Joint resistivity and MUON tomography for generating 3D saturation and porosity models of heap leach operation

Abstract

Systems and methods generate a 3D model of saturation and an independent 3D model of porosity for a heap leach operation. Methods comprise determining, based at least in part on measured electrical potential, measured resistivity data at a plurality of resistivity-detection locations; determining, based at least in part on measured interactions of muons with muon detectors, measured directional muon intensities for a plurality of muon-detection locations; and performing tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile of saturation of the heap and a 3D profile of porosity of the heap.

Description

JOINT RESISTIVITY AND MUON TOMOGRAPHY FOR GENERATING 3D SATURATION AND POROSITY MODELS OF HEAP LEACH OPERATIONReference to Related Applications

[0001] This application claims priority from, and for the purposes of the United States of America the benefit under 35 USC §119 in connection with, United States application No. 63 / 789195 filed 15 April 2025 which is hereby incorporated herein by reference.Field

[0002] The technology disclosed herein relates to heap leaching. Particular embodiments provide methods and systems for generating 3D models (e.g. profiles) of saturation and porosity of a heap leach operation based on a combination of resistivity measurements / data and muon tomography measurements / data.Background

[0003] Heap leaching is a common mining process for recovering (i.e. extracting) valuable metals or minerals (e.g. gold, copper, uranium, silver, etc.) from ore. The heap leaching process typically involves blasting or crushing the ore into fragments (e.g. fragments of approximately 15 to 100mm in diameter) and piling or stacking the broken ore into a large heap (e.g. possibly up to approximately 1km in length, 400m in width, and 150m in height) on top of a pad or liner (e.g. a rubber membrane), typically fabricated from minimally permeable or impermeable material.

[0004] Stacking of the fragmented ore typically proceeds in phases (commonly referred to as “lifts”) which form layers atop the heap leach pad. Typical lift dimensions are on the order of 6 to 20m in height. Upon stacking the fragmented ore in each lift, the heap (e.g. the combination of lift(s) atop the heap leach pad) is typically irrigated with a lixiviant (e.g. cyanide solution, sulfuric acid, etc.). The lixiviant is usually applied with an array of sprinklers or drip systems across the top of the heap (the most recently stacked lift) for a period of approximately 4 to 6 months per lift. The lixiviant flows through the heap, including any lifts below the current lift. The lixiviant reacts with the crushed ore to leach the metals or minerals out of the crushed ore fragments, thus forming a “pregnant” leached solution containing dissolved metal(s) or mineral(s). The leached solution is collected at the bottom of the heap (e.g. on the membrane pad) and flows through pipes to a collection pond (e.g. locatedat the sides of the base of the heap leach pad). The leached solution is then processed to extract the metal or mineral and the residual solution remaining after extraction of the metal or the mineral may be recycled in a closed loop system back into the sprinklers.

[0005] Typically, recoveries are in a range of about 40% to 80% of the total metal or mineral in the crushed ore in a first pass (i.e. , the initial leaching cycle, where the lixiviant is applied to an uppermost lift of the heap for the first time). Sometimes, residual recovery (e.g. during a second pass, a third pass, etc.) can be achieved via migration of lixiviant through lower lifts, but overall recovery efficiency is typically dominated by the first pass.

[0006] One factor that negatively affects recovery efficiency is the phenomenon of solution pooling and channeling, where the lixiviant solution navigates through the heap via particular channels (e.g. channels exhibiting relatively low resistance to gravity-induced flow) and, consequently, the fragmented ore in the heap is not uniformly saturated. Solution pooling and channeling can result in dry zones, which are regions of the heap where the lixiviant has not penetrated or has a less than desirable amount of penetration. Metal or mineral recovery can be wholly or partially missed in dry zones, thus negatively impacting overall recovery efficiency and reducing the efficacy of additional passes.

[0007] There is a general desire to obtain more information about fluid migration of lixiviant within the heap of a heap leach operation during irrigation phases. Having such information (including for example, models or profiles of saturation and / or porosity of the heap) may permit operators to intervene, to physically re-arrange the ore of the current lift and thereby improve the characteristics of the lixiviant migration and corresponding recovery efficiency before a subsequent lift is stacked on top of the current lift. Given that the dominant cost in a mining and ore-extraction operation is typically in the mining and haulage process, any improvement in recovery efficiency may directly boost bottom line profit.Summary

[0008] Aspects of the invention provide methods and systems for modelling a heap of a heap leach operation - e.g. generating computer-based 3D models of saturation and porosity of the heap leach operation.

[0009] One aspect of the invention provides a method for modelling a heap of a heap leach operation. The method comprises: at each of a plurality of spaced apartresistivity-detection locations at least on, within, and / or adjacent to, the heap: measuring electrical potential across corresponding pairs of electrodes; and determining, by a processor and based at least in part on the measured electrical potential, measured resistivity data at the corresponding resistivity-detection location; at each of a plurality of spaced apart muon-detection locations in, or in a vicinity, of the heap: detecting muons that traverse through at least a part of the heap and interact with a muon detector over a time period; and determining, by the processor and based at least in part on the interactions, measured directional muon intensities for a plurality of directions intersecting at the muon-detection location; and performing, by the processor, tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile (e.g. computer-based model) of saturation of the heap and a 3D profile (e.g. computer-based model) of porosity of the heap.

[0010] Performing the tomographic data inversion to generate the 3D profiles of saturation and porosity of the heap may comprise: generating a 3D model of resistivity of the heap and a 3D model of density of the heap based at least in part on the measured resistivity data and the measured directional muon intensities; and determining corresponding saturation and porosity values based on the 3D models of resistivity and density.

[0011] Measuring electrical potential across corresponding pairs of electrodes may be performed during the time period of detecting muons that traverse through the at least a part of the heap and interact with the muon detector.

[0012] The method may comprise repeating the steps over a plurality of time periods to determine temporal 3D profiles of saturation and porosity of the heap, the temporal profiles comprising the 3D profiles of saturation and porosity of the heap determined for each time period.

[0013] The plurality of time periods may comprise a plurality of overlapping time periods, a plurality of sequential time periods and / or a plurality of temporally spaced apart time periods.

[0014] The plurality of muon-detection locations may comprise one or more of: within one or more monitoring wells in the heap; within one or more trenches formed on a sloped side or terrace of the heap; within one or more boreholes adjacent to the heap; within one or more drainage pipes located on a liner of the heap leach operation; within one or more pipes or trenches embedded within the heap; within one or morepipes located on a periphery of the heap; within one or more pipes located underneath the liner; and, adjacent to the heap.

