the elongation ratio ranges between 1.22 and 1.58 for structures used in the training dataset, while newly prospected areas show higher values, ranging from 1.44 to 1.88 (Fig. 5B). These differences indicate that H₂ seeps exhibit a wide range of geometric variability. If the predictions are correct (which would need to be verified through field measurements), these results suggest that H₂ seeps could also occur in less circular structures, challenging our current perception of SCDs, according to which circularity is a usual distinguishing feature, except for Carolina bays.

### 3.3.2. Preliminary spatial analysis

A preliminary spatial statistical analysis was performed using the Nearest Neighbor Analysis (NNA) (Clark and Evans, 1954) tool in QGIS®. The NNA produces a dimensionless statistical indicator, obtained by comparing the average distance observed between the nearest neighbors to the average distance expected according to the Poisson probability distribution:

$$ \begin{array}{l} \text{Average distance observed} \\ \text{Average distance expected} \end{array} \qquad (\text{Eq. 3}) $$

A NNA <1 indicates a clustered spatial organization. A proximity index = 1 describes random behavior. A proximity index >1 indicates a dispersed or regular spatial distribution. In addition, the Z-score determines whether the results are significant or due to random chance. The Z-score measures the difference between the average observed distance and each of the SCD distances, using a standardized scale. For each SCD (X), the expected mean distance (μ) and standard deviation (SD) were calculated and used to compute the Z-score:

$$ Z = \frac{(X - \mu)}{SD} \qquad (\text{Eq.4}) $$

A score between -1.96 ≤ Z ≤ +1.96 (95 % confidence interval) does not allow us to reject the hypothesis of a random distribution, while a Z-score >1.96 or < -1.96 reveals a significant spatial organization (clustered or dispersed). The further the Z score is from 0, the greater the significance. In our case, the spatial distribution of the rounded relief's predictive of H₂, as evidenced by their positive nearest neighbor index and negative Z score in both countries (Table 1), demonstrates a certain degree of non-random organization, suggesting control by one or more geological processes.

In Kazakhstan, in the northern basin, H₂ SCDs are located close to the
PRELIMINARY MORPHOMETRIC STATISTICS OF TRAINING/PROSPECTED AREAS

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Morphometric statistics for all data, including training and prospected data. (A) Box plots of H₂-emitting and predicting SCDs showing the area in km. (B) Box plots of H₂-emitting and predicting SCDs showing the elongation ratio.</figcaption></figure>

Ural ophiolite suture zone, making these rocks excellent candidates for H₂ generation at depth. In the southern basin, the occurrence of large oil and gas deposits indicates the presence of organic matter-rich source rocks in the subsurface, supporting the hypothesis that H₂ is generated through organic matter maturation, as previously proposed in a similar

context for the Songliao Basin, China (Horsfield et al., 2022). Regardless of the H₂ source, the Karatau Fault system that corresponds to a crustal-scale strike-slip fault in the South Turgay Basin (Allen et al., 2001) appears to exert primary control over the distribution of the H₂-predicting rounded landforms (Fig. 4A). This major fault system,
<table><caption>Table 1
Preliminary morphometric and statistical analyses of SCDs predicted by the model and presented in Figs. 4 and 5.</caption><thead><tr><th>Area</th><th>Study area (km²)</th><th>Nbr H₂ SCDs</th><th>Mean H₂ SCDs area (km²)</th><th>Median H₂ SCDs area (km²)</th><th>Sum H₂ SCDs area (km²)</th><th>Mean H₂ SDC elongation ratio</th><th>Median H₂ SCD elongation ratio</th><th>Nearest neighbor index</th><th>Z score</th></tr></thead><tbody><tr><td>All Kazakhstan</td><td>123527.13</td><td>5378.00</td><td>0.44</td><td>0.054</td><td>2368.28</td><td>2.51</td><td>1.66</td><td>0.57</td><td>-60.03</td></tr><tr><td>All South Africa</td><td>1436.00</td><td>401.00</td><td>0.10</td><td>0.004</td><td>42.23</td><td>0.80</td><td>1.44</td><td>0.70</td><td>-11.66</td></tr></tbody></table>

