Active hyperspectral mineralogical sensing for mining

Abstract

In accordance with an example aspect of the present invention, there is provided a method of active hyperspectral mineralogical sensing during raw ore mining under in-situ conditions, where the method includes irradiating a point on a surface of a mining material with a first broadband light signal having an irradiation range within a wavelength range of about 1350 nm to about 2500 nm, the first broadband light signal having an irradiation range within a wavelength range of about 1350 nm to about 2500 nm, to enable determination of information relating to at least one water-related absorption characteristic, carbonate-related absorption characteristic or hydroxide-related absorption characteristic; receiving a reflected version of the first broadband optical signal, where the reflected version of the first broadband optical signal is a reflection from the mining material at a different wavelength within an illumination range of the first broadband optical signal; determining a reflection spectrum for the point of the mining material based on the reflected version of the first broadband light signal; and further determining the information relating to at least one water-related absorption characteristic, carbonate-related absorption characteristic or hydroxide-related absorption characteristic based on the reflection spectrum; and performing qualitative and quantitative mineralogical analysis based on the information relating to at least one water-related absorption characteristic, carbonate-related absorption characteristic or hydroxide-related absorption characteristic.

Description

TECHNICAL FIELD

[0001] Various exemplary embodiments relate generally to mineralogical analysis, and more particularly to enabling hyperspectral mineralogical sensing, including qualitative and quantitative mineralogical analysis and mapping in situ during the mining of raw ores, including in a mining environment. BACKGROUND

[0002] Non-renewable natural resources play a vital role in the sustainable development of modern society. The mission of mining companies is to meet the resource needs of society while keeping mining projects profitable. As existing ore deposits deplete and new ore deposits are more complex to develop, mining companies face increasingly higher production costs associated with low-grade ores. The only viable and controllable response to this challenge is to improve recovery efficiency. Therefore, effective models supported by mineralogical sensing solutions are expected to be applied to extract and process mined materials. SUMMARY

[0003] According to some aspects, the subject matter of the independent claims is provided. Some embodiments are defined in the dependent claims.

[0004] According to a first aspect of the present invention, there is provided a method for active hyperspectral mineralogical sensing during the mining of raw ores, comprising: illuminating a point on a surface of a mined material using a first broadband light signal having an illumination range in a wavelength range of about 1350 nm to about 2500 nm to enable determining information related to at least one water-related absorption feature, a carbonate-related absorption feature, or a hydroxide-related absorption feature; receiving a reflected version of the first broadband light signal, wherein the reflected version of the first broadband light signal is from a reflection of the mined material at different wavelengths of the illumination range of the first broadband light signal; determining a reflectance spectrum of the point of the mined material based on the reflected version of the first broadband light signal; and determining the information related to at least one water-related absorption feature, a carbonate-related absorption feature, or a hydroxide-related absorption feature based further on the reflectance spectrum; and performing qualitative and quantitative mineralogical analysis based on the information related to at least one water-related absorption feature, a carbonate-related absorption feature, or a hydroxide-related absorption feature.

[0005] According to a second aspect of the application, there is provided an active hyperspectral mineralogical sensing system comprising: at least one light source arranged to illuminate a point on a surface of a mining material using a first broadband light signal having an illumination range in a wavelength range of about 1350 nm to about 2500 nm to enable determination of information relating to at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature; at least one spectrometer arranged to receive a reflected version of the first broadband light signal, wherein the reflected version of the first broadband light signal is from a reflection of the mining material at different wavelengths of the illumination range of the first broadband light signal; and at least one processor configured to: determine a reflectance spectrum of the point of the mining material based on the reflected version of the first broadband light signal, determine the information relating to the at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature based on the reflectance spectrum; and perform a mineralogical analysis based on the information relating to the at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature.

[0006] According to a third aspect of the application, there is provided a computer program comprising instructions which, when the program is executed by a device, cause the device to carry out the method according to the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 An example of a mining production flow is shown according to at least some embodiments of the application;

[0008] Figure 2 Development of a mining deposit based on a block model is shown according to at least some embodiments of the application;

[0009] Figure 3 Examples of reflectance spectra of minerals having iron-related absorption features, carbonate-related absorption features and hydroxide-related absorption features are shown to allow mineralogical identification, analysis and mapping according to at least some embodiments of the application;

[0010] Figure 4 A flowchart of an active hyperspectral mineralogical sensing method comprising qualitative and quantitative mineralogical analysis and mapping is shown according to at least some embodiments of the application;

[0011] Figure 5A A first example system for implementing active hyperspectral mineralogical sensing is shown according to at least some embodiments of the application;

[0012] Figure 5BA second example system for implementing active hyperspectral mineralogy sensing is shown, in accordance with at least some embodiments of the present application;

[0013] Figure 6 An example system for implementing active hyperspectral mineralogy mapping is shown, in accordance with at least some embodiments of the present application;

[0014] Figure 7 A flowchart of qualitative and quantitative mineralogy analysis is shown, in accordance with at least some embodiments of the present application;

[0015] Figure 8 An example mineralogy map of raw materials in an underground mine is shown, in accordance with at least some embodiments of the present application;

[0016] Figure 9 An example device capable of supporting at least some embodiments of the present application is shown;

[0017] Figure 10 A method in accordance with at least some embodiments of the present application is shown. DETAILED DESCRIPTION

[0018] The mining process can be represented by the following segments: mine exploration and planning; production; closure and reclamation. Production can be divided into a Run-Of-Mine stage and a processing plant stage. The Run-Of-Mine stage can include mining raw mineral material and transporting it by truck, followed by any form of treatment. The processing plant stage can include extracting ore from raw material.

[0019] In some example embodiments of the present disclosure, sensing can be part of the mining process. For example, sensing can have been applied during the mine exploration and planning stage. For example, during the exploration stage, exploration drilling can be applied, where samples are taken from deep below the surface. Once the drilling core samples are collected and prepared, the samples can be analyzed in a controlled environment of a laboratory using various sensing methods, including for example X-ray fluorescence (XRF) and X-ray diffraction (XRD) scanners for elemental analysis and Fourier transform infrared (FTIR) sensors and hyperspectral cameras for mineralogy analysis. The end product of this stage is a block model, which can be based on the results of the drilling core analysis and represents the location and parameters of the ore body. Next, the block model can guide the development and excavation activities during the Run-Of-Mine stage.

