Item talk:Q272566
From geokb
{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "70237059",
"url": "https://pubs.usgs.gov/publication/70237059"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70237059
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.1016/j.rse.2022.113000",
"url": "https://doi.org/10.1016/j.rse.2022.113000"
}
],
"journal": {
"@type": "Periodical",
"name": "Remote Sensing of Environment",
"volumeNumber": "275",
"issueNumber": null
},
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Remote Sensing of Environment"
}
],
"datePublished": "2022",
"dateModified": "2022-09-28",
"abstract": "Over the past ~30\u00a0years, hyperspectral\u00a0remote sensing\u00a0of chemical variations in white\u00a0mica\u00a0have proven to be useful for ore deposit studies in a range of deposit types. To better understand\u00a0mineral deposits\u00a0and to guide\u00a0spectrometer\u00a0design, this contribution reviews relevant papers from the fields of remote sensing,\u00a0spectroscopy, and geology that have utilized spectral changes caused by chemical variation in white micas. This contribution reviews spectral studies conducted at the following types of mineral deposits: base metal\u00a0sulfide, epithermal,\u00a0porphyry, sedimentary rock hosted gold deposits, orogenic gold,\u00a0iron oxide\u00a0copper gold, and unconformity-related uranium. The structure, chemical composition, and spectral features of white micas, in this contribution defined as\u00a0muscovite,\u00a0paragonite,\u00a0celadonite,\u00a0phengite,\u00a0illite, and sericite, are given. Reviewed laboratory spectral studies determined that shifts in the position of the white mica 2200\u00a0nm combination feature of 1\u00a0nm correspond to a change in Aloct\u00a0content of approximately \u00b11.05%. Many of the reviewed spectral studies indicated that a shift in the position of the white mica 2200\u00a0nm combination feature of 1\u00a0nm was geologically significant.A sensitivity analysis of spectrometer characteristics; bandpass, sampling interval, and channel position, is conducted using spectra of 19 white micas with deep absorption features to determine minimum characteristics required to accurately measure a shift in the position of the white mica 2200\u00a0nm combination feature. It was determined that a sampling interval\u00a0<\u00a016.3\u00a0nm and bandpass <17.5\u00a0nm are needed to achieve a\u00a0root mean square error\u00a0(RMSE) of 2\u00a0nm, whereas a sampling interval\u00a0<\u00a08.8\u00a0nm and bandpass <9.8\u00a0nm are needed to achieve a RMSE of 1\u00a0nm. For comparison, commonly used\u00a0imaging spectrometers\u00a0HyMap, AVIRIS-Classic, SpecTIR\u00ae's AisaFENIX 1K, and HySpextm\u00a0SWIR 384 have 2.1, 1.2, 0.96, and 0.95\u00a0nm RMSE in determining the position of the 2200\u00a0nm white mica combination feature, respectively.An additional sensitivity analysis is conducted to determine the effect of\u00a0signal to noise ratio\u00a0(SNR) on the RMSE of the position of the white mica 2200\u00a0nm combination feature, using spectra of 18 white micas with deep absorption features. For a spectrometer with sampling interval and bandpass of 1\u00a0nm, we estimate that RMSEs of 1 and 1.5\u00a0nm are achievable with spectra having a minimum SNR of approximately 246 and 64, respectively. For a spectrometer with sampling interval and bandpass of 5\u00a0nm, we estimate that RMSEs of 1 and 1.5\u00a0nm are attainable with spectra having a minimum SNR of approximately 431 and 84, respectively. When using a spectrometer with a sampling interval 8.8\u00a0nm and a bandpass of 9.8\u00a0nm, a RMSE of 1 is only achievable with convolved, noiseless reference spectra. For the 8.8_9.8\u00a0nm spectrometer, spectra with SNR of 250 and 100 result in RMSE of 1.1 and 1.3, respectively. Therefore, fine\u00a0spectral resolution\u00a0characteristics achieve RMSEs better than 1\u00a0nm for high SNR spectra while spectrometers with coarse spectral resolution have larger RMSE, perform well with noisy data, and are useful for white mica studies if RMSE of 1.1 to 1.5\u00a0nm is acceptable.",