which has recorded multiple deformation events over time, may facilitate gas migration and, consequently, leakage toward the surface. This advection of H₂ fluids along faults is a well-documented phenomenon in other locations, such as in Western Australia (Frery et al., 2021), Brazil (Prinzhofer et al., 2024), and the northern Pyrenees (France) (Lefeuvre et al., 2022). However, the lack of data in the northern part of the basin does not allow for discussion of these possible correlations. It would be very interesting to conduct an integrated study of the South Turgay Basin, combining our data on H₂-predicting rounded landforms with all available data from the literature and/or from oil and gas companies, to better understand the gas migration pathways. In other words, this approach would allow for a deeper understanding of H₂-related rounded landform formations and their spatial distribution with geological processes. Such knowledge would enable reverse reasoning: the use of H₂-related rounded landforms could help provide constraints on the geology of the basin.

In South Africa, the H₂-predicting rounded landforms are located in the Karoo Basin overlying the Mpuluzi and Heerenveen batholiths (Fig. 4C). Here, different lithologies could explain H₂ formation, including the well-preserved Archean BIFs in the Barberton greenstone belt (Bontognali et al., 2013) and the Mpuluzi and Heerenveen batholiths through oxidation-reduction and radiolysis processes, respectively. Interestingly, both batholiths intrude the Barberton greenstone belt, generating significant rheological contrasts, also highlighted by major shear zones (Westraat et al., 2005). In that case, we suggest that mineralogical contrasts favor differences in permeability, thereby promoting gas transport towards the surface. Further, given the large spatial extent of the possible H₂ source rocks in the subsurface, one may consider the leakage pattern area relatively limited, especially when compared with the much broader leakage zones documented in Kazakhstan. This apparent inconsistency may result from one or a combination of the following factors: i) heterogeneities in source-rock distribution at depth (e.g., one batolith is more productive than the surrounding rocks); ii) the presence of specific geological features in the Karoo Basin that favor H₂ accumulation in the subsurface and limit leakage in most places. These may include, among others, effective sealing horizons within the basin stratigraphy, and/or the absence of major faults acting as migration pathways toward the surface, as observed in Kazakhstan; iii) significant bacterial activity that consumes H₂ before it can reach the surface.

### 3.4. Estimation of large-scale H₂ flux in these two basins

Our statistical analyses (Table 1) also allow us to estimate large-scale flow quantities based on data from the literature, but this remains highly speculative for two main reasons. First, although our model is trained with SCDs, the objects predicted require field measurements to validate the presence of seeps. Second, the H₂ concentrations in soil vary across different locations and within individual structures due to geological heterogeneity at the regional scale. This variability includes, for instance, the effectiveness of H₂ migration from the deeper-seated source rock and the intensity of H₂ consumption by biological and abiotic chemical processes (e.g., Liu et al., 2025; Wei et al., 2025). As previously mentioned, Moretti et al. (2021b) estimated a flux of 0.03–0.04 m³/day/m² based on long-term monitoring in a SCD structure located in the sedimentary basin in Brazil. Interestingly, this estimated flux does not consider the H₂ consumption by bacterial activity in the soils, which could be half of the real H₂ flux at a greater depth (Myagkiy et al., 2020). These surficial flux estimates neither take into account H₂ adsorption in soil, which may even lower the measured fluxes compared to those at greater depth (Wang et al., 2023, 2024). Based on numerical modeling, Cathles and Prinzhofer (2020) also estimated a flux of 0.05 m³/day/m², which is consistent with field measurements. Assuming a mean H₂ flux of 0.03–0.04 m³/day/m² and average H₂-predicting structure areas of 0.440 km² in Kazakhstan and 0.105 km² in South Africa (Fig. 5A–Table 1), the estimated H₂ flux might reach 1.15–1.53
million $m_{H_2}^3/yr/structure$ in South Africa and 4.82-6.42 million $m_{H_2}^3/yr/structure$ in Kazakhstan. Those estimates correspond to approximately 103.36-137.81 $t_{H_2}/yr/structure$ in South Africa and 433.14-577.52 $t_{H_2}/yr/structure$ in Kazakhstan. At the basin scale, when considering the total area of all SCDs mapped, the fluxes could exceed 41-55 million $t_{H_2}/yr$ in South Africa and 2-3 billion $t_{H_2}/yr$ in Kazakhstan. This means that $H_2$ from part of Kazakhstan could easily meet the growing global demand for hydrogen, if it could be captured. In addition, these values can also be underestimated because our model missed a few detections. Although migration models and the source of $H_2$ are not understood in the crust yet, the $H_2$ flux at the surface implies that the quantities in depth are probably even more important, especially if we consider the possible accumulations. On a global scale, all the $H_2$-related SCDs could emit a phenomenal amount of $H_2$ in the atmosphere. If this assumption is true, the geological $H_2$ will be the game changer for the energy transition.