[0020] Once raw materials are extracted during primary mining, they can be supplied to a processing plant. Here, another opportunity for sensing applications may arise, allowing for ore sorting and measuring data that can help set appropriate material processing parameters. At this point, the raw materials may be pulverized and presented as a single layer of released particles, allowing for the application of a wide range of sensing methods. Similarly, the processing plant provides a controlled environment that allows for accurate assessment of the raw materials. For example, at this stage, pneumatic sensor-based sorters can be utilized, using different sensor types such as XRF, FTIR, color, and hyperspectral sensors to cover a wide selection of elements and minerals.

[0021] While sensing can be applied during exploration and processing activities, sensing during the run-of-mine phase can be extremely challenging, for example, if it is desired to perform sensing during the run-of-mine phase without requiring special material preparation and a controlled environment.

[0022] The collection of mining operations can include face drilling and blasting, extracting materials with shovels or underground loaders, transporting them using mining trucks, and separate storage to manage the quality of input materials for processing plants. All operations can be based on the input of the block model. Exploration drilling of cores and their laboratory analysis can be expensive, so the intervals between locations or drillings can be large, and the block model can have limited accuracy. Lack of sensing capabilities can lead to blind spots in the primary mining stage. Without real-time sensing input, mining operations may produce errors. Such errors may have a significant impact on processing operations. Once materials with the wrong ore grade or mineral composition (content) are mixed in during primary mining, it may be difficult or even impossible to repair such errors.

[0023] For example, a mining company may desire real-time input during raw ore extraction to improve stockpile management decisions. Mining can be characterized by a harsh measurement environment with long measurement distances, unstable ambient lighting, and challenging humidity conditions. Sensors may need to be located outdoors, at distances of up to tens of meters from the raw materials being extracted. Similarly, at least some mines may operate 24 / 7, at night, and on rainy or cloudy days. Thus, embodiments of the present disclosure enable active sensing technology to provide accurate measurement data under all possible conditions.

[0024] From an elemental sensing perspective, active sensing can be provided, for example using XRF and laser-induced breakdown spectroscopy (LIBS). Both techniques can be considered active. XRF applies X-rays to a sample, while LIBS uses short laser pulses to generate microplasmas on the sample surface.

[0025] Mineralogical sensing, including mineralogical identification, analysis and mapping, can detect minerals that have a negative impact on milling, metallurgical processing or occupational health, such as talc, swelling clays, carbonates, asbestos. Likewise, mineralogical data can also be applied to ore grade estimation, either based on direct detection of ore minerals (such as oxides of iron and nickel), or based on detection of alteration minerals that can be used as an indicator of ore grade information.

[0026] With near real-time ore grade and mineralogical monitoring, existing block models can be updated and improved, improving planning applications. For example, separation of ore and gangue during loading operations can be improved, haulage operations can be optimized to reduce gangue, and the quality and accuracy of stockpile operations can be improved. As a result, the value of all further processing operations can be realized to provide inputs to assist in controlling autonomous mining equipment.

[0027] Accordingly, embodiments of the present disclosure enable mineralogical analysis during run-of-mine mining. Hyperspectral data can be used to detect subtle mineralogical variations and map their distribution across the surface of the mined material, providing a new generation of mineralogical information to mine geologists and metallurgists at the earliest stages of excavation. The mined material can be the material on the surface of the mine face, or the raw material excavated after blasting, during haulage or storage.

[0028] More specifically, embodiments of the present disclosure enable active hyperspectral mineralogical sensing. Active hyperspectral mineralogical sensing can be enabled by combining laser-based active illumination and reflected light hyperspectral data acquisition. The active illumination can be based on a set of monochromatic lasers or supercontinuum lasers, or a combination of both. In some example embodiments, collimated laser light can be utilized to allow long-range measurements under any ambient light conditions, providing robust mineralogical sensing during run-of-mine mining.

[0029] Figure 1 An example of a mining production flow is shown, in accordance with at least some embodiments of the present disclosure. More specifically, Figure 1 An example of a process that generally describes surface and / or underground mining is shown, in accordance with at least some embodiments of the present disclosure. At step 100, exploration drilling can be performed as part of the mining and planning activities. Next, a block model 110 can be developed.

[0030] Steps 120 through 170 can constitute a first portion of the mining process, referred to as mine extraction. The mine extraction portion can include the extraction of raw mineral material and the transport of these materials in trucks before any type of processing is performed. At step 120, a rock face can be drilled and blasted to break up the rock and make it easier to transport. The resulting pile of rock after blasting can be referred to as a muck pile. At step 130, the muck pile can be loaded into trucks using a shovel machine, excavator, and / or an underground loader. Then, at step 140, the hauling of the loads can be performed. Steps 150, 160, and 170 can implement a storage process, including storage management based on the orebody model 110 at step 150, and possible mineralogical sensing data input during the raw mine extraction at step 160. As a result of the storage management at step 150, a decision can be made to dispatch a haul truck to a particular storage pile. At step 170, the material is unloaded from the truck to a particular storage pile 171-173. Next, the raw material from the storage pile can be moved to a processing plant 180, where the second portion of the process can begin. Here, multiple steps of the mining material processing can be performed to extract the ore and the elements of interest, such as crushing, grinding, flotation, oxidation, leaching, and absorption.

[0031] The total amount of mined material can be separated into high grade and low grade material or ore (containing the element of interest) and waste (not containing the element of interest in an economically significant amount). Ore sorting is an important part of mining, including sorting by grade, particle size, and mineralogy in many different mineral processes. In general, ore grade sorting enables the removal of gangue at an early stage in the process, saving the mining company effort while reducing the impact on the environment.

[0032] During the primary mining, ore sorting can be performed by storage. Storage can be a process in which the mined material is separated into different storage piles taking into account economic, technical and geological metallurgical parameters. The mine exploitation storage piles formed in step 170 can be seen as an important part of the mining value chain as they can be used as temporary storage of raw material. There are at least two reasons for performing storage. From the perspective of ore grade, this allows improving the average grade of the ore processed by the plant, thus maximizing the recovery of the elements of interest. For example, material from high grade storage piles can be processed first. Likewise, material from different storage piles can be mixed to ensure a certain quality of the feed material. Finally, in case of fluctuations in the raw material delivery rate, storage helps to ensure a stable feed rate. From the perspective of mineral composition, storage can provide an opportunity to divert or bypass materials that are harmful to the processing plant equipment, thus reducing the use of water, electricity and reagents. Likewise, different ore-bearing minerals can require different processing flows and can be stored separately. Both ore grade and mineral composition are taken into account to decide whether processing of raw material is economically feasible at the current time. In step 150, a decision is made about separating the raw material into different storage piles. The decision can be based on the block model and can be supported by possible mineralogical sensing data input during the primary mining in step 160.