[0015] The method may comprise installing, at each of the plurality of muon-detection locations adjacent to the heap, a panel muon detector oriented along a direction that results in relatively high acceptance of muons traversing substantially horizontally through the heap when compared to muons traversing the heap in other directions.

[0016] Performing the tomographic data inversion may be based at least in part on additional information comprising one or more of: measured gravity data measured at locations in, on or adjacent to the heap; and, measured temperature data measured at locations in, on or adjacent to the heap.

[0017] Performing the tomographic data inversion may be based at least in part on prior information, the prior information obtained from a prior execution of performing the tomographic data inversion.

[0018] Performing the tomographic data inversion may be based at least in part on prior information comprising one or more of: a prior 3D resistivity model of the heap obtained from a prior execution of performing the tomographic data inversion; a prior 3D density model of the heap obtained from a prior execution of performing the tomographic data inversion; a prior 3D saturation model of the heap obtained from a prior execution of performing the tomographic data inversion; and a prior 3D porosity model of the heap obtained from a prior execution of performing the tomographic data inversion.

[0019] Performing the tomographic data inversion may be based at least in part on an additional estimated porosity profile of the heap, the additional estimate porosity profile based at least on an assumed depth relationship from compactification of material under weight of stacking.

[0020] Generating the 3D model of resistivity of the heap and the 3D model of density of the heap may comprise determining a resistivity value and a density value for each of a plurality of 3D voxels in the heap.

[0021] For each voxel, the resistivity value may comprise a resistivity distribution and the density value may comprise a density distribution.

[0022] Determining corresponding saturation and porosity values based on the 3D models of resistivity and density may comprise, for each of the plurality of 3D voxels, computing a saturation value and a porosity value based on the corresponding resistivity value and density value of the 3D voxel.

[0023] For each voxel, the saturation value may comprise a saturation distribution and the porosity value may comprise a density distribution.

[0024] Performing the tomographic data inversion may comprise determining, for each of a plurality of 3D voxels in the heap, a corresponding saturation value and a corresponding porosity value. The saturation value may comprise a saturation distribution and the porosity value may comprise a density distribution.

[0025] Another aspect of the invention provides a system for modelling a heap of a heap leach operation. The system comprises: a plurality of spaced apart voltmeters distributed on, within, and / or adjacent to, the heap, each voltmeter measuring electrical potential across pairs of electrodes located at a corresponding resistivitydetection location; a plurality of spaced apart muon detectors distributed in, or in a vicinity of the heap leach operation, each muon detector detecting muons that traverse through at least a part of the heap and interact with the muon detector over a time period at one or more corresponding muon detection locations; and, a processor configured to: determine, based at least in part on the measured electrical potential, measured resistivity data at each of the resistivity-detection locations; determine, from the muon interactions, measured directional muon intensities for a plurality of directions intersecting at each of the muon detection locations; and perform tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile (e.g. computer-based model) of saturation of the heap and a 3D profile (e.g. computer-based model) of porosity of the heap.

[0026] The system may comprise any of the features, combinations of features or sub-combinations of features of the method recited above.

[0027] Aspects of the invention provide apparatus having any new and inventive feature, combination of features, or sub-combination of features as described herein.

[0028] Aspects of the invention provide methods having any new and inventive steps, acts, combination of steps and / or acts or sub-combination of steps and / or acts as described herein.

[0029] It is emphasized that the invention relates to all combinations of the above features, even if these are recited in different claims.

[0030] Further aspects and example embodiments are illustrated in the accompanying drawings and / or described in the following description.Brief Description of the Drawings

[0031] The accompanying drawings illustrate non-limiting example embodiments of the invention.

[0032] FIG. 1 A is a schematic cross-sectional view of a system for generating 3D saturation and porosity profiles for a heap leach operation according to an example embodiment.

[0033] FIG. 1B is a top plan view of the FIG. 1 A system.

[0034] FIG. 2 is a flowchart of a method for generating 3D saturation and porosity profiles a heap leach operation according to an example embodiment.Detailed Description

[0035] Throughout the following description, specific details are set forth in order to provide a more thorough understanding of the invention. However, the invention may be practiced without these particulars. In other instances, well known elements have not been shown or described in detail to avoid unnecessarily obscuring the invention. Accordingly, the specification and drawings are to be regarded in an illustrative, rather than a restrictive sense.

[0036] Aspects of the invention provide methods and systems for modelling a heap of a heap leach operation - e.g. generating computer-based 3D models of saturation and porosity of the heap leach operation.

[0037] Methods according to particular embodiments comprise: at each of a plurality of spaced apart resistivity-detection locations at least on, within, and / or adjacent to, the heap: measuring electrical potential across corresponding pairs of electrodes; and determining, by a processor and based at least in part on the measured electrical potential, measured resistivity data at the corresponding resistivity-detection location; at each of a plurality of spaced apart muon-detection locations in, or in a vicinity, of the heap: detecting muons that traverse through at least a part of the heap and interact with a muon detector over a time period; and determining, by the processor and based at least in part on the interactions, measured directional muon intensities for a plurality of directions intersecting at the muon-detection location; performing, by the processor, tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile of saturation of the heap and a 3D profile of porosity of the heap.

[0038] Systems according to particular embodiments comprise: a plurality of spaced apart voltmeters distributed on, within, and / or adjacent to, the heap, each voltmetermeasuring electrical potential across pairs of electrodes located at a corresponding resistivity-detection location; a plurality of spaced apart muon detectors distributed in, or in a vicinity of the heap leach operation, each muon detector detecting muons that traverse through at least a part of the heap and interact with the muon detector over a time period at one or more corresponding muon detection locations; and, a processor configured to: determine, based at least in part on the measured electrical potential, measured resistivity data at each of the resistivity-detection locations; determine, from the muon interactions, measured directional muon intensities for a plurality of directions intersecting at each of the muon detection locations; and performg tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile (e.g. computer-based model) of saturation of the heap and a 3D profile (e.g. computer-based model) of porosity of the heap.

[0039] One aspect of obtaining more information about fluid migration of lixiviant within the heap of a heap leach operation involves understanding the hydrodynamic properties of the crushed ore in the heap. Relevant hydrodynamic parameters for the heap of a heap leach operation comprise porosity and saturation. Porosity is a measure of the percentage of a material’s volume that consists of space (which may be occupied by liquid or gas or through which liquid or gas may pass) rather than solid matter. In the heap leaching context, porosity is a measure of the percentage of interstitial voids between fragments of broken or crushed ore (i.e. solids) relative to the overall volume of the heap. Saturation is a measure of the percentage of the available porous volume that is filled with liquid. In the heap leaching context, saturation is a measure of percentage of available porous volume in the heap that is filled with the lixiviant solution.