## 4. Conclusion

Exploration has begun in several countries, including the USA, Australia, France, Mali and Namibia, with initiatives ranging from SCDs mapping to exploratory drilling. These various projects reflect a growing interest in $H_2$ as a clean, sustainable energy source. Our study demonstrates the effectiveness of an automated approach to detecting $H_2$ seeps on a global scale, offering significant advantages over conventional methods that take several days to map a few hundred structures. Our method offers a precise, fast, cost-effective, and scalable alternative for large-scale prospecting. By leveraging satellite imagery, it optimizes $H_2$ detection of seepage targets by selecting areas with confirmed seepage as training samples. This allows for the rapid compilation of high-quality datasets, which can ultimately be integrated with other methods such as radar interferometry to analyze the subsidence of $H_2$-related SCDs as well as geophysical data to better understand the formation and evolution of these structures over time.

At the basin-scale, links between morphological parameters, spatial distribution, and the geological context (e.g., faults, the presence of $H_2$-generating rocks) can be highlighted, offering insights into understanding the $H_2$ migration, this, in turn, could provide major constraints on the $H_2$ system. This innovative approach, therefore, provides valuable support for informed decision-making in the mapping, analysis, and exploration phase of research into this high-potential low-carbon energy source. More broadly, the use of artificial intelligence can be applied to the exploration of other natural resources, for example, to map other natural gases (e.g., abiotic methane) or even to investigate other planets. For example, on Mars, where the Curiosity rover has detected $H_2$ and serpentinization processes occur (e.g., McCollom et al., 2022; Vance et al., 2007).

### CRediT authorship contribution statement

**V. Roche:** Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Funding acquisition, Data curation, Conceptualization. **J. Daynac:** Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis, Data curation. **S. Hesni:** Writing – review & editing, Validation, Software, Formal analysis, Data curation. **U. Geymond:** Writing – review & editing, Validation, Conceptualization. **N. Ginzburg:** Software, Formal analysis, Data curation. **J. Philippon:** Writing – review & editing, Data curation. **I. Moretti:** Writing – review & editing.

### Availability statement

We use a YOLOv8 convolutional neural network (CNN) algorithm, available as open source (Jocher et al., 2023). The locations of Google Earth® images used to train the model from Australia, Namibia, Brazil, Russia, and the United States of America are given in the manuscript, in

the method section.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This research was supported by Pulsar Programm of Région Pays de la Loire. We thank the LPG for providing access to a supercomputer to run the models. We also thank both reviewers for their comments, which helped improve the paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.marpetgeo.2025.107617.

### Data availability

Data are available in the manuscript.

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