[0033] Figure 2 Development of a mining deposit using a block model is shown. More specifically, Figure 2 An example of mineral deposit development using a block model is shown.

[0034] The shape of the pit 200 can be developed according to the block model 210. The block model 210 can be a simplified representation of the ore body 211 and its surrounding environment, which is shown as a stack of computer-generated cells 212 to 214, which represent small volumes of rock in the deposit. Ore blocks with high grade are labeled 212, ore blocks with low grade are labeled 213, and gangue blocks are labeled 214.

[0035] Each cell can contain data estimates such as element grade, density, and other geologic or engineering entity values. Prior to development of the mine, the grade, location, shape, and mineralogy of the ore body 211 can be assessed as part of the exploration core drilling of 221 and 222. Exploration core drilling and core lab analysis can be costly, and therefore the spacing 223 between drilling locations can be large, reaching tens or even hundreds of meters, which will limit the ore block model resolution and the quality of the stockpile management decisions. The lack of real-time sensing input to the run-of-mine extraction can result in blind spots and stockpile errors. Such errors can have a significant impact on the processing plant operations. In some embodiments, the accuracy of the ore block model and the quality of the stockpile decisions during run-of-mine extraction can be improved by direct real-time active hyperspectral mineralogy sensing of the run-of-mine material’s heaps during the run-of-mine extraction 160.

[0036] Figure 3 Examples of reflectance spectra of minerals with iron-related absorption features, water-related absorption features, carbonate-related absorption features, and hydroxide-related absorption features are shown to allow for mineralogical identification, analysis, and mapping according to at least some embodiments of the present invention. More specifically, Figure 3 Examples of reflectance spectra of some minerals 300, such as muscovite, hematite, and epidote, in the visible, near-infrared, and shortwave infrared wavelength ranges are shown. Hyperspectral mineralogy sensing, including qualitative and quantitative mineralogy analysis, can be performed with a collection of absorption features in the visible and near-infrared range 310 portion and the shortwave infrared range 320 portion. The portion in the visible and near-infrared range can be, for example, between about 400 nm and about 1100 nm to enable determination of information related to, for example, iron-related absorption features. Iron-related absorption features can be related to absorption features of iron ions and iron hydroxides. This information can be applicable to the identification of ore-bearing minerals. The portion in the shortwave range can be, for example, between about 1350 nm and about 2500 nm to enable determination of said information related to, for example, water-related absorption features, carbonate-related absorption features, and hydroxide-related absorption features. Water-related absorption features consist of O-H related absorption bands of free water, for example, water molecules not part of the mineral crystal structure, or hydrous minerals, for example, water molecules within the crystal structure of a mineral, such as CaS04.2H20. Carbonate-related absorption features can be related to absorption bands of C-O. Hydroxide-related absorption features can be related to absorption bands of Al-OH, Mg-OH, Fe-OH. This information can be applicable to the identification of gangue minerals, such as phyllosilicates and water content of the mining material surface.

[0037] Figure 4A flowchart of an active hyperspectral mineralogical sensing method including qualitative and quantitative mineralogical analysis and mapping according to at least some embodiments of the present application is shown. The active hyperspectral mineralogical sensing method 400 can be performed during the mining of raw ores following the procedures described herein. More specifically, the mineralogical analysis can be performed by using active hyperspectral mineralogical sensing to enable remote mineralogical sensing under any ambient light conditions, e.g. in the mine.

[0038] Steps 411 to 414 can constitute a first part of the method, in which a reflection spectrum is captured for a single point on the surface of the mining material. In step 411, a broadband light signal can be generated, e.g. using a supercontinuum laser or a monochromatic laser bank. The application of a laser is required for active hyperspectral mineralogical sensing, as a laser can be collimated into a beam with low divergence, allowing the illumination of the mining material from a distance. For the illumination of the visible and near-infrared range 310 part shown in Fig. 3, a supercontinuum laser can be applied. Alternatively or additionally, due to the widespread availability of monochromatic lasers for the visible and near-infrared wavelength range and the broad characteristics of the spectral features within the range 310, a monochromatic laser bank can be applied. For the illumination of the shortwave infrared range 320 part shown in Fig. 3, a supercontinuum laser can be required, as the availability of lasers with high optical power in the order of several watts is limited for the wavelength range of about 2300 nm to 2500 nm. Figure 3 Figure 3 For the illumination of the shortwave infrared range 320 part shown in Fig. 3, a supercontinuum laser can be required, as the availability of lasers with high optical power in the order of several watts is limited for the wavelength range of about 2300 nm to 2500 nm.

[0039] In step 412, the point on the surface of the mining material can be illuminated, e.g. at an appropriate time, using a first broadband light signal having an illumination range within the part of the visible and near-infrared range 310 to enable the determination of information related to at least one iron-related absorption feature, or having an illumination range within the part of the shortwave infrared range 320 to enable the determination of information related to at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature, or having an illumination range within the combination of both ranges 310 and 320.

[0040] In step 413, a reflected version of the broadband light signal can be acquired, which is the reflection of the point of the mining material at different wavelengths within the illumination range of the broadband light signal.

[0041] In step 414, the reflection spectrum of the point of the mining material within the illumination range of the broadband light signal can be determined. In step 414, a correction of the reflection spectrum can be performed using, e.g., a pre-recorded reflection spectrum of a calibration target.

[0042] ​At step 415, a point-by-point scan of the plurality of points on the surface of the mining material can be performed according to the predetermined scan pattern to determine a plurality of reflectance spectra for each point of the mining material. That is, a separate scan can be performed for each point of the mining surface. For example, a point-by-point scan of the plurality of points on the surface of the mining material can be performed, wherein the scan comprises illuminating each point of the mining material with the first broadband light signal and receiving a reflected version of the first broadband light signal.

