A Multimodal Learning Framework for Comprehensive 3D Mineral Prospectivity Modeling with Jointly Learned Structure-Fluid Relationships

This study presents a novel multimodal fusion model for three-dimensional mineral prospectivity mapping (3D MPM), effectively integrating structural and fluid information through a deep network architecture. Leveraging Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP), the model employs canonical correlation analysis (CCA) to align and fuse multimodal features. Rigorous evaluation on the Jiaojia gold deposit dataset demonstrates the model's superior performance in distinguishing ore-bearing instances and predicting mineral prospectivity, outperforming other models in result analyses. Ablation studies further reveal the benefits of joint feature utilization and CCA incorporation. This research not only advances mineral prospectivity modeling but also highlights the pivotal role of data integration and feature alignment for enhanced exploration decision-making

Quantitative Optimisation of Drilling for Brownfields Mineral Exploration

This research presents a novel optimisation framework for brownfields exploration drilling. The proposed optimisation methodology has been developed applying geostatistical methods and modern portfolio theory. The use of conditional simulations ensures that geological uncertainty is taken into account, and the application of Markowitz portfolio theory makes drilling funds allocation optimal. The proposed method closes the gap in current research by incorporating the inherent geological uncertainty of an exploration target and mineral economics

FUCOM-MOORA and FUCOM-MOOSRA: new MCDM-based knowledge-driven procedures for mineral potential mapping in greenfields

AbstractIn this study, we present the application of two novel hybrid multiple-criteria decision-making (MCDM) techniques in the mineral potential mapping (MPM), namely FUCOM-MOORA and FUCOM-MOOSRA, as robust computational frameworks for MPM. These were applied to a set of exploration targeting criteria of skarn. The multi-objective optimization method on the basis of ratio analysis (MOORA) and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA) approaches are used to prioritize and rank individual cells. What makes MOORA and MOOSRA more reliable compared to many other methods is the fact that the optimizations procedure is applied to calculate the prospectivity score of individual unit cells. This reduces the uncertainty stemming from erroneous mathematical calculations. The full consistency method (FUCOM), on the other hand, is useful for assigning weights to the spatial proxies. The FUCOM method, as a pairwise comparison method, reduces a large number of pairwise comparisons of similar and popular approaches such as analytic hierarchy process (AHP) with $$n\left( {n - 1} \right)/2$$ n n - 1 / 2 and the best–worst method (BWM) with $$2n - 3$$ 2 n - 3 number of pairwise comparisons with $$n - 1$$ n - 1 which leads to a less time-consuming and more consistent performance compared with AHP and BWM. These were applied to a set of exploration targeting criteria of skarn iron deposits from Central Iran. Two potential maps were retrieved from the procedures applied, the comparison of which using correct classification rates and field checks revealed the superiority of FUCOM-MOOSRA over the FUCOM-MOORA

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran.

Peer review journal article. Geology.Diverse deposit-types or mineral systems form by diverse geological processes, so translation of knowledge about the controls of mineralization acquired from the 4D geological modeling into 2D spatial predictor maps is a major challenge for prospectivity analysis. In this regard, mathematical functions have been used to model the conceptual or perceived spatial relationships between geological variables and targeted type or system of mineralization. In this paper, due to the different models of spatial relationships between predictors and mineral deposits, we investigated the performance of different fuzzification functions to quantify the relationships. We demonstrated that various types of relationships between exploration features and a mineralization-type sought could be quantified using different fuzzification functions for prospectivity analysis. We illustrated the process of the prospectivity analysis by using a data set of orogenic gold deposits in Saqez-Sardasht Goldfield, Iran. Prospectivity modeling of orogenic gold mineralization in the study area showed that the NE-SW trending targets have priority for further prospecting of the deposits

Improving spatial prioritisation for remote marine regions: optimising biodiversity conservation and sustainable development trade-offs

Creating large conservation zones in remote areas, with less intense stakeholder overlap and limited environmental information, requires periodic review to ensure zonation mitigates primary threats and fill gaps in representation, while achieving conservation targets. Follow-up reviews can utilise improved methods and data, potentially identifying new planning options yielding a desirable balance between stakeholder interests. This research explored a marine zoning system in north-west Australia–abiodiverse area with poorly documented biota. Although remote, it is economically significant (i.e. petroleum extraction and fishing). Stakeholder engagement was used to source the best available biodiversity and socio-economic data and advanced spatial analyses produced 765 high resolution data layers, including 674 species distributions representing 119 families. Gap analysis revealed the current proposed zoning system as inadequate, with 98.2% of species below the Convention on Biological Diversity 10% representation targets. A systematic conservation planning algorithm Maxan provided zoning options to meet representation targets while balancing this with industry interests. Resulting scenarios revealed that conservation targets could be met with minimal impacts on petroleum and fishing industries, with estimated losses of 4.9% and 7.2% respectively. The approach addressed important knowledge gaps and provided a powerful and transparent method to reconcile industry interests with marine conservation

The efficiency of logistic function and prediction-area plot in prospectivity analysis of mineral deposits.

