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By B L. Dickson, E, M F. Lee, P A. Mikula

Computational techniques are needed to assist in the analysis and interpretation of the increasing amounts of geoscientific and mining related data and information that are routinely gathered at mine sites. New interpretation methods are needed to provide an integrated approach so as to establish relationships (cause and effect) or to be predictive about possible outcomes based on acquired data. Traditional multivariate statistical methods are often confused by variable relationships that are non-linear, data distributions that are non-normal, and by the data themselves that may contain missing values, text and both continuous and discontinuous numeric values. Modern data mining methods are available to understand the relationships within and between diverse data sets, and identify relationships or trends associated with processes, measurements and mine design parameters in mines. The self-organizing map (SOM) is a data mining approach with the advantage that all input data samples are represented as vectors in a data-space defined by the observations (variables). The SOM procedure is an exploratory analysis tool that highlights patterns and relationships. Results are internally derived, in an unsupervised fashion based on measures of vector similarity. Our SOM outputs are highly visual, which assists in understanding and illustrating the dataÆs structure and internal relationships. Two studies are presented. The first uses SOM to investigate the relationships between a range of measured and derived rock property parameters and their observed behaviours during mining. Our aim was to determine whether a SOM analysis would allow a better understanding of observed and predicted rock behaviours from their rock properties. The SOM analysis showed that there were indeed relationships between æbrittleÆ, æsugaryÆ, æslabbyÆ and æsqueezingÆ rock behaviours and a number of the input parameters. The computed SOM æstructureÆ was then used to assess the likely behaviour of unknown samples by assigning them into behavioural fields based on their measured and derived geotechnical responses. The second study was aimed at assessing whether a SOM analysis of microseismic data at the Kalgoorlie Consolidated Gold MineÆs (KCGM) Mt Charlotte mine can contribute towards understanding and potentially forecasting/minimising seismicity at the mine-design stage. The database comprised 40 sample æclustersÆ of microseismic parameters from the MS-RAP software package, which were derived from 2500 individual seismic events. The SOM analysis showed that some of the initial mining conditions are related to the seismic parameters. In each of the studies, the SOM procedures assisted in understanding the data and provided new insights. Because of its æordered vector quantisationÆ foundation, the SOM procedure is a substantial improvement over traditional statistical methods in three main areas:its ability to analyse data from disparate sources, including different data types; its ability to analyse sparse data sets with missing values; and its ability to analyse and visualise non-linear relationships within a data set.

Jan 1, 2006

By S. Dessureault

The relatively recent explosion of available data in all business sectors has induced a common expression of “drowning in data, starving for information.” The mining sector is affected by the information age through equipment-based sensors, process monitoring, and traditional enterprise data sources. Information technologists have developed techniques such as data mining that apply new and old statistical algorithms to large datasets. Mine planners and managers can apply these tools for improved planning and control, thereby increasing the use of the highly underutilized information system infrastructure in most modern mines. This paper describes the use of data mining and its enabling technology, data warehousing, in the development of an energy consumption model for large surface mines using real historical data.