[0040] In petrophysics, Archie’s Law is an empirical law that relates the measured electrical resistivity (or conductivity) of a material (e.g. porous rocks in the heap of an irrigated heap leach operation) to the porosity and fluid saturation of the material.R = T Rfluid- p-mS~n(1)where R is the bulk resistivity of the fluid saturated (or partially saturated) material, Rfiuidis theresistivity of the fluid, p is the porosity, S is the fluid saturation, T is the tortuosity factor, which relates to the curvature of micro-channels in the porous rockmatrix, and m is the cementation exponent of the material (usually in the range of about 1.5 to 2.0) and n is the saturation exponent (usually close to 2.0). Archie’s Law is formulated based on an attempt to describe ion flow in clean, consolidated sands, with varying inter-granular porosity. In the context of Archie’s law, electrical conduction is assumed to be exclusively performed by ions dissolved in the porefilling fluid and electrical conduction is considered to be absent in the rock grains of the solid phase. Additional empirical laws, such as the Waxman-Smits equation, adapt Archie's Law for shaly (clay-bearing) formations, or other types of formations, and may be used as appropriate in different contexts. Regardless, the bulk conductivity of a porous rock formation that is partially saturated with ion-bearing, conductive fluids, depends on both the pore space properties, and the saturation of the pore volumes.

[0041] Archie’s Law has been relied on extensively in oil and gas well logging to infer properties of the subsurface from observed resistivity data. Resistivity tomography (ERT) is a geophysical technology for modelling the resistivity (which is inversely related to conductivity) of conductive media (e.g., the irrigated heap of a heap leach operation) by injecting currents into the media with electrodes, and measuring the electrical potential across pairs of electrodes in an area of the media within the vicinity of the injected currents. By comparing how the electric potential (voltage) varies across a set of measurements and understanding how current is moving through the conductive media, the resistivity of the material in the conductive media can be ascertained.

[0042] Typical ERT deployments in a heap leaching operation comprise arranging a line or a grid of both current injection points (e.g. electrodes) and voltage measurement points (e.g. electrodes). The process of deploying an ERT system usually comprises laying out large arrays of interconnected electrodes, coupling them to the ground (usually with metal stakes, or a conductive surface that sits on the ground), injecting currents between electrodes following a particular (e.g. configurable) pattern, and measuring the voltage across the same electrodes and / or other electrodes, also following a particular (e.g. configurable) pattern. The measurement data comprises the locations of the electrodes, the injected currents (voltages) and the observed potential differences across the measurement electrodes. Using this resistivity measurement data, a three-dimensional (3D) model (e.g. image or profile) of resistivity (or conductivity) of the conductive media in theheap can be determined using data inversion. By performing this process at discrete time intervals, successive temporal 3D resistivity profiles may be obtained to determine how the 3D resistivity evolves during a heap leaching operation. With the resistivity model, some insights into the fluid migration within a leach pad can be obtained.

[0043] In the heap leaching context, resistivity (and conductivity) are related to the material properties, and to the saturation of the material in the heap with mineral and ion-laden solutions, such as lixiviants, which significantly enhance the bulk apparent conductivity. Regions of the heap that are saturated with lixiviant solution are likely highly conductive, whereas regions that are not saturated are typically relatively more resistive.

[0044] As set out above, Archie’s Law relates resistivity to a mathematical combination of porosity and saturation. One limitation prior art ERT systems is that the modelled 3D resistivity determined by data inversion does not facilitate unambiguous determination of both porosity and saturation separately based on Archie’s Law - i.e. according to Archie’s Law (equation (1) above), resistivity is a function of both porosity and saturation.

[0045] Muon tomography (in a geophysical context) is a technology that measures intensities of cosmic ray muon flux traversing any material composition to thereby generate a 3D density model / mapping of the material composition using tomographic data inversion. Cosmic ray muons may be generated when high-energy cosmic rays (e.g., protons, heavy nuclei, etc.) from cosmic sources (e.g., supernova explosions, active galactic nuclei, pulsars and neutron stars, black hole accretion disks, etc.) interact with the Earth’s atmosphere (e.g., colliding with atomic nuclei such as nitrogen and oxygen to create a shower of secondary particles). Muons may be relatively long-lived compared to other particles in the shower of secondary particles. Muons generally do not interact strongly and can penetrate deep into the atmosphere with minimal energy loss. Due to relativistic time dilation, many muons survive long enough to reach the Earth's surface. These muons can often penetrate through materials, including penetrating underground, reaching depths of up to several kilometers in rock or even deeper in water or ice. The directional intensity of muon flux (e.g. the number of muons per unit time per unit solid angle and referred to herein as directional muon intensity) relates directly to the average density of matter along a straight-line path in the direction of measurement. Consequently, a densitydistribution of a material composition can be modelled (using data inversion) by measuring the attenuation of muon flux intensity through the material composition.

[0046] The relationship between porosity and density may be modelled according to:d = (1 - p) • dQ+ p • dvoid(2)where: d is the bulk density; p is the porosity, defined as a percentage of the volume that is void (non-solid) space; dQis the density of the in-situ material (e.g., the crushed ore); and dvoidis the density of the void material (which could be fluid or air depending on the level of saturation). Accounting for the effect of saturation on dvoid, equation (2) above becomes:d = (1 - p) • d0+ p ■ (S ■ dsol+ (1 - S) • dair) (3)Where: dsotis the density of the lixiviant solution; dairis the density of air (about 0 g / cc); and S is the saturation, which is the percentage of the void volume that is filled with lixiviant solution.

[0047] Therefore, by combining the relationships between porosity and density (equation (3)), and the relationship between resistivity, porosity and saturation (as stated by Archie’s Law - equation (1)), the saturation S and porosity p at any 3D location in the heap of a heap leach operation can be separately solved based on knowledge of the local density d and local resistivity R.

[0048] FIG. 1A is a schematic cross-sectional view of a system 100 for generating a 3D saturation profile and a 3D porosity profile for a heap leach operation 102 according to an example embodiment. FIG. 1 B is a top plan view of the FIG. 1 A system 100.

[0049] Heap leach operation 102 comprises a heap 104 of broken ore fragments stacked on top of a liner or pad 106. In some embodiments, pad 106 comprises a membrane fabricated from rubber and / or other non-permeable or minimally permeable material(s). Heap 104 may comprise any suitable number of lifts. In the example embodiment shown in FIG. 1 A and FIG. 1 B, heap 104 is shown as comprising three lifts 104-1, 104-2 and 104-3 (demarcated by dotted lines in FIG. 1A). It is to be understood that the three lifts 104-1 , 104-2, 104-3 shown in FIG. 1A and FIG. 1 B are shown for illustrative purposes only and there is no restriction on thenumber of lifts.

