Determining ore characteristics

1. An ore processing system, comprising:

one or more processors; and

one or more tangible, non-transitory media operably connectable to the one or more processors and storing instructions that, when executed, cause the one or more processors to perform operations comprising:

causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility, wherein the ore fragments are treated with a fluid imaging enhancement prior to the recording of the plurality of images;

correlating the plurality of images of the stream of ore fragments with one or more characteristics of the ore fragments using a machine learning model that comprises a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments;

determining, based on the correlation, at least one of the one or more characteristics of the ore fragments;

determining, based on at least one of the plurality of images, an anomaly within the stream of ore fragments;

based on the determination of the anomaly, causing a change to an operation of the ore processing facility; and

generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.

2. The system of claim 1 , wherein the plurality of images comprise:

images comprising layers of red, green, blue, and grey;

hyperspectral images;

acoustic images;

gravimetric images; or

depth imagery images.

3. The system of claim 1 , wherein the plurality of ore parameter measurements comprise measurements based on at least one of x-ray diffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray (EDS).

4. The system of claim 1 , wherein the one or more characteristics comprises at least one of mineral composition, density, porosity, fracture type, fragment size, fragment moisture content, or hardness.

5. The system of claim 1 , wherein causing a change to the operation of the ore processing facility comprises at least one of:

causing a change of route of the stream of ore fragments from the first location in the ore processing facility to a third location in the ore processing facility different than the second location;

causing a stop to a movement of the stream of ore fragments en route from the first location in the ore processing facility to the second location in the ore processing facility; or

causing an adjustment of an ore source of the stream of ore fragments moving through the ore processing facility.

6. The system of claim 1 , further comprising an electromagnetic (EM) imaging system, the operations further comprising:

causing the EM imaging system to record a plurality of EM images of the stream of ore fragments moving from the first location in the ore processing facility to the second location in the ore processing facility; and

determining, based on the plurality of EM images, one or more mineral characteristics of the ore fragments.

7. The system of claim 6 , wherein the one or more mineral characteristics comprises at least one of ore fragment density, ore fragment size, or ore fragment surface composition.

8. The system of claim 1 , wherein causing the imaging capture system to record the plurality of images of the stream of ore fragments en route from the first location in the ore processing facility to the second location in the ore processing facility comprises:

causing the imaging capture system to record the plurality of images of the stream of ore fragments as the ore fragments are moving on a conveyor or belt continuous feed system from the first location in the ore processing facility to the second location in the ore processing facility.

9. The system of claim 1 , wherein the machine learning model is trained on a data corpus that comprises a plurality of ore fragment samples measured by at least one of x-ray diffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray (EDS) to correlate a plurality of ore parameter measurements of the ore fragment samples with at least one ore fragment characteristic of the ore fragment samples.

10. A computer-implemented ore processing method executed by one or more processors, the method comprising:

causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility, wherein the ore fragments are treated with a fluid imaging enhancement prior to the recording of the plurality of images;

correlating the plurality of images of the stream of ore fragments with one or more characteristics of the ore fragments using a machine learning model that comprises a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments;

determining, based on the correlation, at least one of the one or more characteristics of the ore fragments;

determining, based on at least one of the plurality of images, an anomaly within the stream of ore fragments;

based on the determination of the anomaly, causing a change to an operation of the ore processing facility; and

generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.

11. The method of claim 10 , wherein the plurality of images comprise:

images comprising layers of red, green, blue, and grey;

hyperspectral images;

acoustic images;

gravimetric images; or

depth imagery images.

12. The method of claim 10 , wherein the plurality of ore parameter measurements comprise measurements based on at least one of x-ray diffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray (EDS).

13. The method of claim 10 , wherein the one or more characteristics comprises at least one of mineral composition, density, porosity, fracture type, fragment size, fragment moisture content, or hardness.

14. The method of claim 10 , wherein causing a change to the operation of the ore processing facility comprises at least one of:

causing a change of route of the stream of ore fragments from the first location in the ore processing facility to a third location in the ore processing facility different than the second location;

causing a stop to a movement of the stream of ore fragments en route from the first location in the ore processing facility to the second location in the ore processing facility; or

causing an adjustment of an ore source of the stream of ore fragments moving through the ore processing facility.

15. The method of claim 10 , further comprising an electromagnetic (EM) imaging system, the operations further comprising:

causing the EM imaging system to record a plurality of EM images of the stream of ore fragments moving from the first location in the ore processing facility to the second location in the ore processing facility; and

determining, based on the plurality of EM images, one or more mineral characteristics of the ore fragments.

16. The method of claim 15 , wherein the one or more mineral characteristics comprises at least one of ore fragment density, ore fragment size, or ore fragment surface composition.

17. The method of claim 10 , wherein causing the imaging capture system to record the plurality of images of the stream of ore fragments en route from the first location in the ore processing facility to the second location in the ore processing facility comprises:

causing the imaging capture system to record the plurality of images of the stream of ore fragments as the ore fragments are moving on a conveyor or belt continuous feed system from the first location in the ore processing facility to the second location in the ore processing facility.

18. The method of claim 10 , wherein the machine learning model is trained on a data corpus that comprises a plurality of ore fragment samples measured by at least one of x-ray diffraction (XRD), x-ray fluorescence (XRF), or energy dispersive x-ray (EDS) to correlate a plurality of ore parameter measurements of the ore fragment samples with at least one ore fragment characteristic of the ore fragment samples.

19. A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility, wherein the ore fragments are treated with a fluid imaging enhancement prior to the recording of the plurality of images;

correlating the plurality of images of the stream of ore fragments with one or more characteristics of the ore fragments using a machine learning model that comprises a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments;

determining, based on the correlation, at least one of the one or more characteristics of the ore fragments;

determining, based on at least one of the plurality of images, an anomaly within the stream of ore fragments;

based on the determination of the anomaly, causing a change to an operation of the ore processing facility; and

generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.