Geological Input Research Articles

Strength of viscous subduction interfaces: A global compilation

The long- and short-term dynamics of subduction zones are strongly affected by interface rheology. Current estimates of interface viscosity are based on endmember flow laws or simple mixing models, but the variation of possible materials on the subduction interface theoretically permits viscosity variations of up to five orders of magnitude. To better constrain the range of strength along deep/viscous subduction interfaces, we compiled a global database of rock types and block-and-matrix distributions in exhumed deep subduction interface mélange shear zones. We applied numerical shear experiments to each shear zone to quantify their bulk shear strengths/viscosities. Our results show that natural subduction shear zones have viscosities ranging from ∼1018 to 1020 Pa⋅s and shear strengths of ∼1−100 MPa. The variation among them is dominantly controlled by temperature, but even for shear zones deformed at the same temperature, variations of up to a factor of 50 are observed. These variations are attributed to differences in matrix composition, shear zone width, and block distributions. Our results are consistent with paleopiezometric and force-balance−derived estimates of interface strength, and they confirm the importance of variation in geological inputs and their spatial distribution along the interface for subduction dynamics.

Geologic Input Databases for the 2025 Puerto Rico—U.S. Virgin Islands National Seismic Hazard Model Update: Crustal Faults Component

Abstract The last National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI) was published in 2003. In advance of the 2025 PRVI NSHM update, we created three geologic input databases to summarize new onshore and offshore fault source information in the northern Caribbean region between 62°–70° W and 16°–21° N. These databases, of fault sections, fault-zone polygons, and geologic estimates of fault activity (fault-slip rate and earthquake recurrence intervals) at specific sites, document updates to fault parameters used in prior seismic hazard models in PRVI. Fault sources were reviewed from published studies since 2003, which document substantial changes to the understanding of fault location, geometry, or activity. New fault section sources were added for features that meet the criteria of (1) length ≥7 km, (2) unequivocal evidence of recurrent tectonic Quaternary activity, and (3) documentation that is publicly available in a peer-reviewed source. In addition, we revised several broad areal sources, such as the Mona and Anegada extensional zones. The 2003 model included three fault sections and two fault-zone polygons (areal sources). These databases include 35 fault sections, 6 fault-zone polygons, and 51 earthquake geology sites. To characterize fault activity rates, slip-rate bins were assigned based on landscape expression and paleoseismic trench observations for faults without published slip-rate sites. Additional fault sources were evaluated but not included in these databases due to a lack of published information about fault location, geometry, or recurrent Quaternary activity. The PRVI NSHM 2025 geologic input databases describe crustal faulting; the geometries and coupling of Puerto Rico subduction zone and Muertos Trough models are considered in a separate database. Updates to the fault sections, fault-zone polygons, and earthquake geology databases can help inform the location and recurrence rate of damaging earthquakes in the PRVI NSHM implementation.

Predict: Assessing the Seismic Response in the city of Rome, Part 1. New Data for a Geologic Overview

This work presents the preliminary results of the stratigraphic, paleomagnetic, micropaleontologic and geochronologic investigations carried out on the cores of three boreholes performed in the historic center of Rome within the Predict Project, aimed at evaluating the seismic response within the City through the 3D modeling of the subsoil. Moreover, we have integrated the investigations on the cores of four previously performed boreholes and we use this larger dataset to provide an objective element for the interpretation and validation of a large databank of paper stratigraphies of boreholes carried out for civil engineering purposes in the Roman area. The new data are presented within an exhaustive review of state of the art on the scientific knowledge on the geology of Rome, aimed at providing an updated background for the Quaternary scientists, seismologists, engineers and professional technicians operating in this region. Within this framework, we provide a detailed reconstruction of the chronostratigraphic setting in central Rome, highlighting a coherent picture within the glacio‐eustatic control on the sedimentary processes and providing the background geological input data for the creation of a geo‐database in a dynamic GIS environment, which is the subject of a forthcoming sister‐paper.

