Application of geophysics in metalliferous mines

Fallon, Gary N.; Fullagar, Peter K.; Sheard, S. N.

doi:10.1080/08120099708728321

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…전기탐사에 는 크게 지하 매질의 전기비저항을 파악하는 전기비저항 (electrical resistivity) 탐사와 매질의 충전 특성을 파악하 는 유도분극(induced polarization, IP) 탐사, 그리고 자연적 으로 발생하는 자연 전위를 측정하는 자연전위(spontaneous/ self-potential, SP) 탐사가 있다. 전기전자탐사는 비교적 짧은 시간 동안 넓은 범위의 정 보를 얻을 수 있는 방법으로 지하수분포 (Binley et al, 2015;Watlet et al, 2018), 산사태 안전성 (Tábořík et al, 2017;Boyd et al, 2021), 광물 탐사 (Pelton et al, 1978;Fallon et al, 1997), 지반침하 위험성 (Giampaolo et al, 2016), 유류오염 (Naudet et al, 2011;Islami, 2017)과 같은 다양한 분야에서 적용되고 있다. 물리 탐사 자료(data)를 다루는 일반적인 과정은 자료획득(acquisition), 자료처리 (processing), 수치 모델링(numerical modeling), 예측 (prediction) 및 해석(interpretation)으로 구성된다 (Yu and Ma, 2021).…”

Section: 서 론unclassified

Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys

Jeong¹,

Jang

Caesary

et al. 2022

J. Korean Soc. Miner. Energy Resour. Eng.

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Technological innovations within the context of electrical and electromagnetic (EM) surveys have allowed for a rapid, efficient, and easier acquisition of a high quantity of data. Such innovations have been integral in mineral exploration and groundwater surveys. On the other hand, conventional inversion of electrical or EM survey data is computationally time-consuming and expensive. To circumvent the limitations of conventional inversion, the implementation of deep learning (DL) using improved neural networks has garnered substantial attention. In this study, we review various DL methods that can be used as substitutes for traditional inversion methods. Specifically, we investigate cases highlighting the successful implementation of DL to electrical or EM surveys and also comprehensively examine the advantages and disadvantages of such an application of DL.

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Section: 서 론unclassified

Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys

Jeong¹,

Jang

Caesary

et al. 2022

J. Korean Soc. Miner. Energy Resour. Eng.

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“…Geophysical methods can provide ideal tools for the assessment of ore continuity, grade estimation, and ore body delineation (Campbell, 1994; Fallon et al , 1997). Of the geophysical subsurface imaging techniques, ground-penetrating radar (GPR), in particular, has recently gained greater acceptance in the mining industry because of its high-resolution and fast data acquisition capabilities (Davis and Annan, 1989).…”

Section: Introductionmentioning

confidence: 99%

Incorporating fine-scale ground-penetrating radar data into the mapping of lateral variability of a laterite-type bauxite horizon

et al. 2014

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Ground-penetrating radar (GPR) offers an inexpensive and rapid method for delineating the laterite profiles by acquiring fine-scale data from the ground. In a case study, a GPR survey was conducted at the Weipa bauxite mine in Australia, in which numerous pick points corresponding to the depth to the bauxite/ironstone boundary were acquired from the ground. These pick points were subsequently merged with the available exploration borehole data using four prediction algorithms, including standard linear regression (SLR), simple kriging with varying local means (SKLM), Bayesian integration (BAY), and ordinary co-located cokriging (OCCK). The required structural inputs for the aforementioned algorithms were derived from the modelled auto and cross-semi-variograms. The cross-validation results suggest that the SKLM approach yielded the most robust estimates. The comparison of these estimates with the actual mine floor also indicates that the inclusion of ancillary GPR data substantially improved the estimation quality.

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“…For these reasons, rockburst hazard forecasting at a mine working face must be made short-term and safe. Geophysical methods can help to reduce the risks (FALLON et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Calculation of Electromagnetic Radiation Criterion for Rockburst Hazard Forecast in Coal Mines

Frid¹

2001

Pure appl. geophys.

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Ð Intensive micro-fracturing of rock close to mining operations accompanies an increase in the likelihood of rockbursting. This fracturing causes an increase of the electromagnetic radiation (EMR) level by up two orders of magnitude, depending on the mining environment. Several examples of this enhanced EMR are presented in this paper. We ®rst treat the EMR theoretical criterion of rockburst hazard in coal mines and compare it with the empirical criterion of EMR activity that was revealed on the basis of more than 400 dierent hazardous and non-hazardous situations in underground coal mines. Only the following parameters are needed to estimate the EMR criterion of rockburst hazard: limiting value of gum volume, mine working width, coal seam thickness, and coal elastic properties.

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Application of geophysics in metalliferous mines

Cited by 14 publications

References 22 publications

Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys

Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys

Incorporating fine-scale ground-penetrating radar data into the mapping of lateral variability of a laterite-type bauxite horizon

Calculation of Electromagnetic Radiation Criterion for Rockburst Hazard Forecast in Coal Mines

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