Mineral Resource Spatial Association Analysis and Prediction:A Case Study in Western China

doi:10.19657/j.geoscience.1000-8527.2019.04.06

Abstract

Abstract:

Based on the similarity-analogy theory of concealed orebody prediction, a method of spatial location correlation analysis and prediction is designed and developed. By analyzing the published spatial distribution data of mineral resources, correlation of known mineral occurrences is performed to analyze and predict the concealed orebodies. Taking some areas of Western China as an example, the Apriori algorithm is used to analyze the relationship between the spatial location of known mineral occurrences, and the relationship between the symbiosis and mineral association in that area. Consequently, prediction is made on the locations of concealed orebodies. Effectiveness and feasibility of this method are proven by comparing with the existing results. Visualization tools, such as GoogleEarth, are used to show the predicted results, and to make a better comparative study. This project has newly designed and developed a method based on spatial location correlation analysis and prediction.

Key words: mineral prediction, web crawl, association analysis, Apriori algorithm

CLC Number:

LIU Guo, WANG Yizhe, XUE Tao, WU Chenyao, XUE Bo, TANG Tiantian, LIU Shiming. Mineral Resource Spatial Association Analysis and Prediction:A Case Study in Western China[J]. Geoscience, 2019, 33(04): 751-758.

Figures/Tables 8

References 12

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矿种组合	支持度/%	置信度/%
{A,C}	50	100.0
{B,C}	50	66.6
{B,E}	75	100.0
{C,E}	50	66.6
{B,C,E}	50	66.6

矿种组合	支持度/%	置信度/%
{A,C}	50	100.0
{B,C}	50	66.6
{B,E}	75	100.0
{C,E}	50	66.6
{B,C,E}	50	66.6

1	from bs4 import BeautifulSoup
2	import urllib.request,http.cookiejar
3	import math
4	import re
5	head=“http://219.142.81.85/arcgis/services/0refield/ MapServer/WMSServer?request=getfeatureinfo&transparent= true&format=xml%2Fhtml&version=1.1.1&layers= 0&query_layers=0&srs=EPSG%3A4326&”
6	tail=“&height=409&width=1366&bbox= 115.11282992829184%2C39.63686022517068% 2C116.9887454556356%2C40.198536250561304”
7	myset=set()
8	for x in range(546,804):
9	for y in range(126,388):
10	url=head+“X=”+str(x)+“&Y=”+str(y)+tail
11	request=urllib.request.Request(url)
12	request.add_header(“Origin”,“ngac.org.cn”)
13	request.add_header(“User-Agent”,“Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0”)
14	#用带有cookie的urllib访问网页
15	try:
16	response=urllib.request.urlopen(request,timeout=10)
17	#print (response.getcode())
18	cont=response.read()
19	mycont=cont.decode(“utf8”)
20	if re.search(‘FIELDS’,mycont):myset.add(mycont)
21	time.sleep(0.1)
22	response.close()
23	except urllib.error.URLError as e:
24	print(“错误:”+e)
25	else:
26	print(“OK”)
27	print(myset)
28	print(“爬虫结束”)

1	from bs4 import BeautifulSoup
2	import urllib.request,http.cookiejar
3	import math
4	import re
5	head=“http://219.142.81.85/arcgis/services/0refield/ MapServer/WMSServer?request=getfeatureinfo&transparent= true&format=xml%2Fhtml&version=1.1.1&layers= 0&query_layers=0&srs=EPSG%3A4326&”
6	tail=“&height=409&width=1366&bbox= 115.11282992829184%2C39.63686022517068% 2C116.9887454556356%2C40.198536250561304”
7	myset=set()
8	for x in range(546,804):
9	for y in range(126,388):
10	url=head+“X=”+str(x)+“&Y=”+str(y)+tail
11	request=urllib.request.Request(url)
12	request.add_header(“Origin”,“ngac.org.cn”)
13	request.add_header(“User-Agent”,“Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0”)
14	#用带有cookie的urllib访问网页
15	try:
16	response=urllib.request.urlopen(request,timeout=10)
17	#print (response.getcode())
18	cont=response.read()
19	mycont=cont.decode(“utf8”)
20	if re.search(‘FIELDS’,mycont):myset.add(mycont)
21	time.sleep(0.1)
22	response.close()
23	except urllib.error.URLError as e:
24	print(“错误:”+e)
25	else:
26	print(“OK”)
27	print(myset)
28	print(“爬虫结束”)

选择项	推荐	选择项数目	关联项数目	支持度 /%	置信度
云母	金矿	10	10	100.00	0.47
盐矿	铜矿	19	15	78.95	0.30
盐矿	铁矿	19	14	73.68	0.22
盐矿	煤	19	13	68.42	0.16
盐矿	金矿	19	11	57.89	0.23
铜矿	铁矿	42	28	66.67	0.27
铜矿	煤	42	27	64.29	0.21
铜矿	金矿	42	24	57.14	0.38
铜矿	水泥用灰岩	42	17	40.48	0.24
铁矿	煤	45	31	68.89	0.31
铁矿	铜矿	45	28	62.22	0.30
铁矿	金矿	45	26	57.78	0.44
铁矿	水泥用灰岩	45	18	40.00	0.25
水泥用灰岩	煤	28	22	78.57	0.30
水泥用灰岩	铁矿	28	18	64.29	0.17
水泥用灰岩	铜矿	28	17	60.71	0.18
水泥用灰岩	金矿	28	14	50.00	0.17
石灰岩	煤	23	16	69.57	0.19
石灰岩	金矿	23	14	60.87	0.28
石灰岩	铜矿	23	14	60.87	0.16
石灰岩	铁矿	23	13	56.52	0.07
石灰岩	水泥用灰岩	23	11	47.83	0.27
石膏(含泥膏矿)	铜矿	13	10	76.92	0.25
石膏(含泥膏矿)	铁矿	13	10	76.92	0.21
铅矿	铜矿	14	10	71.43	0.22
煤	铁矿	46	31	67.39	0.32
煤	铜矿	46	27	58.70	0.23
煤	金矿	46	24	52.17	0.31
煤	水泥用灰岩	46	22	47.83	0.51
芒硝	铁矿	19	15	78.95	0.26
芒硝	煤	19	14	73.68	0.20
芒硝	金矿	19	12	63.16	0.28
芒硝	铜矿	19	12	63.16	0.17
芒硝	水泥用灰岩	19	10	52.63	0.31
硫铁矿	铜矿	13	12	92.31	0.35
硫铁矿	铁矿	13	12	92.31	0.31
硫铁矿	煤	13	11	84.62	0.25
金矿	铁矿	35	26	74.29	0.33
金矿	铜矿	35	24	68.57	0.31
金矿	煤	35	24	68.57	0.23
金矿	石灰岩	35	14	40.00	0.37
金矿	水泥用灰岩	35	14	40.00	0.20
铬矿	铁矿	13	13	100.00	0.35
铬矿	煤	13	11	84.62	0.25
铬矿	金矿	13	10	76.92	0.35