GIS analysis for the selection of optimal sites for mine water geothermal energy application: a case study of Scotland's mining regions

Water within flooded coal mines can be abstracted via boreholes or shafts, where heat can be extracted from (or rejected to) it to satisfy surface heating (or cooling) demands. Following use, water can be reinjected into the mine workings or discharged to a surface water receptor. Four criteria have been applied, using ArcGIS, to datasets describing mine workings and mine water below the Midland Valley of Scotland to provide an initial screening tool for suitability for mine water geothermal energy exploitation. The criteria are: (i) the presence of two or more worked coal seams below site; (ii) the absence of potentially unstable shallow (<30 m) workings; (iii) a depth to mine water piezometric head of less than 60 m; and (iv) a depth of coal mine workings of less than 250 m. The result is the Mine Water Geothermal Resource Atlas for Scotland (MiRAS). MiRAS suggests that a total area of 370 km 2 is ‘optimal’ for mine water geothermal development across 19 local authority areas, with greatest coverage in North Lanarkshire. This result should not be taken to suggest that mine water geothermal potential does not exist at locations outside the identified ‘optimal’ footprint. The MiRAS does not preclude the necessity for specialist engineering and geological input during a full feasibility study.

Comparing shallow landslide susceptibility maps in Northeastern Türkiye (Beşikdüzü, Trabzon): a multivariate statistical, machine learning, and physical data-based analysis

This study endeavors to assess and compare the efficacy of various modeling approaches, including statistical, machine learning, and physical-based models, in the creation of shallow landslide susceptibility maps within the Besikduzu district of Trabzon province, situated in the Black Sea Region of Türkiye. The landslide inventory data, spanning from 2000 to 2018, was acquired through meticulous field surveys and analysis of Google Earth satellite imagery. Key topographic and geologic input parameters, such as slope, aspect, topographic wetness index, stream power index, plan and profile curvature, and geologic units, were extracted from a high-resolution 10 m spatial DEM (Digital Elevation Model) and a 1:25,000 scaled digital geology map, respectively. Additionally, soil unit weight and shear strength parameters, critical for the physical-based model, were determined through field samples. To evaluate landslide susceptibility, logistic regression, random forest, and Shalstab were employed as the chosen methods. The accuracy of susceptibility maps generated by each method was assessed using the area under the curve method, yielding impressive values of 0.99 for the random forest model, 0.97 for the logistic regression model, and 0.93 for the Shalstab model. These results underscore the robust performance of all three methods, suggesting their applicability for generating shallow landslide susceptibility maps not only in the Black Sea Region but also in analogous areas with similar geological characteristics.

ASSESSMENT OF ROCK MASS QUALITY AND ROCKFALL POTENTIAL EVALUATION FOR RECLAMATION OF A QUARRY

In order to make an informed decision about implementing sustainable and efficient development during land reclamation in an area deemed geohazard-prone, thorough geological input is necessary. Quarry areas are vulnerable to slope failure and rockfalls, making it imperative to evaluate rock slopes for quarry reclamation. For this reason, researchers in Kinta Valley, Malaysia, set out to evaluate the slopes GG1, GG2, and GG3 at a defunct quarry. To evaluate the rock mass classification, the Rock Mass Rating (RMR) and Slope Mass Rating (SMR) systems were utilized, and the analyzing rock block's trajectory was using a rockfall analysis. The kinematic stability analysis was also performed to identify possible failure mechanisms. In order to assess the site's suitability for urban development, the rockfall scenarios were conducted. The SMR scaled from moderate to very good, while the RMR ranked the rock mass quality as good to very good. For all three of the slopes analyzed, the kinematic stability analysis pointed to the possibility of various failures (toppling, planar and wedge). According to the rockfall trajectory analysis, the rock block could roll as far as 5 metres from the base of the slope. For this reason, the study advised creating a buffer zone of 20 metres or more away from the rock slope as a means of protection against the geohazard of rockfall.

A heuristic method for production scheduling of an open pit mining operation

ABSTRACT A mathematical model for production scheduling of open pit mines maximises the net present value and satisfies the reserves, pit slope angle, and production capacity constraints. A solution to this model aims to deliver an extraction sequence and the movement of materials across various stages within the operation over a defined planning horizon. However, given that the mineral reserves delineated into thousands of mining blocks form the geological input, the model falls in the category of large-scale optimisation problems, i.e. it is computationally intractable, and exact methods cannot solve realistic scenarios of the problem. Therefore, this paper contributes an alternative to the conventional mathematical model along with a corresponding heuristic method to solve this proposed model. An implementation of the proposed method at various realistic instances reveals better performance in terms of net present value, computation time and optimality gap as compared to the traditional methods.