"description": "113000, 18 p.",
"publisher": {
"@type": "Organization",
"name": "Elsevier"
},
"author": [
{
"@type": "Person",
"name": "Meyer, John Michael",
"givenName": "John Michael",
"familyName": "Meyer",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-2810-9414",
"url": "https://orcid.org/0000-0003-2810-9414"
},
"affiliation": [
{
"@type": "Organization",
"name": "Geology, Geophysics, and Geochemistry Science Center",
"url": "https://www.usgs.gov/centers/gggsc"
}
]
},
{
"@type": "Person",
"name": "Holley, Elizabeth A.",
"givenName": "Elizabeth A.",
"familyName": "Holley",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-2504-4555",
"url": "https://orcid.org/0000-0003-2504-4555"
},
"affiliation": [
{
"@type": "Organization",
"name": "Colorado School of Mines"
}
]
},
{
"@type": "Person",
"name": "Kokaly, Raymond F.",
"givenName": "Raymond F.",
"familyName": "Kokaly",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-0276-7101",
"url": "https://orcid.org/0000-0003-0276-7101"
},
"affiliation": [
{
"@type": "Organization",
"name": "Southwest Regional Director's Office",
"url": "https://www.usgs.gov/regions/southwest"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "Geology, Geophysics, and Geochemistry Science Center",
"url": "https://www.usgs.gov/centers/gggsc"
}
]
},
"OpenAlex": {
"_id": "https://openalex.org/w4225712758",
"abstract_inverted_index": {
"Over": [
0
],
"the": [
1,
46,
73,
130,
133,
155,
164,
167,
215,
218,
297,
300,
316,
325,
328,
331,
445
],
"past": [
2
],
"~30": [
3
],
"years,": [
4
],
"hyperspectral": [
5
],
"remote": [
6,
49
],
"sensing": [
7
],
"of": [
8,
26,
48,
76,
105,
132,
140,
150,
154,
166,
174,
183,
196,
217,
249,
269,
299,
318,
327,
330,
340,
356,
363,
376,
390,
397,
410,
429,
434,
452,
459,
502
],
"chemical": [
9,
61,
100
],
"variations": [
10
],
"in": [
11,
23,
63,
108,
129,
147,
163,
214,
295,
457
],
"white": [
12,
64,
106,
134,
168,
198,
219,
303,
332,
342,
497
],
"mica": [
13,
135,
169,
220,
304,
333,
498
],
"have": [
14,
55,
287,
485
],
"proven": [
15
],
"to": [
16,
35,
144,
204,
209,
241,
265,
314,
320,
504
],
"be": [
17
],
"useful": [
18,
495
],
"for": [
19,
475,
496
],
"ore": [
20
],
"deposit": [
21,
27
],
"studies": [
22,
70,
125,
158,
499
],
"a": [
24,
145,
161,
212,
229,
243,
253,
267,
349,
373,
383,
407,
418,
421,
427,
432
],
"range": [
25
],
"types.": [
28
],
"To": [
29
],
"better": [
30,
471
],
"understand": [
31
],
"mineral": [
32,
77
],
"deposits": [
33
],
"and": [
34,
52,
95,
102,
118,
189,
235,
259,
283,
291,
354,
365,
379,
388,
399,
413,
426,
454,
461,
493
],
"guide": [
36
],
"spectrometer": [
37,
184,
350,
384,
419
],
"design,": [
38
],
"this": [
39,
109
],
"contrib": [
40
],
"ution": [
41
],
"reviews": [
42,
68
],
"relevant": [
43
],
"papers": [
44
],
"from": [
45
],
"fields": [
47
],
"sensing,": [
50
],
"spectroscopy,": [
51
],
"geology": [
53
],
"that": [
54,
127,
160,
228,
361,
395
],
"utilized": [
56
],
"spectral": [
57,
69,
103,
124,
157,
466,
483
],
"changes": [
58
],
"caused": [
59
],
"by": [
60
],
"variation": [
62
],
"micas.": [
65
],
"This": [
66
],
"contribution": [
67,
110
],
"conducted": [
71,
193,
313
],
"at": [
72
],
"following": [
74
],
"types": [
75
],
"deposits:": [
78
],
"base": [
79
],
"metal": [
80
],
"sulfide,": [
81
],
"epithermal,": [
82
],
"porphyry,": [
83
],
"sedimentary": [
84
],
"rock": [
85
],
"hosted": [
86
],
"gold": [
87
],
"deposits,": [
88
],
"orogenic": [
89
],
"gold,": [
90,
94
],