Jan 1, 2007

By Austin Metz, Kevin Stanton, Ramin Motamed

"A recent study calibrated Load and Resistance Factor Design (LRFD) resistance factors for local soil conditions in the Las Vegas Valley. As a part of this calibration, an extensive database of drilled shaft load tests and associated subsurface soil information were collected and compiled. This database, deemed the Nevada Department of Transportation Deep Foundation Load Test Database or NDFLTD, was designed with the presentation of load test data, the subsurface soil conditions, and the spatial distribution of the data in mind. In addition, the NDFLTD was developed in a way that allows additional data to be added as it becomes available. As such, the rules of database normalization were followed to allow for additional and different types of data to be added to the database and reports. Microsoft Access was chosen to present the data because it is easily accessible to anyone with Microsoft office software, it has a quick learning curve, and it has the ability to present a relational database. The largest obstacle faced when creating the NDFLTD was normalizing the data used in the LRFD calibration, with the goal of reaching the 5th normal form.INTRODUCTIONLRFD is based on statistical performance of engineering components, as opposed to the more traditional Allowable Strength Design (ASD), which divides the strength of a component by a safety factor. Calibration of resistance factors for LRFD requires a robust statistical analysis of past performance. For drilled shafts this requires a database of full scale load tests (Abu-Hejleh et al. 2014). The FHWA developed the largest US based deep foundation load test database, deemed the DFLTD, collecting data from 1985 to 2003 when funds ran out (Abu-Hejleh et al. 2014). Recently, the DFLTD has been updated to modern standards (Petek et al. 2016). In October of 2007, the FHWA mandated state department of transportations (DOTs) to follow AAHSTO LRFD guidelines for their highway foundation designs (Abu-Hejleh et al. 2014). Since then multiple state DOTs have developed their own, region specific deep foundation load test databases. Florida has a long history of developing databases for deep foundation LRFD resistance factor calibration, with the two main databases being the FL database, and The Deep Foundations Database (Abu-Hejleh et al. 2014). Iowa developed a Microsoft Access based database in 2014 (Garder et al. 2014). Oregon developed a database compiling pertinent data from the DFLTD, Florida DOT’s Deep Foundation Database, as well as from other databases (Smith et al. 2011). Illinois developed the “International Database” for static and dynamic reliability based calibration (Long et al. 2009). Louisiana developed a database for LRFD Calibration of driven piles in 2009 (Abu-Farsakh et al. 2009). The California Department of Transportation has its own internal foundation database, which is housed within the Foundation Testing Branch archive (Smith et al. 2011). In 2016, the Nevada Department of Transportation (NDOT) presented its own LRFD calibration for the Las Vegas Valley (Stanton et al. 2017), and the NDFLTD was developed as a part of that project (Motamed et al. 2016)."

Jan 1, 2017

By Richard F. Link, George S. Koch

About 50,000 assays of mine samples were required in a preliminary investigation of the distribution of gold, silver, lead, copper, and zinc in two large Mexican mines, the Frisco mine at San Francisco del Oro (Chihuahua) and the Fresnillo mine at Fresnillo (Zacatecas). The feasibility of working with so many assays rests directly on the use of machine processing methods. The purpose of this article is to describe the methods used because machine (or data) processing can also be used efficiently to make the many calculations from mine assays needed in mining operations. No particular originality is claimed for these methods, which are straightforward applications of standard procedures for machine processing. Inasmuch as machine processing is essentially a routine job once it is begun, procedures must be organized more completely than is the case where human judgment can be inserted at every step. Before undertaking any work, it is necessary to decide carefully just which data must be processed as well as the exact results desired.

Jan 9, 1960

By Shankar Narasimhan

Data Reconciliation is a technique that has been developed more than fifty years ago and has been refined over the past few decades and is now being deployed routinely in several process industries such as refining, petrochemicals, mineral processing and hydro-metallurgy. The main aim of data reconciliation is to obtain accurate estimates of all process variables and parameters using plant measurements that are invariably corrupted by random errors and may also contain biases and other gross errors. These reconciled estimates are subsequently used either in offline exercises such as simulation, retrofitting, and de-bottlenecking or in online optimization and control applications. In this presentation, a brief history of data reconciliation, its assumptions, and uses are reviewed. New developments in this area are highlighted such as robust data reconciliation to handle model uncertainties, and reconciliation using identified models derived purely from operating data. Several case studies in the mineral processing industries reported in the literature are also highlighted to indicate the potential benefits of this technique.

Sep 1, 2012

The objective of the paper is to develop a material balance calculation procedure for a production unit involving two plants separated by a pipeline. The case study is taken from the oil sands industry. The raw sands are first mixed with water in a conditioning plant, then transported through a pipeline to an oil extraction plant. The combination of pipeline dimensions and nominal throughput creates a significant variable time lag between the two plants. As usual, mass balance calculations required for the evaluation of plant performance indicators, such as bitumen recovery, are inaccurate due to measurement errors. In addition, in the present case study, the time lag of the pipe creates an additional source of uncertainty, which may lead to misinterpretations and wrong decisions. Raw data are usually reconciled through steady-state mass balancing, to correct for measurement errors. However, because of the significant pipeline dynamic lag, standard steady-state daily mass balance may fail at giving an appropriate picture of the operations. The proposed solution to the data reconciliation problem includes the use of a pipeline plug-flow model in the mass balance calculation. To fulfil the industrial constraint that current data reconciliation must not modify past reconciled values, while still satisfying dynamic constraints, a daily one-step method, i.e. a unitary filter-ing horizon, is recommended. The method is illustrated with simulation data, and the results com-pared to those of standard data reconciliation methods.

