Detecting Goaf Ahead of the Mine Tunnel Using SAP: A Case Study in Iron Mine, China

Author(s):  
Lei Hao ◽  
Ningbo Li ◽  
Xinji Xu ◽  
Qingsong Zhang ◽  
Lei Chen ◽  
...  
Keyword(s):  
2018 ◽  
Vol 33 (2) ◽  
pp. 51-59 ◽  
Author(s):  
Sara Kasmaee ◽  
◽  
Francesco Tinti ◽  
Roberto Bruno ◽  
◽  
...  
Keyword(s):  

2010 ◽  
Vol 57 (3) ◽  
pp. 249-261 ◽  
Author(s):  
Sophie Decrée ◽  
Gilles Ruffet ◽  
Thierry De Putter ◽  
Jean-Marc Baele ◽  
Philippe Recourt ◽  
...  

2017 ◽  
Vol 24 (23) ◽  
pp. 18798-18816 ◽  
Author(s):  
Naghmeh Soltani ◽  
Behnam Keshavarzi ◽  
Farid Moore ◽  
Armin Sorooshian ◽  
Mohamad Reza Ahmadi

2019 ◽  
Vol 37 (4) ◽  
pp. 235
Author(s):  
GUILHERME A. S. PROSDOCIMI ◽  
MARCO A. DA S. BRAGA ◽  
MARCELO R. BARBOSA ◽  
IURI VIANA BRANDI

ABSTRACT The Brazilian speleological heritage is protected by laws, and every region where caves are present requires scientific studies to assist in environmental licensing. In the context of mining in iron formations, the near-surface geophysical studies using electroresistivity survey were performed in the cave N4E-0022, located at the N4EN iron mine, Carajás Complex, northern Brazil. The near-surface geophysical surveys generated continuous images that enhanced the lithostructural mapping of the lateritic profile, especially in places to which access by conventional methods was difficult. The electrical resistivity profiles were acquired with the dipole-dipole arrangement in the upper portion of the cave. Three sections were performed in SW-NE direction and two in SE-NW direction, totaling 435 meters of acquisition. The geoelectrical signatures were correlated with the lithologic logs of drillholes, the geophysical well logging and with the typical lateritic profile in the cave N4E-0022 surroundings. The results showed a satisfactory interpretation for the near-surface geoelectrical profiles and evolved to a comparison with the lateritic profile (lateritic crust, transition horizon, and saprolite horizon), providing inherent resistivity signatures for each modeled material.Keywords: applied geophysics, iron cave, Carajás.RESUMO O patrimônio espeleológico brasileiro é protegido por leis, e qualquer região com a presença de cavidades precisa de estudos científicos para auxiliar o licenciamento de empreendimentos. No contexto da mineração em terrenos ferríferos, estudos geofísicos rasos utilizando eletrorresistividade foram executados na cavidade N4E-0022, localizada no extremo norte da Mina de N4EN, Complexo Carajás. A geofísica rasa gerou imagens contínuas que potencializaram o mapeamento lito-estrutural do substrato rochoso, principalmente em locais de difícil acesso aos métodos convencionais. Os perfis de eletrorresistividade foram adquiridos com arranjo dipolo-dipolo na porção superior da cavidade. de modo paralelo entre si na porção superior da cavidade. Três seções foram executadas na direção SW-NE e duas na direção SE-NW, totalizando 435 metros de aquisição. As assinaturas geoelétricas das seções foram correlacionadas com as descrições litológicas de furos de sondagem, perfilagens geofísicas de furos de sonda e o mapeamento de detalhe do perfil laterítico no entorno da cavidade N4E-0022, evidenciando um resultado satisfatório para as interpretações realizadas. Os resultados evoluíram para uma comparação com o perfil de alteração típico de rochas ferruginosas (crosta laterítica, horizonte de transição e saprolito), e forneceram assinaturas de resistividades inerentes à cada material modelado. Palavras-chave: geofísica aplicada, caverna ferrífera, Carajás.


2018 ◽  
Vol 77 (19) ◽  
Author(s):  
Filipe Altoé Temporim ◽  
Fábio Furlan Gama ◽  
Waldir Renato Paradella ◽  
José Cláudio Mura ◽  
Guilherme Gregório Silva ◽  
...  

2014 ◽  
Author(s):  
José C. Mura ◽  
Waldir R. Paradella ◽  
Fabio F. Gama ◽  
Athos R. Santos ◽  
Mauricio Galo ◽  
...  

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 896 ◽  
Author(s):  
Xiaobo Liu ◽  
Lei Yang ◽  
Xingfan Zhang ◽  
Liancheng Wang

The analysis of crosscut stability is an indispensable task in underground mining activities. Crosscut instabilities usually cause geological disasters and delay of the project. On site, mining engineers analyze and predict the crosscut condition by monitoring its convergence and stress; however, stress monitoring is time-consuming and expensive. In this study, we propose an improved extreme learning machine (ELM) algorithm to predict crosscut’s stress based on convergence data, for the first time in literature. The performance of the proposed technique is validated using a crosscut response by means of the FLAC3D finite difference program. It is found that the improved ELM algorithm performs higher generalization performance compared to traditional ELM, as it eliminates the random selection for input weights. Furthermore, a crosscut construction project in an underground mine, Yanqianshan iron mine, located in Liaoning Province (China), is selected as the case study. The accuracy and efficiency of the improved ELM algorithm has been demonstrated by comparing predicted stress data to measured data on site. Additionally, a comparison is conducted between the improved ELM algorithm and other commonly used artificial neural network algorithms.


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