Remote-sensing mineral exploration is critical for identifying economically viable mineral deposits, yet it poses significant challenges for multimodal large language models (MLLMs). These include limitations in domain-specific geological knowledge and difficulties in reasoning across multiple remote-sensing images, further exacerbating long-context issues. To address these, we present MineAgent, a modular framework leveraging hierarchical judging and decision-making modules to improve multi-image reasoning and spatial-spectral integration. Complementing this, we propose MineBench, a benchmark specific for evaluating MLLMs in domain-specific mineral exploration tasks using geological and hyperspectral data. Extensive experiments demonstrate the effectiveness of MineAgent, highlighting its potential to advance MLLMs in remote-sensing mineral exploration.

In various geosciences branches, including mineral exploration, geometallurgical characterization on established mining operations, and remote sensing, the regionalized input variables are spatially well-sampled across the domain of interest, limiting the scope of spatial uncertainty quantification procedures. In turn, response outcomes such as the mineral potential in a given region, mining throughput, metallurgical recovery, or in-situ estimations from remote satellite imagery, are usually modeled from a much-restricted subset of testing samples, collected at certain locations due to accessibility restrictions and the high acquisition costs. Our limited understanding of these functions, in terms of the multi-dimensional complexity of causalities and unnoticed dependencies on inaccessible inputs, may lead to observing changes in such functions based on their geographical location. Pooling together different response functions across the domain is critical to correctly predict outcome responses, the uncertainty associated with these inferred values, and the significance of inputs in such predictions at unexplored areas. This paper introduces the notion of a dual random field (dRF), where the response function itself is considered a regionalized variable. In this way, different established response models across the geographic domain can be considered as observations of a dRF realization, enabling the spatial inference and uncertainty assessment of both response models and their predictions. We explain how dRFs inherit all the properties from classical random fields, allowing the use of standard Gaussian simulation procedures to simulate them. These models are combined to obtain a mineral potential response, providing an example of how to rigorously integrate machine learning approaches with geostatistics.

Optimal Bayesian decision making on what geoscientific data to acquire requires stating a prior model of uncertainty. Data acquisition is then optimized by reducing uncertainty on some property of interest maximally, and on average. In the context of exploration, very few, sometimes no data at all, is available prior to data acquisition planning. The prior model therefore needs to include human interpretations on the nature of spatial variability, or on analogue data deemed relevant for the area being explored. In mineral exploration, for example, humans may rely on conceptual models on the genesis of the mineralization to define multiple hypotheses, each representing a specific spatial variability of mineralization. More often than not, after the data is acquired, all of the stated hypotheses may be proven incorrect, i.e. falsified, hence prior hypotheses need to be revised, or additional hypotheses generated. Planning data acquisition under wrong geological priors is likely to be inefficient since the estimated uncertainty on the target property is incorrect, hence uncertainty may not be reduced at all. In this paper, we develop an intelligent agent based on partially observable Markov decision processes that plans optimally in the case of multiple geological or geoscientific hypotheses on the nature of spatial variability. Additionally, the artificial intelligence is equipped with a method that allows detecting, early on, whether the human stated hypotheses are incorrect, thereby saving considerable expense in data acquisition. Our approach is tested on a sediment-hosted copper deposit, and the algorithm presented has aided in the characterization of an ultra high-grade deposit in Zambia in 2023.

The platform uses geospatial data and satellite imagery to provide data-based applications for mineral exploration and discovery and promises to increase hypothesis testing and the speed of the exploration lifecycle. "Traditionally, remote sensing is carried out by specialists (remote sensing geologists) on behalf of the mineral exploration team. Although they still have a role in supporting the process, the Descartes Labs platform puts the technology into the hands of the exploration geologists who know the project areas the best. By leveraging the data obtained from satellite and airborne imagery, they can accelerate their hypothesis formulation and exploration strategies to find new deposits," James Orsulak, senior director of business and sales at Descartes Labs, told MINING.COM. MDC: Your platform puts emphasis on the data refinery.