Jan 1, 2014

By K. El-Ouassiti, M. Zouadi, C. Bazin, D. Hodouin

Data reconciliation techniques are used to attenuate the effect of measurement errors and estimate unmeasured variables. This paper illustrates the application of data reconciliation to hydrometallurgy processes using measurements from leaching and solvent extraction obtained from laboratory experiments. The application of data reconciliation provides an efficient way to detect gross and systematic errors and produces reliable performance indices for process evaluation.

Jan 1, 2005

By Gerald V. Jergensen

Does an idea have merit? What does it cost to implement? Can the concept be implemented successfully? The ultimate proof is to try, then see. However, when there are many millions of dollars at stake, blindly building and trying requires considerable courage. Owners and investors in retrospect might choose other less complimentary words. So we might ask, "How might we establish the feasibility of an idea or concept before committing large sums of money to the ultimate test?" From this basic and prudent question, has evolved the engineering concept of the feasibility study. A feasibility study is simply a systematic evaluation of an idea or concept to determine the prospects for success. In a literal sense, a feasibility study considers an idea in terms of its data base, "builds" the idea on paper, "operates" that idea, calculates the cost and benefits, then measures the probable success in terms of some economic, technical or financial standard. Feasibility studies can be quite modest in scope yet provide useful insights as to the viability of an idea. A study might be initiated only to examine the completeness or validity of the metallurgical data base. Another study could be a search for a workable idea or solution to an already-identified possibly troublesome problem. In this latter case, the question, "Will it work?" may be far more important than, "How much will it cost?" Feasibility studies are sometimes global in scope, involving several dozens of specialists and examining an entire "grass roots" mining complex. Other studies may be less ambitious but equally important in examining an expansion proposal or developing new operating techniques. Studies may be commissioned to remove plant bottle-necks, solve maintenance problems and so on. The terms study and feasibility study are used interchangeably in this chapter. They denote a systematic method to examine a proposed concept or idea. DATA BASE Typically, studies are initiated with a minimum amount of specific information and a maximum number of assumptions. As the work proceeds and the data base expands, assumptions are replaced

Jan 1, 1982

By C. Sunderman, R. Jacksha

"Mine monitoring through various sensors is a vital component of successful miner safety and health programs. Data from environmental, geotechnical, infrastructure, and other types of sensors are increasingly being used to discover and mitigate health and safety concerns in underground mines. In many smaller underground mines, as well as in new development headings of larger underground mines, leaky feeder communication systems may be the only available means to transport crucial monitoring data. In addition, data transport is increasingly being delivered using Internet Protocol (IP) while older forms of serial communication are being retired. This paper presents the selection, configuration, and testing methodologies that were employed by researchers from the National Institute for Occupational Safety and Health (NIOSH) to integrate commercially available land mobile data radios into an existing leaky feeder communication system to provide Internet Protocol data transport. INTRODUCTION To enhance the health and safety of their mining workforce, mine owners and operators are increasingly installing environmental monitoring and control systems in underground mines. Real-time, mine-wide monitoring is becoming a reality with the implementation of the Industrial Internet of Things (IIoT) into the underground mining industry [1]. However, not all underground mines, especially smaller mines and new development headings in larger mines, have the information technology (IT) infrastructure in place to handle the data transport requirements for the newer IP-based technologies that are part of IIoT. This was the case with a mine in the Western United States; it lacked the infrastructure to perform critical monitoring in remote headings. The mine partnered with the U.S. National Institute for Occupational Safety and Health’s (NIOSH’s) Spokane Mining Research Division (SMRD) to develop and evaluate a cost-effective solution for real-time monitoring of geotechnical conditions in relatively remote sections of the mine. This paper describes SMRD’s solution to leverage the mine’s existing leaky feeder system to achieve IP data transport using commercially available land mobile data radios. DATA RADIO SELECTION The primary selection criteria for the data radios were network data format, modulation type, and radio channel spacing. The network data format decided upon was Internet Protocol. This format allowed for straightforward integration between the IP-enabled data acquisition system located in a remote heading and the network switch located in the control room. It also eliminated the added complexity of additional network hardware such as Ethernet to Serial converters. IP additionally provided a direct path to integration into higher bandwidth networks, such as fiber optic, should they become available."