[0050] System 100 comprises a plurality of muon detectors 108 located at each of a plurality of spaced apart muon-detection locations 110 in, under, adjacent to or otherwise in a vicinity, of heap 104. At each muon-detection location 110, muon detectors 108 detect muons that traverse through at least a part of heap 104 over a time period. A controller 120 (comprising one or more suitably configured data processor(s)) is in data communication with muon detectors 108 to determine, from the interactions, measured directional muon intensities for a plurality of directions intersecting at each muon-detection location 110. In some embodiments, each muon detector 108 comprises a muon tracking sensor for detecting muons passing through the sensor, suitable memory for storing data relating to muon detection events, suitable communication hardware (e.g. a network interface and / or the like) for communicating data relating to muon detection events to controller 120, and an integrated processor for determining trajectories associated with such passing muons and for controlling the detector 108. Non-limiting examples of suitable muon detectors are described in Patent Cooperation Treaty (PCT) applications No.PCT / CA2020 / 050716, PCT / CA2020 / 000036, PCT / CA2021 / 051509 and PCT / CA2023 / 050548, which are hereby incorporated herein by reference.

[0051] Muon-detection locations 110 of muon detectors 108 may be known and such detector location information may be communicated to or otherwise provided to controller 120.

[0052] Muon detectors 108 may be located within boreholes adjacent to heap 104 and / or adjacent to heap leach operation 102. Such muon detectors 108 may be referred to as “adjacent borehole muon detectors”. In the example embodiment shown in FIG. 1A and FIG. 1 B, muon detector 108-1 at muon-detection location 110-1 is an example of an “adjacent borehole muon detector” located in a borehole 112 drilled or otherwise formed adjacent to heap 104. Borehole 112 may extend along any suitable direction. “Adjacent borehole muon detector” 108-1 may comprise any suitable borehole muon detectors and be installed with any suitable orientations.

[0053] Muon detectors 108 may be located within monitoring wells located in the volume of heap 104 and / or extending from an outer surface of heap 104. Such muon detectors 108 may be referred to as “monitoring well muon detectors”. In the example embodiment shown in FIG. 1A and FIG. 1B, muon detector 108-2 at muon-detection location 110-2 is an example of a “monitoring well muon detector” located in amonitoring well 114. Monitoring well 114 of the illustrated embodiment extends from an outer surface of heap 104, although this is not necessary. In some embodiments, monitoring well 114 may be located within the volume of heap 104 (e.g. after being covered by a subsequent lift). Monitoring well 114 may extend along any suitable directions. In some embodiments, monitoring well 114 extends along a direction that is orthogonal or substantially orthogonal (e.g. ±10° or ±15°) to an outer surface of heap 104. “Monitoring well muon detector” 108-2 may comprise any suitable borehole muon detectors and be installed with any suitable orientations.

[0054] Muon detectors 108 may be located within trenches located in the volume of heap 104 and / or dug into an outer surface of heap 104. For example, trenches may be dug into sloped and / or terraced sides (e.g. terraced side 104S) of heap 104. Such muon detectors 108 may be referred to as “trench muon detectors”. In the example embodiment shown in FIG. 1A and FIG. 1 B, muon detector 108-3 at muon-detection location 110-3 is an example of a “trench muon detector” located in a trench 116. Trench 116 may be dug along any sloped and / or terraced sides of the outer surface of heap 104 and in any lift(s) of heap 104. Trench 116 of the illustrated embodiment extends from an outer surface (e.g. a terrace) of heap 104, although this is not necessary. In some embodiments, trench 116 may be located within the volume of heap 104 (e.g. after being covered by a subsequent lift). Trenches 116 may extend along any suitable directions. In some embodiments, trench 116 extends along a direction that is substantially parallel to a direction of extension of heap 104, although this is not necessary. “Trench muon detectors” 108-3 may comprise any suitable borehole muon detectors and be installed with any suitable orientations.

[0055] Muon detectors may be located on an outer surface of heap 104, adjacent to heap 104 and / or adjacent to heap leach operation 102. Such muon detectors 108 may be referred to as “adjacent muon detectors”. In the example embodiment shown in FIG. 1A and FIG. 1B, muon detector 108-4 at muon-detection location 110-4 is an example of an “adjacent muon detector” located proximate to heap 104. “Adjacent muon detectors” 108-4 may comprise any suitable muon detectors and be installed with any suitable orientations. In some embodiments, “adjacent muon detector” 108-4 comprises a panel muon detector oriented along a direction that results in high acceptance of muons traversing substantially horizontally through heap 104 and / or heap leach operation 102.

[0056] Muon detectors 108 may be located within pipes located at a base of heap104, embedded within heap 104 (e.g., installed between lifts during stacking process), located around a periphery of heap leach operation 102, or located beneath pad 106 of heap leach operation102. For example, pipes installed at a base of heap 104 may comprise drainage pipes, pipes embedded within heap 104 may comprise aeration pipes, solution distribution pipes, and / or monitoring pipes, and pipes installed beneath pad 106 may comprise leak detection pipes. Such muon detectors 108 may be referred to as “pipe muon detectors”. In the example embodiment shown in FIG. 1 A and FIG. 1 B, muon detector 108-5 at muon-detection location 110-5 is an example of a “pipe muon detector” located within a leak detection pipe 118 that is installed underneath pad 106 of heap leach operation 102, and muon detector 108-6 at muondetection location 110-6 is an example of a “pipe muon detector” located within a drainage pipe 122 that is installed at a base of heap 104.

[0057] By placing muon detectors 108 in various configurations in various muondetection locations 110, a 3D density distribution of heap 104 and / or heap leach operation 102 can be dynamically modelled via muon tomography. Typical resolutions that can be achieved, would be to image a 0.2 g / cc density contrast with spatial resolution on the order of ~5m across heap 104, thus providing insight into porosity and saturation of the material, where porosity is the measure of interstitial voids between particles of crushed ore, and saturation is the percentage of the available porous volume that is filled with lixiviant solution. Variations in crushed material density (such as from different geological structures in the original ore body), and in porosity, and in fluid saturation, are all factors that affect in-situ density distributions.