"iron": [
91
],
"oxide": [
92
],
"copper": [
93
],
"unconformity-related": [
96
],
"uranium.": [
97
],
"The": [
98
],
"structure,": [
99
],
"composition,": [
101
],
"features": [
104,
203
],
"micas,": [
107
],
"defined": [
111
],
"as": [
112
],
"muscovite,": [
113
],
"paragonite,": [
114
],
"celadonite,": [
115
],
"phengite,": [
116
],
"illite,": [
117
],
"sericite,": [
119
],
"are": [
120,
239,
263,
368,
402,
494
],
"given.": [
121
],
"Reviewed": [
122
],
"laboratory": [
123
],
"determined": [
126,
227
],
"shifts": [
128
],
"position": [
131,
165,
216,
298,
329
],
"2200": [
136,
170,
221,
301,
334
],
"nm": [
137,
142,
171,
176,
222,
234,
238,
258,
262,
293,
302,
335,
367,
401,
425,
447,
474,
506
],
"combination": [
138,
172,
223,
305,
336
],
"feature": [
139,
173
],
"1": [
141,
175,
270,
357,
364,
398,
435,
473
],
"correspond": [
143
],
"change": [
146
],
"Aloct": [
148
],
"content": [
149
],
"approximately": [
151,
377,
411
],
"\u00b11.05%.": [
152
],
"Many": [
153
],
"reviewed": [
156
],
"indicated": [
159
],
"shift": [
162,
213
],
"was": [
177,
226
],
"geologically": [
178
],
"significant.": [
179
],
"A": [
180
],
"sensitivity": [
181,
310
],
"analysis": [
182,
311
],
"characteristics;": [
185
],
"bandpass,": [
186
],
"sampling": [
187,
230,
254,
352,
386,
422
],
"interval,": [
188
],
"channel": [
190
],
"position,": [
191
],
"is": [
192,
312,
436,
507
],
"using": [
194,
338,
417
],
"spectra": [
195,
339,
371,
405,
449,
478
],
"19": [
197
],
"micas": [
199,
343
],
"with": [
200,
344,
351,
370,
385,
404,
420,
439,
450,
481,
490
],
"deep": [
201,
345
],
"absorption": [
202,
346
],
"determine": [
205,
315
],
"minimum": [
206,
374,
408
],
"characteristics": [
207,
468
],
"required": [
208
],
"accurately": [
210
],
"measure": [
211
],
"feature.": [
224
],
"It": [
225
],
"interval": [
231,
255,
353,
387,
423
],
"<": [
232,
256
],
"16.3": [
233
],
"bandpass": [
236,
260,
355,
389,
428
],
"<17.5": [
237
],
"needed": [
240,
264
],
"achieve": [
242,
266,
469
],
"root": [
244
],
"mean": [
245
],
"square": [
246
],
"error": [
247
],
"(RMSE)": [
248
],
"2": [
250
],
"nm,": [
251,
358,
392,
431
],
"whereas": [
252
],
"8.8": [
257,
424
],
"<9.8": [
261
],
"RMSE": [
268,
294,
326,
433,
458,
501
],
"nm.": [
271
],
"For": [
272,
348,
382,
444
],
"comparison,": [
273
],
"commonly": [
274
],
"used": [
275
],
"imaging": [
276
],
"spectrometers": [
277,
480
],
"HyMap,": [
278
],
"AVIRIS-Classic,": [
279
],
"SpecTIR\u00ae's": [
280
],
"AisaFENIX": [
281
],
"1K,": [
282
],
"HySpextm": [
284
],
"SWIR": [
285
],
"384": [
286
],
"2.1,": [
288
],
"1.2,": [
289
],
"0.96,": [
290
],
"0.95": [
292
],
"determining": [
296
],
"feature,": [
306,
337
],
"respectively.": [
307,
381,
415,
463
],
"An": [
308
],
"additional": [
309
],
"effect": [
317
],
"signal": [
319
],
"noise": [
321
],
"ratio": [
322
],
"(SNR)": [
323
],
"on": [
324
],
"18": [
341
],
"features.": [
347
],
"we": [
359,
393
],
"estimate": [
360,
394
],
"RMSEs": [
362,
396,
470
],
"1.5": [
366,
400,
505
],
"achievable": [
369,
438
],
"having": [
372,
406
],
"SNR": [
375,
409,
451,
477
],
"246": [
378
],
"64,": [
380
],
"5": [
391
],
"attainable": [
403
],
"431": [
412
],
"84,": [
414
],
"When": [
416
],
"9.8": [
430
],
"only": [
437
],
"convolved,": [
440
],
"noiseless": [
441
],
"reference": [
442
],
"spectra.": [
443
],
"8.8_9.8": [
446
],
"spectrometer,": [
448
],
"250": [
453
],
"100": [
455
],
"result": [
456
],
"1.1": [
460,
503
],
"1.3,": [
462
],
"Therefore,": [
464
],
"fine": [
465
],
"resolution": [
467,
484
],
"than": [
472
],
"high": [
476
],
"while": [
479
],
"coarse": [
482
],
"larger": [
486
],
"RMSE,": [
487
],
"perform": [
488
],
"well": [
489
],
"noisy": [
491
],
"data,": [
492
],
"if": [
500
],
"acceptable.": [
508
]
},
"apc_list": {
"value": 4070,
"currency": "USD",