[0043] At step 416, a hyperspectral cube comprising a combination of all reflectance spectra and corresponding coordinates can be generated according to the predetermined scan pattern.

[0044] At step 417, mineral qualitative analysis and mapping (e.g., according to steps 706 and 707 shown in FIG. 7) and quantitative analysis and mapping (e.g., according to steps 708 and 709 shown in FIG. 7) can be performed, thereby generating results of the mineralogical analysis, including information about the identified minerals and their concentrations, and a mineral map, e.g., according to step 710 shown in FIG. 7, performing a mineral map across the surface of the mining material. At step 417, Figure 7 Figure 7 Figure 7 Figure 7 The set of methods and algorithms shown in FIG. 7 can be applied to mineralogical identification, concentration measurement, and mapping based on at least one iron-related absorption feature, water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature. In some embodiments, by applying the pre-calibrated machine learning classifier at step 707 shown in FIG. 7 and the pre-calibrated quantitative model at step 709 shown in FIG. 7 to the hyperspectral cube and at least one iron-related absorption feature, water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature, the results can be presented as an identified mineral composition and average mineral concentration across the surface of the mining material. In some embodiments, by applying the pre-calibrated machine learning classifier at step 707 shown in FIG. 7 and the pre-calibrated quantitative model at step 709 shown in FIG. 7 to the hyperspectral cube and at least one iron-related absorption feature, water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature, a mineral distribution map across the surface of the mining material can be formed at step 710 shown in FIG. 7. Figure 7 Figure 7 Figure 7 Figure 7 Figure 7

[0045] ​​​​​​​​At step 418, the results of the mineral qualitative and quantitative analysis can be transmitted to the mining equipment for application during the mining and exploration activities. In some embodiments, the identified mineral composition and average mineral concentration across the surface of the mined material can be applied to improve loading, handling and storage operations during the run-of-mine mining and / or to assist in the ore block modeling. In some embodiments, the mapping of the mineral distribution across the surface of the mined material can be applied to improve the exploration activities.

[0046] In some embodiments, a portion of the broadband light signal generated at step 411 can be separated prior to the irradiation 412 of the points on the mined material. This portion of the broadband light signal can be applied to a reference measurement 421 of the broadband light signal spectrum. At step 414, the reference measurement spectrum can be applied as part of the reflection spectrum correction. For example, the correction can be performed by dividing the reflection spectrum at step 414 by the reference measurement spectrum. Flence, fluctuations in the spectral shape of the broadband light signal generated at step 411 can be eliminated.

[0047] The use of the reference measurement 421 of the broadband light signal at step 414 to correct the reflection spectrum can allow to take into account deviations in the spectral shape of the broadband light signal generated at step 411. Deviations in the spectral shape of the broadband light signal can be due to different environmental conditions, such as temperature, humidity, pressure, aging of the broadband light signal. The reference measurement 421 of the broadband light signal spectrum can be particularly important in case at least one supercontinuum laser is applied to generate the broadband light signal at step 411, as the nonlinear characteristics of the supercontinuum generation process can lead to fluctuations in the spectral shape of the broadband light signal.

[0048] In some embodiments, active hyperspectral mineralogical mapping can be performed using a combination of two systems operating within a wide range of wavelengths, such as in the visible and near-infrared (400 nm to 1100 nm) and shortwave infrared (1350 nm to 2500 nm) optical ranges. Flence, embodiments of the present invention enable active hyperspectral mineralogical sensing, including remote qualitative and quantitative analysis and mapping under any ambient light conditions. Such mineralogical analysis and mapping can be performed during the run-of-mine mining. The system can be operated both in open pit mines and in underground mines. The mineralogical analysis and mapping can be performed using at least one supercontinuum laser based irradiation system.

[0049] In some embodiments, supplementary measurements 431 of distance information using Light Detection and Ranging (LIDAR), humidity and / or pressure can be performed and applied at step 417 for correction.

[0050] Figure 5AA first example system for implementing active hyperspectral mineralogy sensing according to at least some embodiments of the present application is shown. The example active hyperspectral mineralogy sensing system 500 can comprise at least one light source 510, at least one spectrometer 512, at least one optical illumination scheme 511, at least one optical collection scheme 513, and at least one device 515. The active hyperspectral mineralogy sensing system 500 can be used for illuminating and determining mineralogical information about a mining material 540, more specifically about a point 542.

[0051] The device 515 can be a processing unit or data processing board, containing for example an application-specific integrated circuit (ASIC), a system on a chip (SoC), a field-programmable gate array (FPGA), a single-board computer (SBC), or a microprocessor, and be configured to control the at least one light source 510 and the at least one spectrometer 512. The device 515 can be connected to the light source 510 and / or the spectrometer 512, respectively, by a wired connection. The device 515 can generally be a control means. The device 515 can be a means for performing Figure 4 and Figure 7 the method steps shown.

[0052] For example, at least one processor of the device 515 can be configured to cause the first broadband light source 510 to illuminate the point 542. At least one processor of the device 515 can be configured to cause the illumination by sending a command to the first broadband light source 510 such that dynamic operation and power saving can be achieved without the need to illuminate the target.

[0053] The command can be a command to the first broadband light source 510 indicating that it needs to turn on its light generation, for example for a certain time period. Power saving is particularly beneficial for applications where it is not possible to provide the active hyperspectral mineralogy sensing system 500 with a connection to a fixed electrical power network. For example, battery-powered devices can be used in mines, since mines are usually located in remote areas where it is not possible or appropriate to provide a connection to a fixed electrical power network. Therefore, a power-efficient solution is preferred.

[0054] In some embodiments, a supercontinuum laser can be used as the first light source 510. Using a supercontinuum laser as a light source 510 to illuminate the point 542 of the mining surface 540 allows to have only one light source for a wide range of wavelengths, for example from about 1350 nm to about 2500 nm. At the same time, the light of a supercontinuum laser can be collimated, allowing for remote illumination of up to several hundred meters. Furthermore, supercontinuum lasers are characterized by high optical power of several watts. All these parameters are important for remote sensing in mining applications.

[0055] The first light source 510 can be arranged to illuminate a point 542 of the mining face 540 with a first broadband light signal 516. The first broadband light signal 516 can have Figure 3 an illumination range within a portion of the shortwave infrared range 320 shown.

