An explainable semi-supervised deep learning framework for mineral prospectivity mapping: DEEP-SEAM v1.0

Luo, Zijing; Farahbakhsh, Ehsan; Hore, Stephen; Müller, R. Dietmar

doi:https://doi.org/10.5194/egusphere-2025-3283

Preprints

https://doi.org/10.5194/egusphere-2025-3283

Preprints

15 Sep 2025

| 15 Sep 2025

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

An explainable semi-supervised deep learning framework for mineral prospectivity mapping: DEEP-SEAM v1.0

Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, and R. Dietmar Müller

Abstract. The clean energy transition demands a significant increase in exploration for critical minerals, particularly rare earth elements (REEs), beyond the well-explored surface deposits. Discovery rates have been declining for decades, escalating the need for new exploration methodologies. Deep learning (DL) utilizes multi-layer neural networks to automatically model high-level abstractions of data, extracting information relevant to the target task, thereby positioning itself as a potentially powerful tool for mineral prediction. But the non-linear and highly heterogeneous characteristics of complex exploration data sets paired with sparse, imbalanced training data and a lack of interpretability represent significant challenges. In this study, we have developed DEEP-SEAM v1.0, a novel explainable semi-supervised DL framework for prospectivity mapping of REE mineralisation in Northern Curnamona Province, South Australia. This framework proposes a comprehensive data preprocessing pipeline and introduces a semi-supervised anomaly detection DL model, termed the Deviation Network (DevNet). DevNet leverages a limited number of positive samples alongside a large number of unlabelled samples to effectively establish the mapping between multi-source exploration data and mineralisation probability. The results indicate that prospective mineralisation areas exhibit a strong spatial coupling with known REE deposits, with high-probability mineralisation areas primarily concentrated in faulted regions, felsic granites, and Mesoproterozoic strata. To address concerns about the poor interpretability of DL models, we incorporate a post-hoc model interpretation technique known as the SHapley Additive exPlanations method. The method facilitates an improved understanding of the decision-making mechanisms and logic underlying DevNet. By comparing DEEP-SEAM’s decisions with our understanding of mineral systems, we not only enhance the model’s transparency and interpretability but also strengthen the reliability and credibility of the predicted prospective mineralisation areas.

Received: 09 Jul 2025 – Discussion started: 15 Sep 2025

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Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, and R. Dietmar Müller

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RC1:
'Comment on egusphere-2025-3283', Anonymous Referee #1, 10 Oct 2025 reply
The manuscript by Luo et at al. entitled “An explainable semi-supervised deep learning framework for mineral prospectivity mapping: DEEP-SEAM v1.0” (egusphere-2025-3283) describes a novel explainable semi-supervised deep learning framework for prospectivity mapping (MPM) of REE mineralisation in Northern Curnamona Province, South Australia.
The newly developed DEEP-SEAM v1.0 tool and associated case study should be of interest to readers of EGUsphere and the broader community of (mathematical) geoscientists but the manuscript has some shortcomings that I believe will require major revisions before it can be considered for publication.
My main issues are as follows:
Introduction
The Introduction requires significant shortening by at least 33%. In its current format, it is too ‘wordy’ and presents a somewhat cumbersome read in that it contains too much technical detail, which would be better suited to the Materials and Methods section.

Geological Setting and REE Mineralisation
In my opinion, this section is the weakest part of your manuscript and one that requires significant strengthening. I understand that the focus of your study is on technology development and demonstrating the capabilities of the new newly developed DEEP-SEAM v1.0 tool. However, presenting a stronger more comprehensive targeting model would help to better position your manuscript and tool as well as generate greater impact and interest.