[0058] System 100 also comprises a plurality of resistivity measuring systems 124 (schematically illustrated using black circles with vertical lines in FIG. 1 A) at a plurality of spaced apart resistivity-detection locations 126. The plurality of spaced apart resistivity-detection locations 126 may be on, within, and / or adjacent to heap 104 and / or heap leach operation102. In the illustrated FIG. 1A embodiment, an example of a resistivity measuring system 124 located on heap 104 is resistivity measuring system 124-1 at resistivity-detection location 126-1. An example of a resistivity measuring system 124 located within heap 104 is resistivity measuring system 124-2 at resistivity-detection location 126-2. An example of a resistivity measuring system 124 located adjacent to heap 104 is resistivity measuring system 124-3 at resistivitydetection location 126-3. Each resistivity measuring system 124 comprises at least a pair of electrodes configurable to inject currents into heap 104 or surroundingmaterials, and a voltmeter configured to measure the electrical potential difference between pairs of electrodes (which may be the same and / or different from the current-injecting electrodes). In some embodiments, controller 120 is in data communication with each resistivity measuring system 124 to receive data about the injected currents, to receive data about the electrical potential measurements from corresponding resistivity detection locations and to determine the corresponding measured resistivity based on the injected currents and measured electrical potential differences.

[0059] FIG. 2 is a flowchart of a method 200 for modelling a heap leach operation (e.g. heap leach operation 102 of FIGs. 1A, 1 B) according to an example embodiment. Method 200 may be performed by system 100. Individual steps of method 200 may be performed at least in part by controller 120 (FIGs. 1 A, 1 B).

[0060] Method 200 begins with block 202 which involves obtaining resistivity data 205. Obtaining resistivity data comprises, at each of a plurality of spaced apart resistivity-detection locations (e.g. at resistivity-detection locations 126-1, 126-2, 126-3, etc.) on, within, and / or adjacent to, the heap of a heap leach operation (e.g. heap 104 of heap leach operation 102): measuring electrical potential across pairs of electrodes; and determining, from the measured electrical potential, measured resistivity data for the corresponding resistivity-detection location. In some embodiments, measuring electrical potential across pairs of electrodes comprises injecting currents between pairs of electrodes and measuring the voltage across pairs of electrodes (which may be the same and / or different from the current-injecting electrodes) following a particular (e.g. configurable) pattern. For example, if the plurality of spaced apart resistivity-detection locations is arranged in a rectangular grid pattern with columns and rows, an example particular pattern of current injection and voltage measurement can comprise performing the measurements along a first column and then after completing the measurements along the first column, performing the measurements along a second column, and so on until measurements have been acquired from all or some suitable subset of the columns. Adjacent columns may have shared electrodes for injecting currents and / or measuring voltages.

[0061] Method 200 then proceeds to block 206 which involves obtaining muon data 209. In some embodiments, muon data 209 comprises directional muon intensity data (e.g. a number of muons per unit time per unit solid angle). Obtaining muon data 209comprises, at each of a plurality of spaced apart muon-detection locations (e.g. muon detection locations 110) in, and / or in a vicinity, of the heap of a heap leach operation (e.g. heap 104 of heap leach operation 102): detecting muons that traverse through at least a part of the heap 104 and interact with a muon detector (e.g. muon detector 108) over a suitable time period; and determining, from the interactions, measured directional muon intensities for a plurality of directions intersecting the muon-detection location. For each muon detector or each muon-detection location, such directional muon intensity data may comprise, for each of a set of discrete directions (e.g. discrete solid angles), a count of muons that interact with the muon detector during the time period in the discrete direction. Detecting directional muon intensity is described in more detail in PCT / CA2023 / 051177 which is hereby incorporated herein by reference. As known to those familiar with subterranean muon tomography, there is a relationship between directional muon intensity and the line integral of density in the corresponding direction. Block 206 may be performed over any suitable time period(s).

[0062] After obtaining resistivity data 205 and muon data 209, method 200 proceeds to block 210 of performing joint tomography data inversion based on resistivity data 205 and muon data 209 to generate 3D representations (e.g. profiles or models) of saturation 217A and porosity 217B. The block 210 tomographic data inversion may be joint in the sense that both resistivity data 205 and muon data 209 are obtained for the same temporal period. The block 210 tomographic data inversion may be joint in the sense that determining both 3D saturation model 217A and 3D porosity model 217B (or both 3D resistivity model 213A and porosity model 213B) may be based on both resistivity data 205 and muon data 209. This is not necessary, however; in some embodiments, as discussed below, the block 210 data inversion may use resistivity data 205 to determine a 3D resistivity model 213A independently of muon data 209 and may use muon data 209 to determine a 3D density model 213B independently of resistivity data 205 and then 3D resistivity model 213A and porosity model 213B may be used to generate 3D saturation model 217A and 3D porosity model 217B.

[0063] Block 210 may be performed by a suitably configured computer (e.g. controller 120 (FIGs. 1A, 1 B)). Saturation model 217A and porosity model 217B may comprise computer-based 3D models. Saturation model 217A and porosity model 217B may comprise a defined set of 3D voxels (which may be the same voxels or different voxels for each of models 217A, 217B), with a saturation value for each voxel(saturation model 217A) and a separate porosity value for each voxel (porosity model 217B). As used herein, unless specified otherwise, saturation “values” of saturation model 217A may comprise deterministic saturation values for each voxel, stochastic saturation distributions for each voxel or both; and, similarly, porosity “values” of porosity model 217B may comprise deterministic porosity values for each voxel, stochastic porosity distributions for each voxel or both. In some embodiments, performing the joint tomography data inversion at block 210 based at least on resistivity data 205 and muon data 209 comprises performing an optimization to fit the inverted (3D) data to the measured data (e.g. resistivity data 205 and muon data 209).

[0064] The block 210 data-inversion optimization may comprise generating a proposed 3D saturation profile of heap 104 and optimizing (e.g. minimizing) an objective function comprising term(s) that attribute cost to differences between saturation values predicted based on measured resistivity data 205 and muon data 209 and proposed saturation values of the proposed 3D saturation profile to thereby determine an optimized 3D profile of saturation 217A. The block 210 data-inversion optimization may also comprise generating a proposed 3D porosity profile of heap 104 and optimizing (e.g. minimizing) an objective function comprising term(s) that attribute cost to differences between porosity values predicted based on measured resistivity data 205 and muon data 209 and proposed porosity values of the proposed 3D porosity profile to thereby determine an optimized 3D profile of porosity 217B. Either or both of these block 210 data-inversion optimizations may be iterative.

[0065] In some embodiments, proposed saturation values of the proposed 3D profile of saturation and / or proposed porosity values of the proposed 3D profile of porosity are estimated based at least in part on optional prior information 201 A. Such optional prior information 201 A may comprise information obtained from prior block 210 operations - e.g. block 210 operations performed for different (e.g. prior or previous) sets of resistivity data 205 and / or muon data 209. Prior information 201 A may comprise: prior resistivity information (e.g. prior 3D resistivity model(s) 213A discussed in more detail below), prior density information (e.g. prior 3D density model(s) 213B discussed in more detail below), prior saturation information (e.g. 3D prior saturation models 217A), prior porosity information (e.g. 3D prior porosity models 217B), resistivity values, density values, saturation values and / or porosity values determined from one or more previous iterations of block 210 and / or method200 based on different (e.g. prior or previous) sets of resistivity data 205 and / or muon data 209.