Reconstruction of Missing Well-Logs Using Facies-Informed Discrete Wavelet Transform and Time Series Regression

Summary Geophysical logging is widely used in lithofacies identification, reservoir parameter prediction, and geological modeling. However, it is common to have well-log sections with low-quality and/or missing segments. Repeating the well-log measurements is not only expensive but might also be impossible depending on the condition of the borehole walls. In these situations, reliable and accurate well-log prediction is, therefore, necessary in different stages of the geomodeling workflow. In this study, we propose a time series regression model to predict missing well-log data, incorporating facies information as an additional geological input and using discrete wavelet transform (DWT) to denoise the input data set. The main contributions of this work are threefold: (i) We jointly use facies information with well logs as the input data set; (ii) we use DWT to denoise the input data and consequently improve the signal-to-noise ratio of the input data; and (iii) we regard the depth domain as the time domain and use a time series regression algorithm for log reconstruction modeling. We show a real application example in two distinct scenarios. In the first, we predict missing well-log intervals. In the second, we predict complete well logs. The experimental results show the ability of the proposed prediction model to recover missing well-log data with high accuracy levels.

Development and Validation of Universal 3D Blast Fragmentation Model

The dominant technology in hard rock mining is drilling and blasting; therefore, the importance of fragment size estimation is an essential problem in the mining industry. By using a theoretical foundation that explains rock-fracturing mechanisms by blasting, a 3D fragmentation model with general applicability was developed. The main capabilities of the model are the ability to consider different and complex blasting patterns, both with parallel and non-parallel boreholes, and different explosive and rock properties. The geological input of the model is defined by generating the primary blocks within rock mass using discrete fracture networks. The model handles different, complex geological setups and blasting scenarios that include bench, ring, and tunneling blasting. Also, it considers borehole deviation, misfires, and the influence of previous blasts. Full-scale ring blasts were carried out to validate the model against real-world data, and the results showed a high level of agreement between model predictions and in situ data. In situ data were collected using image processing methodology and WipFrag v4 software. X50 and X80 size values showed a maximum error in prediction of around 15%, while R2 values between fragmentation curves were approximately 90% without a detailed model calibration to the obtained data.

Vanadium in phyllosilicate ores: Occurrence, crystal chemistry, and leaching behavior

This article examines the nanoscale occurrence patterns of vanadium in phyllosilicates and correlates them with amenability to leaching. A large fraction of the global vanadium resource is hosted in phyllosilicate ores such as roscoelite and V-bearing chlorites, clays, and micas. Recovery of V from these minerals in acid leaching is typically low but varies widely for unclear reasons. Transmission electron microscopy (TEM) of V-illites, V-chlorites, and roscoelites in leach heads and tails from the La Sal mine (Colorado Plateau) shows that the observed variability in recoveries correlates with intercalations of nanoscale V oxide phases within the phyllosilicate lattice. These are present in some of the V-illites in this study, which displayed higher leach recovery than V-illites without the V oxides. In the latter, as well as in V-chlorites, the V occurs as crystallographic substitutions without oxide intercalations. Electron energy loss spectroscopy (EELS) measurements in the scanning transmission electron microscope (STEM) show that this crystallographic V is mixed-valence, averaging 3.3 in V-chlorite, 3.6 in roscoelite, and 3.8 in V-illite. Unlike the oxide intercalations, this crystallographic type of V corresponds to negligible leaching recoveries irrespective of V siting, oxidation state, or type of phyllosilicate.These results shed light on the causes of a central problem in geometallurgy, which is attempting quantitative predictions of process outcomes from geological inputs. These results show that what appears to be the same mineral (in this case V-illite) can yield wildly disparate leach recoveries depending on nanoscale metal occurrence factors. Similar under-recognized variation in other ore minerals, within or between sites, may explain much of the notorious variability in their leaching behavior.