I strongly recommend the following: (1) Restructure and expand your Table 1 to include additional information relating to host rocks (types and ages), host structures and ore controls, mineralisation styles, ore and gangue mineralogy, alteration types and assemblages, mineralisation ages, grade/tonnage information, etc. (2) The restructuring and expansion of Table 1 will go a long way to better demonstrate that the REE and U(-REE) and Cu(-REE) occurrences your work is based on form part of a coherent group of mineral deposit (i.e., part of a single mineral system). As a reader who is unfamiliar with these occurrences your manuscript does little to convince me of their similar origin. This is a critical issue as mixing mineral systems in MPM may give you garbage results. (3) Combine or link the improved Table 1 with Table 2 in a way that you clearly demonstrate which data map which expressions of the targeted mineral system. (4) The study area is relatively small, in particular for an MPM study. Why did you not include the entire Mt Babbage and Mt Painter inliers, or at least the parts that are well exposed? That's not clear to me. Superimposing artificial, man-made boundaries on mineral systems should be avoided at all costs. At the very least, a clear explanation is required here as to the design of and rationale for the study area and its boundaries. Ideally, you should expand the study area to include the entire prospective tract. This should be easily possible given the great open-source data provided by the geological surveys of South Australia and New South Wales.

Materials and Methods
Here you briefly refer to a “conceptual mineralisation model”, which, however, is nowhere presented in the manuscript. This point links back to my above comments regarding the need for including a comprehensive targeting model that links mineralising processes to mappable ingredients of the REE-U-Cu system and the datasets available for mapping the system footprints. See paper by McCuaig et al. (2010) for more information (https://www.sciencedirect.com/science/article/pii/S0169136810000612)

How did you determine buffer zone sizes?

Results and Discussion
Parts of the Discussion (highlighted in the attached commented PDF) deal with geology and methodology. These parts should be presented in the relevant Geology and Methods sections.

Edge effects in Figure 10 (p. 29) must be discussed.

Additional minor comments are provided in the attached PDF.

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Citation: https://doi.org/10.5194/egusphere-2025-3283-RC1
CEC1:
'Comment on egusphere-2025-3283 - No compliance with the policy of the journal', Juan Antonio Añel, 11 Oct 2025 reply

Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".

https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
In your "Code and data availability" section (and in the corresponding methods section in the manuscript) you link sites to access the data that you use in your work, which are not suitable repositories according to our policy (for example the South Australian Resources Information Gateway). Therefore, the current situation with your manuscript is irregular. Please, publish all the data that you have used to produce your manuscript (input and output) in one of the appropriate repositories according to our policy, and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Also, you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the information of the new repositories.
I must note that if you do not fix this problem, we cannot accept your manuscript for publication in our journal.
Juan A. Añel

Geosci. Model Dev. Executive Editor

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Citation: https://doi.org/10.5194/egusphere-2025-3283-CEC1
- AC1: 'Reply on CEC1', Zijing Luo, 13 Oct 2025 reply
  
  Dear Editor Añel,
  Thank you for your feedback regarding our manuscript's compliance with the Code and Data Policy.
  I would like to clarify that all input and output data necessary to reproduce our study have been deposited in Zenodo. The repository can be accessed at: https://doi.org/10.5281/zenodo.17098677
  
  The repository contains:
  
  1. Input_Data_Layers/ - All processed and clipped data layers used as model inputs (derived from the South Australian Resources Information Gateway)
  
  2. Output_Features_Generated/ - All model outputs and results
  
  3. Code/ - Complete code to reproduce the results
  While the original raw data were downloaded from the South Australian Resources Information Gateway (as cited in our manuscript), all processed input data specific to our study area (e.g., clipped and projected using ArcGIS), along with all model outputs, are available in the above repository. Users can directly run our code using the provided input data to reproduce our results without needing to access the original data sources.
  We acknowledge that our original "Code and Data Availability" section may not have clearly communicated this. We will revise this section in our manuscript to explicitly state the repository information and clarify the relationship between the original data sources and our processed datasets.
  Please let us know if any additional information or modifications are needed.
  Best regards,
  
  Zijing
  
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2025-3283-AC1
RC2: 'Comment on egusphere-2025-3283', Anonymous Referee #2, 12 Oct 2025 reply