[0066] In some embodiments, optional additional information 201 B is incorporated in the block 210 tomographic data inversion. Such additional information 201 B may be used as a basis for additional constraint(s) and / or terms (e.g. soft constraints) in the objective function. In some embodiments, additional information 201 B comprises resistivity information (e.g. prior 3D resistivity model(s) 213A discussed in more detail below), density information (e.g. prior 3D density model(s) 213B discussed in more detail below), saturation information (e.g. 3D prior saturation models 217A), porosity information (e.g. 3D prior porosity models 217B), resistivity values, density values, saturation values and / or porosity values determined from one or more previous iterations of block 210 and / or method 200 based on different (e.g. prior or previous) sets of resistivity data 205 and / or muon data 209. Additional information 201 B may additionally or alternatively come from other source(s). For example, any block of material in the heap of a heap leach operation can be traced back to the original block of material mined from the ore body. As a result, as a non-limiting example, a prior density model and / or estimated porosity profile can be established given assumed compactification of the material in the heap leach pad.

[0067] In some embodiments, additional information 201 B comprises airborne or ground gravity data which can be incorporated as additional constraints for data inversion of subsurface density distributions (e.g. local variations in gravity may be related to density differences in the materials of the respective areas). In some embodiments, additional information 201 B comprises airborne or ground and in-well temperature measurements which can be incorporated as additional constraints for data inversion of resistivity distributions of the heap leach operation (e.g. temperature variations may be related to presence, or lack of, lixiviant). In some embodiments, additional information 201 B comprises information about resistivity, density, porosity and / or saturation distributions of the heap leach operation generated from previous iteration(s) of method 200.

[0068] In some embodiments, block 210 comprises an optional block 212 (shown in dashed lines) which comprises generating a 3D models of resistivity 213A and density 213B by performing a joint tomographic data inversion and then, proceeds to optional block 214 which involves determining corresponding saturation and porosity values based on 3D models of resistivity 213A and density 213B to thereby generate3D representations (e.g. profiles or models) of saturation 217A and porosity 217B of the heap of the heap leach operation. The block 212 tomographic data inversion may be joint in the sense that both resistivity data 205 and muon data 209 are obtained for the same temporal period. The block 212 tomographic data inversion may be joint in the sense that determining both 3D resistivity model 213A and porosity model 213B may be based on both resistivity data 205 and muon data 209. This is not necessary, however; in some embodiments, the block 212 data inversion may use resistivity data 205 to determine a 3D resistivity model 213A independently of muon data 209 and may use muon data 209 to determine a 3D density model 213B independently of resistivity data 205 Resistivity model 213A and density model 213B may comprise computer-based 3D models. Resistivity model 213A and density model 213B may comprise a defined set of 3D voxels (which may be the same voxels or different voxels for each of models 213A, 213B), with a resistivity value for each voxel (resistivity model 213A) and a separate density value for each voxel (density model 213B). As used herein, unless specified otherwise, resistivity “values” of resistivity model 213A may comprise deterministic resistivity values for each voxel, stochastic resistivity distributions for each voxel or both; and, similarly, density “values” of density model 217B may comprise deterministic density values for each voxel, stochastic density distributions for each voxel or both. The optional block 212 data inversion may be similar to that of the block 210 data inversion discussed above, may involve analogous optimizations and may incorporate similar prior information 201 A and / or additional information 201 B.

[0069] In some embodiments, determining saturation and porosity values at optional block 214 comprises computing saturation and porosity values based on equations (1) and (3) above. In some embodiments, generating the 3D profiles of saturation 217A and porosity 217B (block 214) comprises, for each of the plurality of 3D voxels in the heap, computing a saturation value and a porosity value based on the corresponding resistivity value and density value of the 3D voxel. In some embodiments, generating 3D models of resistivity 213A and density 213B at block 212 and then determining saturation and porosity values at block 214 are integrated into block 210 of performing joint tomography data inversion, such that 3D profiles of saturation 217A and porosity 217B are directly generated based at least in part on resistivity data 205 and muon data 209 via block 210 of joint tomography data inversion.

[0070] Method 200 may be repeated over a plurality of time periods to provide a temporal profile of saturation and porosity of the heap leach operation. The temporal profile may be characterized by the profiles of saturation 217A and porosity 217B of the heap leach operation determined for each time period. In some embodiments, the plurality of time periods comprises: a plurality of overlapping time periods; a plurality of sequential time periods; and / or a plurality of temporally spaced apart time periods.

[0071] Thus, simultaneously mapping resistivity with ERT and mapping density with muon tomography facilitate determining porosity and fluid saturation separately by performing a joint data invention to obtain a 3D model of resistivity and density distributions based on the measured resistivity data and muon data. By collecting resistivity data and muon data continuously over periods of time and obtaining corresponding porosity and fluid saturation profiles, a temporal understanding of fluid migration within a heap leach operation can be ascertained. The temporal understanding of fluid migration within a heap leach operation may facilitate timely interventions to increase saturation levels in regions of the heap leach operation with relatively low saturation levels, resulting in improved saturation profile and, consequently, higher recovery efficiency (e.g. 90% or higher in a first pass).

[0072] Where a component (e.g. a software module, processor, assembly, device, circuit, etc.) is referred to herein, unless otherwise indicated, reference to that component (including a reference to a “means”) should be interpreted as including as equivalents of that component any component which performs the function of the described component (i.e. , that is functionally equivalent), including components which are not structurally equivalent to the disclosed structure which performs the function in the illustrated exemplary embodiments of the invention.

[0073] Embodiments of the invention may be implemented using specifically designed hardware, configurable hardware, programmable data processors configured by the provision of software (which may optionally comprise “firmware”) capable of executing on the data processors, special purpose computers or data processors that are specifically programmed, configured, or constructed to perform one or more steps in a method as explained in detail herein and / or combinations of two or more of these. Examples of specifically designed hardware are: logic circuits, application-specific integrated circuits (“ASICs”), large scale integrated circuits (“LSIs”), very large scale integrated circuits (“VLSIs”), and the like. Examples of configurable hardware are: one or more programmable logic devices such as programmable array logic (“PALs”),programmable logic arrays (“PLAs”), and field programmable gate arrays (“FPGAs”). Examples of programmable data processors are: microprocessors, digital signal processors (“DSPs”), embedded processors, graphics processors, math coprocessors, general purpose computers, server computers, cloud computers, mainframe computers, computer workstations, and the like. For example, one or more data processors in a control circuit for a device may implement methods as described herein by executing software instructions in a program memory accessible to the processors.