Imaging of small-scale faults in seismic reflection data: Insights from seismic modelling of faults in outcrop

Faults with throws that fall below vertical seismic resolution are challenging to identify in reflection seismic datasets. Nevertheless, such small-scale faults may still affect the seismic images, and in this study, we build seismic models of outcrop analogues to investigate how. Using photogrammetry from faults affecting Oligocene to Miocene carbonate rocks in Malta, we build a series of geological models from which synthetic seismic images are produced. The resulting seismic images are analysed to elucidate the effects of varying geologic input, signal properties and introduction of noise, and compared to real seismic data from the SW Barents Sea, offshore Norway. Our results suggest that at signal peak frequencies of 30 Hz and higher, using the classic Ricker wavelet type and without introducing noise, graben forming faults with a combined displacement down to ∼5 m affect the seismic image by slight downwarping of reflections, whereas single faults with displacement down to ∼10 m show detectable non-discrete reflection offsets in form of a monoclinal geometry at signal peak frequencies at 60 Hz. Using an Ormsby wavelet, we get seismic images with a quality that lie in between that of the 30 Hz and 60 Hz Ricker, even though the peak frequency is lower. The identified structures can also be seen when noise is included, although the reflections are more irregular and harder to detect. This suggests that under relatively noise-free conditions in high-quality reflection seismic datasets, lower-throw faults (as low as 5 m in this study) that do not induce discrete reflection offsets in seismic images may still produce reflection distortions. Additionally, seismic modelling using the Ormsby wavelet, and its effect on the seismic image, is lacking in literature as of today. We suggest that the results and examples shown in this study may be used to geologically inform fault interpretations in real seismic datasets and may form an empirical basis for geologically concept-driven fault interpretation strategies.

Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

AbstractGeological models are commonly used to predict the position of relevant geological features, such as rock types or faults in the subsurface. These models can contain significant uncertainties, as the geological input parameters are often not perfectly known. Predictions of geological features, for example, on the level of a tunnel during an excavation process, are therefore uncertain. This work shows how these uncertainties can be estimated using probabilistic concepts. Furthermore, an approach is presented to automatically adjust the geological model predictions using measurements of the tunnel boring machine (TBM) operation. To this aim, the geological forward model is combined with a measurement model and both are integrated in a probabilistic machine learning framework. This integration enables a Bayesian inference process using computational methods, enabling an update of the parameters of the geological and measurement models. Based on the inferred parameter distributions, the model predictions on the tunnel level are subsequently updated. The application of the concept in a simple schematic application shows that such a combination can accurately and precisely predict features ahead of the operation within the limits of the model capabilities. In future work, the methods need to be tested with a real case study to evaluate the accuracy of predictions using real‐world TBM data and more complex geological models. The framework presented here could directly be extended to this purpose.

How do differences in interpreting seismic images affect estimates of geological slip rates?

Abstract. Uncertainties of geological structural geometry constructed based on seismic reflections can stem from data acquisition, processing, analysis, or interpretation. Uncertainties arising from structural interpretations and subsequent estimates of geological slip have been particularly less quantified and discussed. To illustrate the implications of interpretation uncertainties for seismic potential and structural evolution, I use an example of a shear fault-bend fold in the central Himalaya. I apply a simple solution from the kinematic model of shear fault-bend folding to resolve the geological input slip of given structure and then compare the result with a previous study to show how differences in structural interpretations could impact dependent conclusions. The findings show that only a little variance in interpretations owing to subjectivity or an unclear seismic image could yield geological slip rates differing by up to ∼ 10 mm yr−1, resulting in significantly different scenarios of seismic potential. To reduce unavoidable subjectivity, this study also suggests that the epistemic uncertainty in raw data should be included in interpretations and conclusions.

Integrating Lineament Density in the DRASTIC Model for Better Groundwater Assessment

Forecast of groundwater potential zones is essential, especially in areas where surface water is not sufficient during the dry season, such as the Tampin District, Negeri Sembilan, Malaysia. In the literature, the lineament parameter is often combined with a groundwater assessment model such as the DRASTIC model. However, most of these practices do not follow the procedure of assigning weight to the parameter as used in the model, so many researchers assign different ranges of weights to the lineament parameter. Therefore, this study focuses on how to systematically incorporate a lineament density map into the existing DRASTIC model based on the specific range of weight specified by the model; thus, the inclusion of more geological input will improve the model performance. DRASTIC is an abbreviation for the parameters used in the model: Depth to water; Recharge; Aquifer media; Soil media; Topography; Impact to vadose zone; and Conductivity (hydraulic). The addition of the lineament density map has successfully improved the performance of the DRASTIC model from 50 to 80% based on the distribution of 30 producing wells in the Tampin District, where the geology and lineament density play major roles in determining the potential groundwater area.