This manuscript describes a generalisable method for using a variety of digital representations of geology to locate mineralisation. The application is to hydrothermal REE deposits in the Curnamona Province, Australia. The method uses deep learning for prediction to locate unknown mineralisation, and Shapley values to explain which input features contribute most to the predciction.
The submission is well-written and structured. The figures are a decent quality.
There are some issues that need rectifying before this work can be published.
Generalisability of the method. The authors can provide some discussion to how DEEP-SEAM can be used for other mineralisation types. This will help the manuscript fit within the scope of the journal.
Data reduction of geochemical assay. RPCA is used, and is a better choice than PCA. However, the results in Figure A1 tell me that RPCA is not that effective for data reduction. Six principal components are required to explain > 70% of the variance. I wonder whether a non-linear method such as UMAP may be a better option. UMAP can act like an auto-encoder.
Abundance of magnetic data derivatives versus other data. The number of magnetic layers used indicates the authors think that REE deposits are best imaged using magnetics, however this is not well-explained in the paper. What is the 2d correlation between these layers – i.e. are the same patterns presented in each, which can add bias?
Further, the justification of the various features with respect to REE mineralisation isn’t well established. Aside from the comment regarding magnetics, an example is using a DEM. More detail is needed on which “geological processes and environmental conditions influence mineralisation” are revealed by DEM. I’m assuming the DEM represents erosion and rock competency, but you may be thinking of others. This is important when justifying feature engineering (L258-259).
Each of the geophysical methods image different depths of the crust. This is partly due to the physics of the method itself, but mostly due to station spacing and line spacing. Wider line spacing means deeper minimum depths of imaging. Please explain this in context with the geophysical survey parameters.
The most interesting part of the study is finding areas on unknown mineralisation. However, there is limited analysis of the newly identified prospective areas. Examples are location ‘X’ (SW of E), the area extending SW of ‘E’ and a blob SE of ‘F’. Why would anyone go to these locations to acquire a tenement? Some geological analysis is needed.
The discussion needs to explore geological reasons how shap values align with geological understanding. Explore the mechanisms – what geological features contributing to mineralisation are magnetic? Why are others radioactive (e.g. lin469 to 472)? Are there any that are both magnetic and radioactive?
The results section opens with statements describing the interpretation of results for the results have been presented. E.g. “This study efficiently integrates multi-source exploration data and transforms them into a mineral prospectivity map utilising a semi-supervised DL model. Additionally, it provides interpretability for understanding the model’s prediction process”
and
“The DL-based mineral prospectivity model, DevNet, trained with optimised parameter configurations, effectively captures the complex mapping relationships between multidimensional features and mineralisation probability. The mineralisation probability is obtained by normalizing the anomaly scores output (the original value of the model output) by DevNet.”
The readers need to make up their minds by viewing the results first before the authors can make these statements (which are really discussion items).

I have minor comments listed below.
Intro
Not sure about anomaly detection being relevant to MPM. AD looks at a univariate signal, while MPM is a data fusion exercise. Anomalous prospectivity values - Just needs a clearer explanantion
Do occurrences, prospects and deposits have the same weight within your model?
Negative samples are drawn > 5km from known deposits. Establish that this is reasonable (all deposits are known? Then why do prospectivity analysis?)
L 439 Success rate high accuracy – against what benchmarks/other studies?
You use features which image the subsurface (magnetics and gravity) and those limited to the surface (DEM, ASTER and, at the scale of this study, radiometrics). It would be interesting to look at the SHAP values and see whether surface versus deeper signatures are more useful.
Figure 10. Std based features have border effects. Does this impact the model training and degrade results? Can these border effects be removed?

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Citation: https://doi.org/10.5194/egusphere-2025-3283-RC2

Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, and R. Dietmar Müller

Data sets

An explainable semi-supervised deep learning framework for mineral prospectivity mapping: DEEP-SEAM v1.0 Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, R. Dietmar Müller https://doi.org/10.5281/zenodo.17098677

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Interactive computing environment

Zijing Luo, Ehsan Farahbakhsh, Stephen Hore, and R. Dietmar Müller

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By combining multi-source data with advanced processing techniques, our deep learning model effectively identifies mineralisation patterns despite extremely limited deposit samples, analyses data and validates the geological relevance of its decisions through explanatory analysis, providing a universally reliable solution for artificial intelligence-assisted mineral prospectivity mapping.


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