"value_usd": 4070,
"provenance": "doaj"
},
"apc_paid": {
"value": 4070,
"currency": "USD",
"value_usd": 4070,
"provenance": "doaj"
},
"authorships": [
{
"author_position": "first",
"author": {
"id": "https://openalex.org/A5029715898",
"display_name": "John M. Meyer",
"orcid": null
},
"institutions": [],
"countries": [],
"is_corresponding": false,
"raw_author_name": "John M. Meyer",
"raw_affiliation_strings": [],
"affiliations": []
},
{
"author_position": "middle",
"author": {
"id": "https://openalex.org/A5059111916",
"display_name": "Raymond F. Kokaly",
"orcid": "https://orcid.org/0000-0003-0276-7101"
},
"institutions": [],
"countries": [],
"is_corresponding": false,
"raw_author_name": "Raymond F. Kokaly",
"raw_affiliation_strings": [],
"affiliations": []
},
{
"author_position": "last",
"author": {
"id": "https://openalex.org/A5015630268",
"display_name": "Elizabeth Holley",
"orcid": "https://orcid.org/0000-0003-2504-4555"
},
"institutions": [],
"countries": [],
"is_corresponding": false,
"raw_author_name": "Elizabeth Holley",
"raw_affiliation_strings": [],
"affiliations": []
}
],
"best_oa_location": {
"is_oa": true,
"landing_page_url": "https://doi.org/10.1016/j.rse.2022.113000",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S141808269",
"display_name": "Remote Sensing of Environment",
"issn_l": "0034-4257",
"issn": [
"0034-4257",
"1879-0704"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": "cc-by",
"license_id": "https://openalex.org/licenses/cc-by",
"version": "publishedVersion",
"is_accepted": true,
"is_published": true
},
"biblio": {
"volume": "275",
"issue": null,
"first_page": "113000",
"last_page": "113000"
},
"citation_normalized_percentile": {
"value": 0.997854,
"is_in_top_1_percent": true,
"is_in_top_10_percent": true
},
"cited_by_api_url": "https://api.openalex.org/works?filter=cites:W4225712758",
"cited_by_count": 22,
"cited_by_percentile_year": {
"min": 97,
"max": 98
},
"concepts": [
{
"id": "https://openalex.org/C159078339",
"wikidata": "https://www.wikidata.org/wiki/Q959005",
"display_name": "Hyperspectral imaging",
"level": 2,
"score": 0.8017738
},
{
"id": "https://openalex.org/C2781038479",
"wikidata": "https://www.wikidata.org/wiki/Q114675",
"display_name": "Mica",
"level": 2,
"score": 0.7574068
},
{
"id": "https://openalex.org/C2776581184",
"wikidata": "https://www.wikidata.org/wiki/Q106532",
"display_name": "Muscovite",
"level": 3,
"score": 0.70525956
},
{
"id": "https://openalex.org/C199289684",
"wikidata": "https://www.wikidata.org/wiki/Q83353",
"display_name": "Mineralogy",
"level": 1,
"score": 0.5749887
},
{
"id": "https://openalex.org/C33390570",
"wikidata": "https://www.wikidata.org/wiki/Q188463",
"display_name": "Spectrometer",
"level": 2,
"score": 0.56672144
},
{
"id": "https://openalex.org/C127313418",
"wikidata": "https://www.wikidata.org/wiki/Q1069",
"display_name": "Geology",
"level": 0,
"score": 0.5285736
},
{
"id": "https://openalex.org/C183852935",
"wikidata": "https://www.wikidata.org/wiki/Q6002848",
"display_name": "Imaging spectrometer",
"level": 3,
"score": 0.45506948
},
{
"id": "https://openalex.org/C62649853",
"wikidata": "https://www.wikidata.org/wiki/Q199687",
"display_name": "Remote sensing",
"level": 1,
"score": 0.45346937
},
{
"id": "https://openalex.org/C2778583526",
"wikidata": "https://www.wikidata.org/wiki/Q17497804",
"display_name": "Sericite",
"level": 3,
"score": 0.43571067
},
{
"id": "https://openalex.org/C5457282",
"wikidata": "https://www.wikidata.org/wiki/Q7907352",
"display_name": "VNIR",
"level": 3,
"score": 0.43032712
},
{
"id": "https://openalex.org/C2776432453",
"wikidata": "https://www.wikidata.org/wiki/Q7946",
"display_name": "Mineral",
"level": 2,
"score": 0.41150922
},
{
"id": "https://openalex.org/C192562407",