An economic framework for producing critical minerals as joint products

The topic of economics of critical minerals production has received little attention in the economics literature. This study presents a two-stage optimization model to frame the economics of critical minerals. In the first-stage, firms minimize cost to choose input levels, including the extraction of a common ore to produce a critical mineral with or after the production of a main mineral. We examine the impact of geological, cost, and technology parameters on the level of input use and the decision to further process for critical minerals. We characterize the marginal cost of producing critical minerals, which is used in the second stage to determine production decisions. Results suggest that (1) production of critical minerals could be expanded by investing in technically efficient technologies and technologies with increasing returns to scale, (2) prescriptive mandates requiring firms to process a given percentage of geological input to recover critical minerals would have unintended consequences such as increasing the marginal cost of producing main minerals, and (3) the supply elasticity of critical minerals depends on returns to scale of production.

Risk Assessment and Fractionation of Cadmium Contamination in Sediment of Saguling Lake in West Java Indonesia

This research focused on the speciation and distribution patterns of cadmium in surface sediment from Saguling Lake, which is located in the Upper Citarum River. Organic compounds and heavy metals from anthropogenic activities in the watershed have contaminated the river. Sample from the upper layer of the sediment from Saguling Lake were taken from 12 locations, representing the dry and the rainy seasons in the period 2015-2018. Sediment cadmium (Cd) classification was conducted through a sequential extraction technique to determine Cd’s bioavailability and its risk to the water environment. During the rainy season, the total Cd concentration in the upper layer of the sediment was higher than during the dry season. The average dry and rainy season concentrations were 11.12 ± 2.16 mg/kg and 14.82 ± 1.48 mg/kgm in the sampling locations, distributed differently with the following order of the largest to the smallest concentration: 10B > 1A > 4 > 3 > 2 > 1B > 10A > 7 > 9 > 5 > 6 > 8 for the dry season, and 4 > 1A > 1B > 2 > 7 > 5 > 9 > 3 > 6 > 10A > 8 > 10B for the rainy season. All sampling locations (>60%) showed Cd in the resistant fraction, indicating no significant anthropogenic input of Cd into the surface sediment but more geological input due to high erosion. The values of RAC, ICF, and GFC indicate that the Cd in the surface sediment can be categorized as low risk.

Considering Engineering Geology Input for Probabilistic Flood Hazard Assessments

ABSTRACT Probabilistic risk assessments were developed in the 1970s as consistent approaches to assessing public health protection by nuclear-facility safety measures. Risk-informed initiatives resulted in the characterization of processes that produce extreme events (hazards) independently from the detrimental effects of such events on people or environment from facility damage (risks), as well as quantifying uncertainties. For example, large dams are designed to perform without uncontrolled reservoir release under seismic motion with 1/10,000 annual frequency. Geologic inputs for seismic hazards include ground motion sources and site response. Probabilistic flood hazards analyses are emerging in response to uncertainty about the effects of climate change, aging flood control structures, and acceptance of probabilistic seismic hazard analyses. Geologic inputs for flood hazard have focused on paleoflood hydrology from slackwater deposits and boulder bars. Procedures are available for calculating probable maximum floods, produced from the most severe combination of meteorological and hydrologic conditions, but not for assessing annual frequencies of such events. Flood routing, the domain of hydrologists and hydraulic engineers, typically stipulates channel stability. What if channels erode during extreme floods, watershed slopes are susceptible to landslides, or landslides reduce channel cross sections? Hydrologists and hydraulic engineers evaluate flood flow and water elevation effects at facilities, whereas engineering geologists need to assess slope response and mobilization of debris under extreme precipitation. Keeping slope assessments consistent with probabilistic approaches is challenging. A real location provides a hypothetical example to illustrate selected aspects of the geological approach and to utilize the results of some available tools.