[0056] The first spectrometer 512 can be arranged to receive a reflected version 517 of the first broadband light signal 516, wherein the reflected version 517 of the first broadband light signal 516 is a reflection of the point 542 of the mining material 540 at different wavelengths within the illumination range of the first broadband light signal 516. For example, the at least one processor of the device 515 can be configured to cause the receiving of the reflected version 517 of the first broadband light signal 516. The at least one processor of the device 515 can be configured to cause said receiving by sending a command to the first spectrometer 512 to enable dynamic operation without the need to receive from the target and further to save power. The command can be a command to the first spectrometer 512 indicating that it needs to turn on its light acquisition.

[0057] The optical scheme 511 can be applied to collimate or focus the first broadband light signal 516 of the first broadband light source 510, thereby allowing the first broadband light signal 516 to be delivered from a distance (e.g. 2 to 30 meters) to the point 542 of the mining material 540. The optical scheme 513 can be used to collect the reflected version 517 of the first broadband light signal 516 from the point 542 of the mining material 540 at different wavelengths within the illumination range of the first broadband light signal 516.

[0058] The device 515 can be configured to determine a reflectance spectrum of the point 542 of the mining face or raw material 540 based on the reflected version 517 of the first broadband light signal 516, and determine said information related to the at least one water-related absorption feature, the carbonate-related absorption feature, or the hydroxide-related absorption feature based on the reflectance spectrum. For example, the at least one processor of the device 515 can be configured to determine a reflectance spectrum of the point 542 of the mining face or raw material 540 based on the reflected version 517 of the first broadband light signal 516, and determine said information related to the at least one water-related absorption feature, the carbonate-related absorption feature, or the hydroxide-related absorption feature based on the reflectance spectrum.

[0059] The device 515 can be configured to perform a qualitative and quantitative mineralogical analysis of the single point 542 of the mining material 540 using Figure 7 the set of methods and algorithms shown in

[0060] In some embodiments, the active hyperspectral mineralogical sensing system 500 can comprise a spectrometer 514 for determining a reflectance spectrum of the point 542 of the mining face 540 based on the reflected version 517 of the first broadband light signal 516. Figure 4a reference measurement of a spectrum of a portion of the first broadband light signal 516 in step 421. The reference measurement spectrum can be used as a basis for determining the information related to the iron-related absorption feature in accordance with Figure 4 a correction of a reflected spectrum of the reflected light 517 received in the device 515 in step 414.

[0061] Figure 5B A second example system for implementing active hyperspectral mineralogy sensing is shown in accordance with at least some embodiments of the present application. Figure 5B The hyperspectral mineralogy sensing system 505 shown can include a second broadband light source 520, a second spectrometer 522, a second device 525, and an optical scheme 521, an optical scheme 523 to collimate or focus a second broadband light signal 526 and collect a reflected version 527 of the second broadband light signal 526 at different wavelengths within an illumination range of the second broadband light signal 526 from a point 542 of the mining material 540.

[0062] The second broadband light source 520 can be arranged to illuminate the point 542 of the mining face or raw material 540 with the second broadband light signal 526 having an illumination range within a portion of the visible and near infrared range 310. The portion of the visible and near infrared range can be between about 400 nm to about 1100 nm, for example between about 400 nm to about 1100 nm. In some embodiments, supercontinuum illumination can be used. In some embodiments, the second broadband light source 520 can include a monochromatic laser to operate within the visible and near infrared range. That is, the active hyperspectral mineralogy sensing system 505 can operate using a monochromatic laser within the visible and near infrared range. In some embodiments, the hyperspectral mineralogy sensing system 505 can operate using a set of monochromatic lasers within the visible and near infrared range.

[0063] The second spectrometer 522 can be arranged to receive the reflected version 527 of the second broadband light signal 526, wherein the reflected version 527 of the second broadband light signal 526 is a reflection of the point 542 of the mining face or raw material 540 at different wavelengths within the illumination range of the second broadband light signal 526. The device 525 can be configured to determine a reflected spectrum of the point 542 of the mining face or raw material 540 based on the reflected version 527 of the second broadband light signal 526, and determine the information related to the iron-related absorption feature based on the reflected spectrum.

[0064] Figure 6 An example system for implementing active hyperspectral mineralogy mapping is shown in accordance with at least some embodiments of the present application. The system 600 can be applied to spatially scan a plurality of points on the mining material 540. Figure 6 The system 600 on the left includes, in addition to the device 515, Figure 5BThe system 505 in the above includes, in addition to the parts of the system 505, a pan-tilt unit 602 for two-dimensional scanning of the mining material 540. The steps 411-414 of the method can be repeated for multiple points on the mining face or raw material 540, and the multiple reflectance spectra and coordinates according to a predetermined scanning pattern can be fused for the multiple points to generate a hyperspectral cube of the mining face or raw material 540.

[0065] Figure 6 The right side shows an example of an implementation of a 2D scanning active hyperspectral mineralogical mapping system 605 for spatial scanning of multiple points on the mining face or raw material 540. The system 605 includes an active hyperspectral mineralogical sensing unit 610 comprising at least one light source, at least one spectrometer, at least one optical scheme for collimating or focusing a broadband light signal, at least one optical scheme for collecting a reflected version of the broadband light signal from a point of the mining material at different wavelengths within the illumination range of the broadband light signal. The optical schemes are protected by optical windows 611, 612. In some embodiments, the optical schemes can have one optical axis and can be protected using one optical window. The system 605 includes a pan-tilt unit 620 for moving the active hyperspectral mineralogical sensing unit 610 in pan-tilt directions and for spatial scanning of multiple points on the mining face or raw material surface and generating a hyperspectral cube.

[0066] Figure 7 A flowchart of a qualitative and quantitative mineralogical analysis 700 according to at least some embodiments of the present application is shown. Figure 6 The system 600 in the above can acquire a hyperspectral cube containing reflectance spectra from multiple points of the mining material at step 701. Two dimensions of the hyperspectral cube represent the surface of the mining material and the third dimension represents the reflectance spectra. As a reference for the qualitative analysis, a hyperspectral cube containing reflectance spectra from multiple points of pure minerals can be pre-recorded with the system 600 at step 702. As a reference for the quantitative analysis, a hyperspectral cube containing reflectance spectra from multiple points of reference mineral samples of known concentrations can be pre-recorded with the system 600 at step 703.