[0074] Processing may be centralized or distributed. Where processing is distributed, information including software and / or data may be kept centrally or distributed. Such information may be exchanged between different functional units by way of a communications network, such as a Local Area Network (LAN), Wide Area Network (WAN), or the Internet, wired or wireless data links, electromagnetic signals, or other data communication channel.

[0075] The invention may also be provided in the form of a program product. The program product may comprise any non-transitory medium which carries a set of computer-readable instructions which, when executed by a data processor, cause the data processor to execute a method of the invention. Program products according to the invention may be in any of a wide variety of forms. The program product may comprise, for example, non-transitory media such as magnetic data storage media including floppy diskettes, hard disk drives, optical data storage media including CD ROMs, DVDs, electronic data storage media including ROMs, flash RAM, EPROMs, hardwired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, or the like. The computer-readable signals on the program product may optionally be compressed or encrypted.

[0076] In some embodiments, the invention may be implemented in software. For greater clarity, “software” includes any instructions executed on a processor, and may include (but is not limited to) firmware, resident software, microcode, code for configuring a configurable logic circuit, applications, apps, and the like. Both processing hardware and software may be centralized or distributed (or a combination thereof), in whole or in part, as known to those skilled in the art. For example, software and other modules may be accessible via local memory, via a network, via a browser or other application in a distributed computing context, or via other means suitable for the purposes described above.

[0077] Software and other modules may reside on servers, workstations, personal computers, tablet computers, and other devices suitable for the purposes described herein.Interpretation of Terms

[0078] Unless the context clearly requires otherwise, throughout the description and the claims:• “comprise”, “comprising”, and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to”;• “connected”, “coupled”, or any variant thereof, means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof;• “herein”, “above”, “below”, and words of similar import, when used to describe this specification, shall refer to this specification as a whole, and not to any particular portions of this specification;• “or”, in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list;• the singular forms “a”, “an”, and “the” also include the meaning of any appropriate plural forms. These terms (“a”, “an”, and “the”) mean one or more unless stated otherwise;• “and / or” is used to indicate one or both stated cases may occur, for example A and / or B includes both (A and B) and (A or B);• “approximately” when applied to a numerical value means the numerical value ± 10%;• where a feature is described as being “optional” or “optionally” present or described as being present “in some embodiments” it is intended that the present disclosure encompasses embodiments where that feature is present and other embodiments where that feature is not necessarily present and other embodiments where that feature is excluded. Further, where any combination of features is described in this application this statement is intended to serve as antecedent basis for the use of exclusive terminologysuch as "solely," "only" and the like in relation to the combination of features as well as the use of "negative" limitation(s)” to exclude the presence of other features; and• “first” and “second” are used for descriptive purposes and cannot be understood as indicating or implying relative importance or indicating the number of indicated technical features.

[0079] Words that indicate directions such as “vertical”, “transverse”, “horizontal”, “upward”, “downward”, “forward”, “backward”, “inward”, “outward”, “left”, “right”, “front”, “back”, “top”, “bottom”, “below”, “above”, “under”, and the like, used in this description and any accompanying claims (where present), depend on the specific orientation of the apparatus described and illustrated. The subject matter described herein may assume various alternative orientations. Accordingly, these directional terms are not strictly defined and should not be interpreted narrowly.

[0080] Where a range for a value is stated, the stated range includes all sub-ranges of the range. It is intended that the statement of a range supports the value being at an endpoint of the range as well as at any intervening value to the tenth of the unit of the lower limit of the range, as well as any subrange or sets of sub ranges of the range unless the context clearly dictates otherwise or any portion(s) of the stated range is specifically excluded. Where the stated range includes one or both endpoints of the range, ranges excluding either or both of those included endpoints are also included in the invention.

[0081] Certain numerical values described herein are preceded by "about". In this context, "about" provides literal support for the exact numerical value that it precedes, the exact numerical value ±5%, as well as all other numerical values that are near to or approximately equal to that numerical value. Unless otherwise indicated a particular numerical value is included in “about” a specifically recited numerical value where the particular numerical value provides the substantial equivalent of the specifically recited numerical value in the context in which the specifically recited numerical value is presented. For example, a statement that something has the numerical value of “about 10” is to be interpreted as: the set of statements:• in some embodiments the numerical value is 10;• in some embodiments the numerical value is in the range of 9.5 to 10.5;and if from the context the person of ordinary skill in the art would understand thatvalues within a certain range are substantially equivalent to 10 because the values with the range would be understood to provide substantially the same result as the value 10 then “about 10” also includes:• in some embodiments the numerical value is in the range of C to D where C and D are respectively lower and upper endpoints of the range that encompasses all of those values that provide a substantial equivalent to the value 10

[0082] Specific examples of systems, methods and apparatus have been described herein for purposes of illustration. These are only examples. The technology provided herein can be applied to systems other than the example systems described above. Many alterations, modifications, additions, omissions, and permutations are possible within the practice of this invention. This invention includes variations on described embodiments that would be apparent to the skilled addressee, including variations obtained by: replacing features, elements and / or acts with equivalent features, elements and / or acts; mixing and matching of features, elements and / or acts from different embodiments; combining features, elements and / or acts from embodiments as described herein with features, elements and / or acts of other technology; and / or omitting combining features, elements and / or acts from described embodiments.

[0083] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any other described embodiment(s) without departing from the scope of the present invention.

[0084] Any aspects described above in reference to apparatus may also apply to methods and vice versa.

[0085] Any recited method can be carried out in the order of events recited or in any other order which is logically possible. For example, while processes or blocks are presented in a given order, alternative examples may perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and / or modified to provide alternative or subcombinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks may instead be performed in parallel, simultaneously or at different times.

[0086] Various features are described herein as being present in “some embodiments”. Such features are not mandatory and may not be present in all embodiments. Embodiments of the invention may include zero, any one or any combination of two or more of such features. All possible combinations of such features are contemplated by this disclosure even where such features are shown in different drawings and / or described in different sections or paragraphs. This is limited only to the extent that certain ones of such features are incompatible with other ones of such features in the sense that it would be impossible for a person of ordinary skill in the art to construct a practical embodiment that combines such incompatible features. Consequently, the description that “some embodiments” possess feature A and “some embodiments” possess feature B should be interpreted as an express indication that the inventors also contemplate embodiments which combine features A and B (unless the description states otherwise or features A and B are fundamentally incompatible).This is the case even if features A and B are illustrated in different drawings and / or mentioned in different paragraphs, sections or sentences.