Quantitative evaluation of caprock sealing controlled by fault activity and hydrocarbon accumulation response: K gasfield in the Xihu Depression, East China Sea Basin

Caprock-sealing capability affected by fault activity is an important problem that needs to be resolved in hydrocarbon accumulation analysis, especially for rifted basins. In this article, a quantitative model based on the mechanical properties estimated by geophysical logging was first established to characterize the brittle-ductile transition of mudstone caprocks. By establishing a burial depth reconstruction model, the brittle-ductile stage of the mudstone caprock at the termination time of fault activity (TTFA) was restored. Moreover, the caprock juxtaposition thickness (CJT) for brittle rock and the shale smear factor (SSF) for brittle-ductile rock were selected to quantitatively describe the sealing capacity of caprock controlled by faults. These mathematical methods were finally applied to a case study of the K gasfield in the Xihu Depression, East China Sea Basin. Compared with the sealing characterization based on the current mechanical properties, the brittle-ductile reconstruction at the TTFA and matching sealing evaluation better describe the effectiveness of the mudstone caprock. Additionally, the quantified sealing capacity and corresponding hydrocarbon response indicate that a CJT value of 56.1 m and an SSF value of 3.0 can be the threshold for caprock sealing and nonsealing in the study area. As a result, caprock with poor sealing led to vertical hydrocarbon leakage in the structural low zone and structural high zone, while strong sealing caprock located in the structural center zone effectively prevented the upward migration of hydrocarbons, thereby controlling the vertical hydrocarbon distribution in the K gasfield. In general, the evaluation accuracy of caprock sealing predominantly relies on the geological input to the model, especially the fluid pressure, rock mechanical parameters, and caprock and fault interpretation results, and it is also affected by further uncertainties arising from the sealing threshold definition and structural uplift.

Multi-metric Ecosystem Health Assessment of Three Inland Water Bodies in South-west, Nigeria, with Varying Levels of Sand Mining Activities and Heavy Metal Pollution.

This study was conducted to determine the health status of three water bodies (Badagry Creek, Ologe Lagoon and River Owo) exposed to varying degrees of sand mining activities and industrial pollution. Water, sediment and fish samples were collected monthly from the three water bodies between April 2019 and March 2020. Standard methods were used for the analysis of physico-chemical parameters, fish diversity indices, length-weight relationship, condition factor, sediment pollution indices, ecotoxicology of heavy metals in sediment, potential ecological risks and health risk assessment of heavy metals. Positive matrix factorization (PMF) was used to identify and quantify metal source. Shannon's diversity index (H') and condition factor varied between 2.03-2.37 and 0.84-4.86 respectively. Enrichment factor had a range of 4.05×10-4-8.65×10-1, while geoaccumulation index varied from -12.14 to -0.38. The mean quotients using the probable effect level (m-PEL-Q) are 3.91×10-4, 4.77×10-4 and 7.87×10-4 for Ologe Lagoon, Badagry Creek and River Owo respectively. The trend was the same with mean quotients using effect range-median (m-ERM-Q). The estimated dietary intake ranged from 0.00 mg/kg/day in Pb from River Owo to 1.15 × 10-3 mg/kg/day in Fe still from River Owo. The range of values of the target hazard quotient of the metals in Badagry Creek, River Owo and Ologe Lagoon are 1.23×10-4-1.65×10-2, 0.00-1.64×10-2 and 5.76×10-5-1.65×10-2 respectively. PMF identified three sources of metal into the aquatic ecosystems: agricultural, industrial and geological inputs. The study showed that the three aquatic ecosystems are healthy but require regular monitoring to promptly detect sudden changes in their health status.

Modelling a complex gold deposit with multiple-point statistics

This paper presents an innovative approach to the geological modelling and stochastic estimation of a Mineral Resource in a structurally complex gold deposit. A Direct Sampling multiple-point statistics algorithm is adopted to produce stochastic models of the lithology and gold grade distributions while using sparse geological and grade data. A multivariate modelling framework shows potential for modelling finely intercalated folded lithologies, which are problematic with classical methods. The method performs well with sparse data or even in the absence of data, provided auxiliary variables are available. Geological input is limited to an elementary training image and structural measurements of the lithological contacts to create a representative potential field of ductile deformation. A new modelling framework is adopted where a portion of the ‘true’ model is reserved to be used as a training image, while the remaining part serves as a validation tool prior to arriving at a satisfactory ensemble of realisations. This ensemble is carried forward as a stochastic model for the poorly informed part of the deposit. This approach lends itself to the adaptation of modern developments in artificial intelligence and machine learning. The geologist’s involvement is confined to incorporating an understanding of the deposit and its genesis into the explicit ‘prior’ model.