Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning

Table 6

Fusion characteristics of various events.

Event	knew1	knew2	knew3	knew4	knew5	knew6	knew7	knew8
0_1	0.5001	−1.821	0.02581	−1.325	0.2765	0.1244	−0.1289	0.1526
0_2	0.5617	−2.122	−0.1481	−1.368	0.7622	0.1893	−0.8002	−0.1864
1_1	−0.0343	0.7126	−0.1530	0.2844	0.1190	0.2380	−0.2050	−0.1785
1_2	0.08697	0.9340	0.2391	0.4886	0.3081	1.649	1.553	0.1097
2_1	−0.5977	−0.3184	0.2722	−0.8219	0.4543	0.1980	0.1859	−0.4004
2_2	−0.7905	−0.2000	0.4418	−1.647	0.9214	−0.3722	−0.3227	−0.2133
3_1	0.3421	−1.242	0.4294	−0.3219	−0.3893	1.000	−0.7345	−0.3691
3_2	0.3479	−1.018	0.03796	−0.3265	−0.8431	0.7047	−0.5303	−0.5500
4_1	−0.3321	−0.8270	−0.3389	−0.9336	0.1558	0.3074	−0.4282	−0.2500
4_2	−0.3397	−0.6159	0.4481	−1.538	0.2683	0.4947	−0.9815	−0.4181
5_1	−0.1381	−0.5451	0.7698	−1.989	0.2219	0.3403	−0.7560	0.3496
5_2	−0.1537	−0.1002	1.038	−1.165	0.2116	0.3879	−0.3852	0.6888
6_1	0.3253	0.009533	−0.3325	−0.9649	0.8771	0.5050	0.1364	0.2211
6_2	0.3612	−0.032	−0.434	−0.9573	0.8680	0.5043	−0.9699	−0.6800
7_1	0.9409	−0.9920	−0.5088	−0.3364	−0.9371	0.09867	−0.07757	0.1945
7_2	0.7031	−0.9597	−0.3833	−0.5293	−0.5036	0.02525	−0.3370	0.4775