"wikidata": "https://www.wikidata.org/wiki/Q228736",
"display_name": "Materials science",
"level": 0,
"score": 0.299204
},
{
"id": "https://openalex.org/C120665830",
"wikidata": "https://www.wikidata.org/wiki/Q14620",
"display_name": "Optics",
"level": 1,
"score": 0.23965001
},
{
"id": "https://openalex.org/C2779870107",
"wikidata": "https://www.wikidata.org/wiki/Q43010",
"display_name": "Quartz",
"level": 2,
"score": 0.228327
},
{
"id": "https://openalex.org/C121332964",
"wikidata": "https://www.wikidata.org/wiki/Q413",
"display_name": "Physics",
"level": 0,
"score": 0.09872949
},
{
"id": "https://openalex.org/C151730666",
"wikidata": "https://www.wikidata.org/wiki/Q7205",
"display_name": "Paleontology",
"level": 1,
"score": 0.0
},
{
"id": "https://openalex.org/C191897082",
"wikidata": "https://www.wikidata.org/wiki/Q11467",
"display_name": "Metallurgy",
"level": 1,
"score": 0.0
}
],
"corresponding_author_ids": [],
"corresponding_institution_ids": [],
"countries_distinct_count": 0,
"counts_by_year": [
{
"year": 2024,
"cited_by_count": 12
},
{
"year": 2023,
"cited_by_count": 6
},
{
"year": 2022,
"cited_by_count": 4
}
],
"created_date": "2022-05-05",
"datasets": [],
"display_name": "Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations",
"doi": "https://doi.org/10.1016/j.rse.2022.113000",
"fwci": 1.253,
"grants": [],
"has_fulltext": false,
"id": "https://openalex.org/W4225712758",
"ids": {
"openalex": "https://openalex.org/W4225712758",
"doi": "https://doi.org/10.1016/j.rse.2022.113000"
},
"indexed_in": [
"crossref"
],
"institutions_distinct_count": 0,
"is_paratext": false,
"is_retracted": false,
"keywords": [
{
"id": "https://openalex.org/keywords/muscovite",
"display_name": "Muscovite",
"score": 0.70525956
},
{
"id": "https://openalex.org/keywords/mineral-prospectivity",
"display_name": "Mineral Prospectivity",
"score": 0.533993
},
{
"id": "https://openalex.org/keywords/hyperspectral",
"display_name": "Hyperspectral",
"score": 0.527282
},
{
"id": "https://openalex.org/keywords/spectral-unmixing",
"display_name": "Spectral Unmixing",
"score": 0.519731
},
{
"id": "https://openalex.org/keywords/imaging-spectrometer",
"display_name": "Imaging spectrometer",
"score": 0.45506948
},
{
"id": "https://openalex.org/keywords/sericite",
"display_name": "Sericite",
"score": 0.43571067
},
{
"id": "https://openalex.org/keywords/vnir",
"display_name": "VNIR",
"score": 0.43032712
}
],
"language": "en",
"locations": [
{
"is_oa": true,
"landing_page_url": "https://doi.org/10.1016/j.rse.2022.113000",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S141808269",
"display_name": "Remote Sensing of Environment",
"issn_l": "0034-4257",
"issn": [
"0034-4257",
"1879-0704"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": "cc-by",
"license_id": "https://openalex.org/licenses/cc-by",
"version": "publishedVersion",
"is_accepted": true,
"is_published": true
}
],
"locations_count": 1,
"mesh": [],
"ngrams_url": "https://api.openalex.org/works/W4225712758/ngrams",
"open_access": {
"is_oa": true,
"oa_status": "hybrid",
"oa_url": "https://doi.org/10.1016/j.rse.2022.113000",
"any_repository_has_fulltext": false
},
"primary_location": {
"is_oa": true,
"landing_page_url": "https://doi.org/10.1016/j.rse.2022.113000",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S141808269",
"display_name": "Remote Sensing of Environment",
"issn_l": "0034-4257",
"issn": [
"0034-4257",
"1879-0704"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": "cc-by",
"license_id": "https://openalex.org/licenses/cc-by",
"version": "publishedVersion",
"is_accepted": true,
"is_published": true
},
"primary_topic": {
"id": "https://openalex.org/T12157",
"display_name": "Machine Learning for Mineral Prospectivity Mapping",
"score": 1.0,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