Mineral Prospectivity Conference, France.In this work, we present logistic-based mineral prospectivity mapping (MPM) methods concerning with assigning weights of exploration indicators, without contribution of training sites as in supervised MPM and without using user-judged weights as in unsupervised MPM, to modulate the problems of stochastic and systemic errors. In addition, we discuss the ability of prediction-area plot as a tool to assess and compare evidential layers and prospectivity models

Numerical Modeling of Mineralizing Processes During the Formation of the Yangzhuang Kiruna-Type Iron Deposit, Middle and Lower Yangtze River Metallogenic Belt, China: Implications for the Genesis and Longevity of Kiruna-Type Iron Oxide-Apatite Systems

The Yangzhuang iron deposit is a Kiruna-type iron oxide-apatite (IOA) deposit within the Ningwu mining district of the Middle and Lower Yangtze River Metallogenic Belt (MLYRMB), China. This study applies a numerical modeling approach to identify the key processes associated with the formation of the deposit that cannot be easily identified using traditional analytical approaches, including the duration of the mineralizing process and the genesis of iron orebodies within intrusions associated with the deposit. This approach highlights the practical value of numerical modeling in quantitatively analyzing mineralizing processes during the formation of mineral deposits and assesses how these methods can be used in future geological research. Our numerical model links heat transfer, pressure, fluid flow, chemical reactions, and the movement of ore-forming material. Results show that temperature anomaly and structure (occurrence of the contact of intrusion and the Triassic Xujiashan group) are two key factors controlling the formation of the Yangzhuang deposit. This modeling also indicates that the formation of the Yangzhuang deposit only took some 8000 years, a reaction that is likely to be controlled by temperature and diffusion rates within the system. The dynamic changes of temperature and the distribution of mineralization also indicate that the orebodies located inside the intrusions most likely formed after magma ascent rather than representing blocks of existing mineralization that descended into the magma as a result of stoping or other similar processes. All these data form the basis for future research into the forming processes of Kiruna-type IOA systems as well as magmatic–hydrothermal systems more broadly, including providing useful insights for future exploration for these systems. The simulation approach used in this study has several limitations, such as oversimplified chemical reactions, uncertainty of pre-metallogenic conditions and limitation of 2D model. Future development into both theories and methods will definitely improve the practical significance of numerical simulation of ore-forming processes and provide quantitative results for more geological issues

A Comprehensive Survey on Rare Event Prediction

Rare event prediction involves identifying and forecasting events with a low probability using machine learning and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the machine learning pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and machine learning. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.Comment: 44 page

Unconformity-type uranium systems: a comparative review and predictive modelling of critical genetic factors

A review of descriptive and genetic models is presented for unconformity-type uranium deposits with particular attention given to spatial representations of key process components of the mineralising system and their mappable expressions. This information formed the basis for the construction of mineral potential models for the world's premier unconformity-style uranium provinces, the Athabasca Basin in Saskatchewan, Canada (>650,000 t U3 O8), and the NW McArthur Basin in the Northern Territory, Australia (>450,000 t U3 O8). A novel set of ‘edge’ detection routines was used to identify high-contrast zones in gridded geophysical data in support of the mineral potential modelling. This approach to geophysical data processing and interpretation offers a virtually unbiased means of detecting potential basement structures under cover and at a range of scales. Fuzzy logic mineral potential mapping was demonstrated to be a useful tool for delineating areas that have high potential for hosting economic uranium concentrations, utilising all knowledge and incorporating all relevant spatial data available for the project area. The resulting models not only effectively ‘rediscover’ the known uranium mineralisation but also highlight several other areas containing all of the mappable components deemed critical for the accumulation of economic uranium deposits. The intelligence amplification approach to mineral potential modelling presented herein is an example of augmenting expert-driven conceptual targeting with the powerful logic and rationality of modern computing. The result is a targeting tool that captures the current status quo of geospatial and exploration information and conceptual knowledge pertaining to unconformity-type uranium systems. Importantly, the tool can be readily updated once new information or knowledge comes to hand. As with every targeting tool, these models should not be utilised in isolation, but as one of several inputs informing exploration decision-making. Nor should they be regarded as ‘treasure maps’, but rather as pointers towards areas of high potential that are worthy of further investigation