[0067] The collection of hyperspectral cubes 701, 702, 703 can be submitted to a background removal algorithm 704, for example, to set a threshold on the pixel score of a principal component analysis (PCA) for selecting a region of interest (ROI) and removing outliers in the reflectance spectrum, such as stray material, edges, and bad points on the rock surface, such as detector saturation areas. The reflectance spectra of the points included in the ROI can be subjected to spectral smoothing (e.g., Savitzky-Golay) and a baseline correction algorithm 705 (e.g., asymmetric least squares baseline correction) to minimize noise and spectral baseline distortion caused by variations in sample geometry and surface roughness.

[0068] In some embodiments, a qualitative analysis can be performed. For qualitative analysis, the reflectance spectrum corrected in step 705 (the reflectance spectrum comes from the hyperspectral cube containing the reflectance spectra of pure minerals obtained in step 702) can be used to calibrate a machine learning classifier (e.g., nearest neighbor (k-NN)) in step 706. Subsequently, in step 707, by applying a machine learning classifier, the reflectance spectrum of each point in the corrected hyperspectral cube 701 of the mine face or raw material surface can be assigned to one or several minerals in the set of pure minerals 702. In step 710, a qualitative mineral map can be generated based on the coordinates from the hyperspectral cube. Qualitative mapping may not require prior information about unknown samples and can be used for exploration purposes, such as screening which minerals are present at a certain location and improving the ore block model.

[0069] In some embodiments, a quantitative analysis can be performed. For quantitative mineral mapping, a hyperspectral cube 703 containing reflectance spectra of reference mineral samples with known target mineral concentrations (mineral #1, mineral #2, mineral #3) can be used to calibrate one or more univariate (peak absorbance values ​​at a single wavelength) or multivariate quantitative models, such as partial least squares (PLS), in step 708. The concentrations of all target minerals (mineral #1, mineral #2, mineral #3) at each point in the calibrated hyperspectral cube 701 at the mine face or raw material surface can be predicted by applying a quantitative model 709, thereby generating a quantitative distribution map. In step 710, a quantitative mineral map can be generated. Quantitative mapping may require prior information about the mineral composition in a mining site and can be used to estimate the concentration of specific minerals of interest for mining purposes, for example, to support stockpiling decisions and improve the accuracy of ore block models during run-of-mine mining.

[0070] In some embodiments, the system 500 and the system 505 from FIG. 5 or the system 506 from FIG. Figure 6The system 600 and the system 605 in the mining material can acquire the reflection spectrum from a single point on the surface of the mining material. Then, the single point can be subjected to qualitative and quantitative mineralogical analysis by combining the reference spectrum obtained in the steps 705 to 709 and the steps 702 and 703.

[0071] Figure 8 An example of a mineralogical mapping of raw materials in an underground mine 800 according to at least some embodiments of the present application is shown. The raw materials according to the photo 810 can be located in a mining location of an underground mine. A surface portion of the raw materials 811 can be remotely scanned according to the steps 411 to 416 using the active hyperspectral mineralogical mapping system 600. The acquired hyperspectral cube containing reflection spectra from multiple points of the raw materials can be rendered as a false color image 820 and the mineralogical information 831 can also be overlaid on the false color picture to form a mineral map 830.

[0072] Figure 9 An example device capable of supporting at least some embodiments of the present application is shown. A device 900 is shown, which can include Figure 5A and Figure 5B the device 515 or the device 525 of Figure 5A and Figure 5B the device 515 or the device 525 of

[0073] The device 900 can include a processing unit, i.e. a processing element 910, which can also include, for example, a single-core processor or a multi-core processor, wherein a single-core processor includes one processing core and a multi-core processor includes more than one processing core. The processing unit 910 can generally include a control device. The processing unit 910 can include one or more processors. The processing unit 910 can be a control device. The processing unit 910 can include at least one application-specific integrated circuit (ASIC). The processing unit 910 can include at least one field-programmable gate array (FPGA). The processing unit 910 can include at least one single-board computer (SBC). The processing unit 910 can be a device for executing method steps in the device 900. The processing unit 910 can be configured at least partly by computer instructions to perform actions.

[0074] The device 900 can comprise a memory 920. The memory 920 can comprise a random access memory (RAM) and / or a persistent memory. The memory 920 can comprise at least one RAM chip. For example, the memory 920 can comprise solid state, magnetic, optical, and / or holographic memory. The memory 920 can be at least partially accessible to the processing unit 910. The memory 920 can be at least partially contained within the processing unit 910. The memory 920 can be a means for storing information such as the phase and amplitude of a reflected signal. The memory 920 can comprise computer instructions configured to be executed by the processing unit 910. When computer instructions configured to cause the processing unit 910 to perform certain actions are stored in the memory 920, and the device 900 as a whole is configured to run using the computer instructions from the memory 920 under the direction of the processing unit 910, the processing unit 910 and / or at least one processing core thereof can be said to be configured to perform the certain actions. The memory 920 can be at least partially contained within the processing unit 910. The memory 920 can be at least partially located outside of the device 900, but accessible to the device 900.

[0075] The device 900 can comprise a wired and / or wireless transmitter 930. For example, the transmitter 930 can comprise at least one transmitting antenna, or the transmitter 930 can be connected to at least one transmitting antenna. If the device 900 is a control device for the at least one light source 510, 520 and / or the at least one spectrometer 512, 522, the transmitter 930 can be connected to the at least one light source 510, 520 and / or the at least one spectrometer 512, 522.

[0076] The device 900 can further comprise a wired and / or wireless receiver 940. For example, the receiver 940 can comprise at least one receiving antenna, or the receiver can be connected to at least one receiving antenna. If the device 900 is a control device for the at least one light source 510, 520 and / or the at least one spectrometer 512, 522, the receiver 940 can be connected to the at least one light source 510, 520 and / or the at least one spectrometer 512, 522.

[0077] The device 900 can further comprise a user interface UI 950. The UI 950 can be a web user interface and can be accessible through a wired or wireless connection. A user can operate the device 900 through the UI 950. Furthermore, the UI 950 can be used to display information to the user. For example, the UI 950 can be used to provide (i.e. display) determined information, such as the results of mineralogical identification, analysis, and mapping.