"publication_date": "2022-06-01",
"publication_year": 2022,
"referenced_works": [
"https://openalex.org/W1500273871",
"https://openalex.org/W1538908379",
"https://openalex.org/W168217325",
"https://openalex.org/W182440616",
"https://openalex.org/W1890075632",
"https://openalex.org/W1964549775",
"https://openalex.org/W1983564289",
"https://openalex.org/W1986530877",
"https://openalex.org/W1988014764",
"https://openalex.org/W1992444449",
"https://openalex.org/W1998818470",
"https://openalex.org/W1999726370",
"https://openalex.org/W2008825280",
"https://openalex.org/W2010797000",
"https://openalex.org/W2012749606",
"https://openalex.org/W2018194343",
"https://openalex.org/W2019038438",
"https://openalex.org/W2022470997",
"https://openalex.org/W2048459112",
"https://openalex.org/W2059579037",
"https://openalex.org/W2067087418",
"https://openalex.org/W2071584308",
"https://openalex.org/W2075901285",
"https://openalex.org/W2076430294",
"https://openalex.org/W2082645751",
"https://openalex.org/W2083639974",
"https://openalex.org/W2083808432",
"https://openalex.org/W2087407047",
"https://openalex.org/W2089752283",
"https://openalex.org/W2099662321",
"https://openalex.org/W2106085599",
"https://openalex.org/W2113062784",
"https://openalex.org/W2122452972",
"https://openalex.org/W2128686953",
"https://openalex.org/W2136518035",
"https://openalex.org/W2137209740",
"https://openalex.org/W2139577861",
"https://openalex.org/W2150182117",
"https://openalex.org/W2153889834",
"https://openalex.org/W2161669723",
"https://openalex.org/W2165394332",
"https://openalex.org/W2232172708",
"https://openalex.org/W2293874811",
"https://openalex.org/W2330747182",
"https://openalex.org/W2396451144",
"https://openalex.org/W2460188791",
"https://openalex.org/W2527344479",
"https://openalex.org/W2568355616",
"https://openalex.org/W2599803952",
"https://openalex.org/W2606412288",
"https://openalex.org/W2727830220",
"https://openalex.org/W2748968520",
"https://openalex.org/W2787430445",
"https://openalex.org/W2792216949",
"https://openalex.org/W2895942203",
"https://openalex.org/W2898573755",
"https://openalex.org/W2945071234",
"https://openalex.org/W2960366821",
"https://openalex.org/W3004157881",
"https://openalex.org/W3023114228",
"https://openalex.org/W3113732972",
"https://openalex.org/W3127815140",
"https://openalex.org/W3143884069",
"https://openalex.org/W417239217",
"https://openalex.org/W4247521738",
"https://openalex.org/W4251358664",
"https://openalex.org/W4252799683"
],
"referenced_works_count": 67,
"related_works": [
"https://openalex.org/W4255161740",
"https://openalex.org/W4249918975",
"https://openalex.org/W4237651444",
"https://openalex.org/W4229366085",
"https://openalex.org/W40894355",
"https://openalex.org/W2390114867",
"https://openalex.org/W2385873617",
"https://openalex.org/W2066470398",
"https://openalex.org/W1979108958",
"https://openalex.org/W1965754764"
],
"sustainable_development_goals": [],
"title": "Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations",
"topics": [
{
"id": "https://openalex.org/T12157",
"display_name": "Machine Learning for Mineral Prospectivity Mapping",
"score": 1.0,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10689",
"display_name": "Hyperspectral Image Analysis and Classification",
"score": 0.9945,
"subfield": {
"id": "https://openalex.org/subfields/2214",
"display_name": "Media Technology"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T12282",
"display_name": "Comminution in Mineral Processing",
"score": 0.9857,
"subfield": {
"id": "https://openalex.org/subfields/2210",
"display_name": "Mechanical Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
}
],
"type": "review",
"type_crossref": "journal-article",
"updated_date": "2024-08-13T21:24:28.409925",
"versions": []
}
}