[0087] It is therefore intended that the following appended claims and claims hereafter introduced are interpreted to include all such modifications, permutations, additions, omissions, and sub-combinations as may reasonably be inferred. The scope of the claims should not be limited by the preferred embodiments set forth in the examples, but should be given the broadest interpretation consistent with the description as a whole.

Claims

WHAT IS CLAIMED IS:

1. A method for modelling a heap of a heap leach operation, the method comprising:at each of a plurality of spaced apart resistivity-detection locations at least on, within, and / or adjacent to, the heap: measuring electrical potential across corresponding pairs of electrodes; and determining, by a processor and based at least in part on the measured electrical potential, measured resistivity data at the corresponding resistivity-detection location;at each of a plurality of spaced apart muon-detection locations in, or in a vicinity, of the heap: detecting muons that traverse through at least a part of the heap and interact with a muon detector over a time period; and determining, by the processor and based at least in part on the interactions, measured directional muon intensities for a plurality of directions intersecting at the muon-detection location; performing, by the processor, tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile (e.g. computer-based model) of saturation of the heap and a 3D profile (e.g. computer-based model) of porosity of the heap.

2. The method of claim 1 or any other claim herein wherein performing the tomographic data inversion to generate the 3D profiles of saturation and porosity of the heap comprises: generating a 3D model of resistivity of the heap and a 3D model of density of the heap based at least in part on the measured resistivity data and the measured directional muon intensities; and determining corresponding saturation and porosity values based on the 3D models of resistivity and density.

3. The method of any one of claims 1 to 2 or any other claim herein wherein measuring electrical potential across corresponding pairs of electrodes is performed during the time period of detecting muons that traverse through the at least a part of the heap and interact with the muon detector.

4. The method of any one of claims 1 to 3 or any other claim herein comprising repeating the steps of claim 1 over a plurality of time periods to determine temporal 3D profiles of saturation and porosity of the heap, the temporal profiles comprising the3D profiles of saturation and porosity of the heap determined for each time period.

5. The method of claim 4 or any other claim herein wherein the plurality of time periods comprises a plurality of overlapping time periods.

6. The method of claim 4 or any other claim herein wherein the plurality of time periods comprises a plurality of sequential time periods.

7. The method of claim 4 or any other claim herein wherein the plurality of time periods comprises a plurality of temporally spaced apart time periods.

8. The method of any one of claims 1 to 7 or any other claim herein wherein the plurality of muon-detection locations comprises one or more of: within one or more monitoring wells in the heap; within one or more trenches formed on a sloped side or terrace of the heap; within one or more boreholes adjacent to the heap; within one or more drainage pipes located on a liner of the heap leach operation; within one or more pipes or trenches embedded within the heap; within one or more pipes located on a periphery of the heap; within one or more pipes located underneath the liner; and, adjacent to the heap.

9. The method of claim 8 or any other claim herein comprising installing, at each of the plurality of muon-detection locations adjacent to the heap, a panel muon detector oriented along a direction that results in relatively high acceptance of muons traversing substantially horizontally through the heap when compared to muons traversing the heap in other directions.

10. The method of any one of claims 1 to 9 or any other claim herein wherein performing the tomographic data inversion is based at least in part on additional information comprising one or more of: measured gravity data measured at locations in, on or adjacent to the heap; and, measured temperature data measured at locations in, on or adjacent to the heap.

11. The method of any one or claims 1 to 10 or any other claim herein wherein performing the tomographic data inversion is based at least in part on priorinformation, the prior information obtained from a prior execution of performing the tomographic data inversion.

12. The method according to any one of claims 1 to 11 or any other claim herein wherein performing the tomographic data inversion is based at least in part on prior information comprising one or more of: a prior 3D resistivity model of the heap obtained from a prior execution of performing the tomographic data inversion; a prior 3D density model of the heap obtained from a prior execution of performing the tomographic data inversion; a prior 3D saturation model of the heap obtained from a prior execution of performing the tomographic data inversion; and a prior 3D porosity model of the heap obtained from a prior execution of performing the tomographic data inversion.

13. The method according to any one of claims 1 to 12 or any other claim herein wherein performing the tomographic data inversion is based at least in part on an additional estimated porosity profile of the heap, the additional estimate porosity profile based at least on an assumed depth relationship from compactification of material under weight of stacking.

14. The method of any one of claims 2 to 13 or any other claim herein wherein generating the 3D model of resistivity of the heap and the 3D model of density of the heap comprises determining a resistivity value and a density value for each of a plurality of 3D voxels in the heap.

15. The method of claim 14 or any other claim herein wherein, for each voxel, the resistivity value comprises a resistivity distribution and the density value comprises a density distribution.

16. The method of any one of claims 14 to 15 or any other claim herein wherein determining corresponding saturation and porosity values based on the 3D models of resistivity and density comprises, for each of the plurality of 3D voxels, computing a saturation value and a porosity value based on the corresponding resistivity value and density value of the 3D voxel.

17. The method of claim 16 or any other claim herein wherein, for each voxel, the saturation value comprises a saturation distribution and the porosity value comprises a density distribution.

18. The method of any one of claims 1 to 17 or any other claim herein wherein performing the tomographic data inversion comprises determining, for each of a plurality of 3D voxels in the heap, a corresponding saturation value and a corresponding porosity value.

19. The method of claim 18 or any other claim herein wherein, for each voxel, the saturation value comprises a saturation distribution and the porosity value comprises a density distribution.

20. A system for modelling a heap of a heap leach operation, the system comprising:a plurality of spaced apart voltmeters distributed on, within, and / or adjacent to, the heap, each voltmeter measuring electrical potential across pairs of electrodes located at a corresponding resistivity-detection location;a plurality of spaced apart muon detectors distributed in, or in a vicinity of the heap leach operation, each muon detector detecting muons that traverse through at least a part of the heap and interact with the muon detector over a time period at one or more corresponding muon detection locations; and,a processor configured to:determine, based at least in part on the measured electrical potential, measured resistivity data at each of the resistivity-detection locations;determine, from the muon interactions, measured directional muon intensities for a plurality of directions intersecting at each of the muon detection locations; andperform tomographic data inversion based at least in part on the measured resistivity data and the measured directional muon intensities to generate a 3D profile (e.g. computer-based model) of saturation of the heap and a 3D profile (e.g. computer-based model) of porosity of the heap.

21. A system according to claim 20 comprising any of the features, combinationsof features or sub-combinations of features of any one of claims 1 to 15.

22. Apparatus having any new and inventive feature, combination of features, or sub-combination of features as described herein.

23. Methods having any new and inventive steps, acts, combination of steps and / or acts or sub-combination of steps and / or acts as described herein.

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