Toronto, Ontario--(Newsfile Corp. - September 16, 2020) - GoldSpot Discoveries Corp. (TSXV: SPOT) (the "Company" or "GoldSpot") has been engaged by Cerrado Gold Inc. ("Cerrado") to apply machine learning and its proprietary data science expertise to identify new exploration targets on Cerrado's Minera Don Nicolas (MDN) project, located in Santa Cruz, Argentina. In its analysis, GoldSpot will work with Cerrado's technical team to integrate and analyze geological and remote sensing data available in the area. The process will explore the potential for gold mineralization within the MDN properties, to produce GoldSpot Smart Targets which fuse geoscience knowledge with data science insights. "Minera Don Nicolas is in the mineral and data rich Deseado Massif, an area where GoldSpot is having significant success, particularly at Yamana Gold's Cerro Moro project. MDN has robust property-wide datasets and we look forward to supporting Cerrado's technical team and advancing exploration efforts. The project has significant potential with a land package of more than 273,000 hectares," stated Denis Laviolette, Executive Chairman and President of GoldSpot Discoveries.

Machine learning and artificial intelligence are becoming key components of mineral exploration programs as companies set exploration targets. Machine learning and artificial intelligence (AI) have the ability to solve two of the mining industry's biggest challenges: rising exploration costs and a lack of new discoveries. After a heavy downturn in the past few years, the mining and mineral exploration sector is finally starting to recover, but deep challenges remain. In an industry that thrives on new discoveries, today's resource companies are finding it harder and more expensive to locate new deposits. Gold provides one of the greatest examples of this dearth of new discoveries in the face of rising exploration costs.

Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly proving their value across many industries. Today's infographic comes from GoldSpot Discoveries, and it shows that when this tech is applied to massive geological data sets, that there is growing potential to unlock the next wave of mineral discoveries. Discovering new sources of minerals, such as copper, gold, or even cobalt, can be notoriously difficult but also very rewarding. According to Goldspot, the chance of finding a new deposit is around 0.5%, with odds improving to 5% if exploration takes place near a known resource. On the whole, mineral exploration has not been a winning prospect if you compare the total dollar spend and the actual value of the resulting discoveries.

EARTH AI are helping mineral explorers identify promising areas. They do this by analysing data from multiple sources and using a machine learning algorithm to identify areas where minerals are likely to be found.

It could be the moment scientists accidentally put themselves out of a job. Physicists have revealed artificial intelligence software was used to run a complex experiment. The experiment, developed by physicists from ANU and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. The experiment created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

ABSTRACT Prospector is a computer consultant system intended to aid geologists in evaluating the favorability of an exploration site or region for occurrences of ore deposits of particular types. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof. We describe the form of models in Prospector, focussing on inference networks of geological assertions and the Bayesian propagation formalism used to represent the judgmental reasoning process of the economic geologist who serves as model designer. Following the initial design of a model, simple performance evaluation techniques are used to assess the extent to which the performance of the model reflects faithfully the intent of the model designer. These results identify specific portions of the model that might benefit from "fine tuning", and establish priorities for such revisions. This description of the Prospector system and the model design process serves to illustrate the process of transferring human expertise about a subjective domain into a mechanical realization. I. INTRODUCTION In an increasingly complex and specialized world, human expertise about diverse subjects spanning scientific, economic, social, and political issues plays an increasingly important role in the functioning of all kinds of organizations. Although computers have become indispensable tools in many endeavors, we continue to rely heavily on the human expert's ability to identify and synthesize diverse factors, to form judgments, evaluate alternatives, and make decisions -- in sum, to apply his or her years of experience to the problem at hand. This is especially valid with regard to domains that are not easily amenable to precise scientific formulations, i.e., to domains in which experience and subjective judgment plays a major role.