[0078] The processing unit 910 can be equipped with a transmitter arranged to output information from the processing unit 910 to other devices contained in the apparatus 900 through electrical wiring inside the apparatus 900. Such a transmitter can comprise a serial bus transmitter arranged to output information, e.g. via at least one electrical wiring, to the memory 920 for storage therein. As an alternative to a serial bus, the transmitter can comprise a parallel bus transmitter. Likewise, the processing unit 910 can comprise a receiver arranged to receive information in the processing unit 910 from other devices contained in the apparatus 900 through electrical wiring inside the apparatus 900. Such a receiver can comprise a serial bus receiver arranged to receive information, e.g. via at least one electrical wiring, from the receiver 940 for processing in the processing unit 910. As an alternative to a serial bus, the receiver can also comprise a parallel bus receiver.

[0079] The processing unit 910, the memory 920, the transmitter 930, the receiver 940 and / or the UI 950 can be interconnected in a variety of different manners through electrical wiring inside the apparatus 900. For example, each of the aforementioned devices can be individually connected to a main bus inside the apparatus 900 to allow the devices to exchange information. However, the skilled person will understand that this is merely one example and that various ways of interconnecting at least two of the aforementioned devices can be chosen without departing from the scope of the application, depending on the implementation.

[0080] Figure 10 A method according to at least some embodiments of the application is shown. The method can be used for active hyperspectral mineralogical sensing during open-pit mining under field conditions.

[0081] The method can comprise, at step 1010, illuminating a point on a surface of a mining material using a first broadband light signal having an illumination range in a wavelength range of about 1350 nm to about 2500 nm to enable determining information about at least one water-related absorption feature, a carbonate-related absorption feature or a hydroxide-related absorption feature. The method can further comprise receiving, at step 1020, a reflected version of the first broadband light signal, wherein the reflected version of the first broadband light signal is from a reflection of the mining material at different wavelengths of the illumination range of the first broadband light signal. The method can further comprise determining, at step 1030, a reflectance spectrum of the point of the mining material based on the reflected version of the first broadband light signal and determining the information about at least one water-related absorption feature, a carbonate-related absorption feature or a hydroxide-related absorption feature further based on the reflectance spectrum. Finally, the method can comprise performing, at step 1040, a qualitative and quantitative mineralogical analysis based on the information about at least one water-related absorption feature, a carbonate-related absorption feature or a hydroxide-related absorption feature.

[0082] It should be understood that the disclosed embodiments of the application are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof The disclosed embodiments are described herein with reference to particular structures, process steps, and materials to provide a thorough description for purposes of illustration and explanation.

[0083] Reference throughout this specification to one embodiment or an embodiment means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. As used herein, including within the claims, the term "or" as used in the including in the context of a list of items prefaced with a disjunctive term, for example, the term "or," means a total disjunction of the items in the list of items so prefaced, for example, the phrase "at least one of A or B" means A or B or both A and B.

[0084] As used herein, a plurality of items, structural elements, compositional elements, and / or materials can be presented in a common list for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Thus, no individual member of such list should be construed as a de facto equivalent of any other member of the same list solely based on their presentation in a common group without additional recitation of explicit inclusion of the member in the group. Additionally, the various embodiments and examples of the present application and its various components can be referenced herein in relation to alternative embodiments and examples of the present application and its various components. It should be understood that such embodiments, examples, and alternatives are not to be construed as being factually equivalent to one another, but rather are to be considered as separate and independent representations of the present application.

[0085] In some embodiments, a computer program can be configured to cause a method according to the above embodiments and any combination thereof. In some embodiments, a computer program product embodied on a non-transitory computer readable medium can be configured to control a processing unit to perform a process including the above embodiments and any combination thereof.

[0086] In some embodiments, an apparatus, such as, for example, apparatus 150, can comprise at least one processing unit and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processing unit, cause the apparatus at least to perform the above embodiments and any combination thereof.

[0087] Moreover, the described features, structures, or characteristics can be combined in one or more implementations. In the preceding description, numerous specific details were provided, such as examples of lengths, widths, shapes, etc., to provide a thorough understanding of implementations of the described technology. One skilled in the relevant art will recognize, however, that the technology can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the technology.

[0088] Although the foregoing examples have been described in one or more particular applications, it will be apparent to those of ordinary skill in the art that numerous modifications can be made without departing from the principles and concepts of the application. Accordingly, the present application is not to be limited to the above examples, but is to cover all such modifications as are within the scope of the application.

[0089] The verbs "comprise" and "include" are used as open-ended limitations in this document, both in transition, as in "comprises," "comprising," "included," and "including" and in relation to the claims, as in "comprise comprising," "include," and "including," unless otherwise specified. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated in the specification as if it were individually recited herein. The use of the term "about" in reference to a particular recited value, such as "about 10 mm," means that the exact value is not important so long as the recited value is within a 20% margin of error. Unless otherwise specified, a range of values, when recited, includes both the start and end value of the range, and each intervening value. Unless otherwise specified, the use of the descriptive term "essentially," "substantially," "approximately," "about," and the like, are used in reference to a particular recited value, means that the exact value is not important so long as the recited value is within a 20% margin of error. Unless otherwise specified, the use of the descriptive term "at least one" in reference to a particular recited value, means that the exact value is not important so long as the recited value is within a 20% margin of error. The use of the term "consisting essentially of to recite a series of elements of a combination means that the combination is not limited to the elements recited, but is allowed to include other elements not strictly recited, so long as any other elements do not materially alter the basic and novel characteristics of the combination. The use of the term "consisting of to recite a series of elements of a combination means that the combination is limited to the elements recited.

[0090] Industrial applicability

[0091] At least some implementations of the present application can obtain industrial application in a mineralogical identification, analysis, and mapping system.

[0092] List of abbreviations

[0093] ASIC application specific integrated circuit

[0094] FPGA field programmable gate array

[0095] FTIR fourier transform infrared spectroscopy

[0096] k-NN k-nearest neighbor

[0097] LIBS laser induced breakdown spectroscopy

[0098] LIDAR light detection and ranging

[0099] PCA principal component analysis

[0100] PLS partial least squares

[0101] RAM random access memory

[0102] ROI region of interest

[0103] SBC single board computer

[0104] SoC system on chip

[0105] UI user interface

[0106] XRF X-ray fluorescence

[0107] XRD X-ray diffraction.

Claims

1. A method for active hyperspectral mineralogical sensing during raw ore mining under field conditions in an uncontrolled environment, wherein: The method comprises: - illuminating a point on the surface of the mined material with a first collimated broadband optical signal having an illumination range within a wavelength range of about 1350 nm to about 2500 nm to enable determination of information relating to at least one water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature; - receiving a reflected version of the first collimated broadband optical signal, wherein the reflected version of the first collimated broadband optical signal is a reflection from the mined material at a different wavelength within the illumination range of the first collimated broadband optical signal; - determining a reflection spectrum of said point of said mined material based on said reflected version of said first collimated broadband optical signal, and further determining said information relating to at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature based on said reflection spectrum; and - performing a qualitative and quantitative mineralogical analysis based on said information relating to at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature.

2. The method according to claim 1, wherein Said irradiating said points of said mined material is performed using a collimated broadband optical signal of at least one supercontinuum laser.

3. The method according to claim 1 or 2, further comprising: - measuring a reference measurement spectrum of the first collimated broadband optical signal using a portion of the first collimated broadband optical signal before irradiating the point on the surface of the mined material; - prior to performing said qualitative and quantitative mineralogical analysis, correcting the reflectance spectrum of said point of said mined material using said reference measured spectrum of said first collimated broadband optical signal.

4. The method according to any one of the preceding claims, further comprising: - Identifying minerals by applying a pre-calibrated machine learning classifier and at least one water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature within the wavelength range of the first collimated broadband optical signal.

5. The method according to any one of the preceding claims, further comprising: - identifying the concentration of at least one mineral by applying a pre-calibrated quantitative model and at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature within the wavelength range of the first collimated broadband optical signal.

6. The method according to any one of the preceding claims, further comprising: - scanning a plurality of points on the surface of the mined material point by point according to a predetermined scanning pattern, wherein the scanning comprises illuminating each point of the mined material with the first collimated broadband optical signal and receiving the reflected version of the first collimated broadband optical signal; - determining a plurality of reflectance spectra for each point of the mined material; and - generating a hyperspectral cube for the surface of the mining material based on the combination of all reflection spectra and corresponding coordinates according to the predetermined scanning pattern.

7. The method according to claim 6, further comprising: - Identifying the mineralogical composition and average mineral concentration across the entire surface of the mined material by applying a pre-calibrated machine learning classifier and a pre-calibrated quantitative model to the hyperspectral cube and at least one water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature.

8. The method according to claim 6 or claim 7, further comprising: - forming a mineral distribution map across the entire surface of the mined material by applying a pre-calibrated machine learning classifier and a pre-calibrated quantitative model to the hyperspectral cube and at least one water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature.

9. The method according to any one of the preceding claims, further comprising: - illuminating said point of said mined material with a second collimated broadband optical signal having a wavelength range between about 400 nm and about 1100 nm to enable determination of information relating to at least one iron-related absorption feature; - receiving a reflected version of the second collimated broadband optical signal, wherein the reflected version of the second collimated broadband optical signal is a reflection from the point of the mined material at a different wavelength within the illumination range of the second collimated broadband optical signal; - determining a further reflection spectrum of said point of said mined material based on said reflected version of said second collimated broadband optical signal, and further determining said information relating to at least one iron-related absorption feature based on said reflection spectrum; and - performing said hyperspectral mineralogical analysis based on said information regarding at least one iron-related absorption feature.

10. The method according to claim 9, wherein: The irradiating the point of the mined material with the second collimated broadband optical signal is performed using at least one monochromatic laser.

11. An active hyperspectral mineralogical sensing system for performing active hyperspectral mineralogical sensing during ore mining under field conditions in an uncontrolled environment, the active hyperspectral mineralogical sensing system comprising: at least one light source arranged to illuminate a point on the surface of the mined material with a first collimated broadband optical signal having an illumination range within a wavelength range of about 1350 nm to about 2500 nm to enable determination of information relating to at least one water-related absorption feature, carbonate-related absorption feature or hydroxide-related absorption feature; - at least one first spectrometer arranged to receive reflected versions of the first collimated broadband optical signal, wherein the reflected versions of the first collimated broadband optical signal are reflections from the mined material at different wavelengths within the illumination range of the first collimated broadband optical signal; and - at least one processor configured to: determine a reflectance spectrum of the point of the mined material based on the reflected version of the first collimated broadband optical signal; determine information related to at least one water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature based on the reflectance spectrum; and The mineralogical analysis is performed based on information related to at least one water-related absorption feature, carbonate-related absorption feature, or hydroxide-related absorption feature.

12. The active hyperspectral mineralogical sensing system according to claim 11, further comprising: - at least one beam splitter for separating a portion of the first collimated broadband optical signal for reference measurement, wherein the at least one first spectrometer being connected to the at least one processor and being arranged to receive a reference version of the first collimated broadband optical signal, wherein the reference version of the first collimated broadband optical signal is a reflection from the beam splitter at a different wavelength within the illumination range of the first collimated broadband optical signal. 13 . The active hyperspectral mineralogy sensing system according to claim 11 , further comprising at least one supercontinuum laser as a light source.

14. The active hyperspectral mineralogical sensing system according to any one of claims 11 to 13, further comprising: - a pan-tilt unit arranged for point-by-point scanning of the surface of the mined material to enable also identification of the mineralogical composition and average mineral concentration across the entire surface of the mined material and / or mineralogical mapping of the surface of the mined material.

15. The active hyperspectral mineralogical sensing system according to any one of claims 11 to 14, further comprising: - at least one light source arranged to illuminate said point of said mined material with a second collimated broadband optical signal having an illumination range within a wavelength range of about 400 nm to about 1100 nm to enable determination of information relating to at least one iron-related absorption feature; - at least one second spectrometer arranged to receive a reflected version of the second collimated broadband optical signal, wherein the reflected version of the second collimated broadband optical signal is a reflection from the mined material at a different wavelength within the illumination range of the second collimated broadband optical signal; and - at least one processor configured to: determine a reflectance spectrum of the point of the mined material based on the reflected version of the second collimated broadband optical signal; determine information related to at least one iron-related absorption feature based on the reflectance spectrum; and perform the mineralogical analysis based on the information related to at least one iron-related absorption feature.

16. A computer program for active hyperspectral mineralogical sensing during run-of-mine mining under field conditions in an uncontrolled environment, the computer program comprising instructions which, when executed by a device, cause the device to perform the method according to any one of claims 1 to 10.

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