Jan 1, 2018

By C. Sunderman, R. Jacksha

"Mine monitoring through various sensors is a vital component of successful miner safety and health programs. Data from environmental, geotechnical, infrastructure and other types of sensors are increasingly used to discover and mitigate health and safety concerns in underground mines. In many smaller underground mines, as well as in the new development headings of larger underground mines, leaky feeder communication systems may be the only available means to transport crucial monitoring data. In addition, data transport is increasingly being delivered using Internet Protocol (IP), while older forms of serial communication are being retired. This paper presents the selection, configuration and testing methodologies employed by researchers from the U.S. National Institute for Occupational Safety and Health (NIOSH) to integrate commercially available land mobile data radios into an existing leaky feeder communication system to provide IP data transport.IntroductionTo enhance the health and safety of their mining workforce, mine owners and operators are increasingly installing environmental monitoring and control systems in underground mines. Real-time, mine-wide monitoring is becoming a reality with the implementation of the Industrial Internet of Things (IIoT) into the underground mining industry (Zhou et al., 2017). However, not all underground mines, especially smaller mines and new development headings in larger mines, have the information technology infrastructure in place to handle the data transport requirements for the newer Internet Protocol (IP)-based technologies that are part of IIoT.This was the case with a mine in the Western United States. It lacked the infrastructure to perform critical monitoring in remote headings. Therefore, the mine partnered with the U.S. National Institute for Occupational Safety and Health’s (NIOSH) Spokane Mining Research Division (SMRD) to develop and evaluate a cost-effective solution for the real-time monitoring of geotechnical conditions in relatively remote sections of the mine. This paper describes SMRD’s solution to leverage the mine’s existing leaky feeder communications system to achieve IP data transport using commercially available land mobile data radios (NIOSH, 2013; Forooshani et al., 2013; Novak, Snyder and Kohler, 2010; Yarkan et al., 2009). The solution, fully evaluated in laboratory and mine environments, requires no modifications to the existing system, is economical, simple to implement, and reliable. It also allows for transition to higher-bandwidth IT networks, such as fiber optic or WiFi, should they become available."

Dec 1, 2018

By Frédéric Flament

The aim of this paper is to present the development of a data validation software as component of a commercial mass balance package. Data reconciliation through mass balance is basically an optimization problem constrained by the law of mass conservation. As any optimization problem solved with a modern and robust software application, erroneous information or starting point will always lead to a solution, whether it is realistic or not. Moreover, good information wrongly weighted by an incorrect error model will lead to the same bad ending. In an industrial context where competition requires accurate information, data reconciliation without validation can be disastrous and calls for data validation before and after reconciliation. The challenge in automated data validation resides in the development of general theoretical solutions. Until now, none has been presented and only partial solutions are available. The development presented in this paper incorporates some of these partial solutions. The integration of human knowledge into the application paved the way to a more general solution. The combination ?theoretical validation ? human expertise? proved to be flexible and reliable enough to be integrated in a commercial data validation package. Numerous examples though industrial applications are presented. These applications demonstrate the validation of raw measurements, user support in the development of error models and validation of reconciled measurements.

May 1, 2001

By Randall Morris

After years of steadily declining employment and production, the U.S. coal industry is having a moment. In 2016, when Donald Trump was running for U.S. President, he cast a spotlight on coal mining in his campaign speeches, promising to end the “War on Coal.” Fast forward to 2025. On the first day of his second term, President Trump signed the National Energy Emergency Act, which emphasizes domestic energy production as a matter of national security and focuses on incentivizing domestic energy resources. Then on April 8, President Trump issued an executive order opening federal land to coal mining to reinvigorate “America’s beautiful clean coal industry.” As the nation seeks to identify reliable solutions to meet its ever-growing energy needs, the administration is going all in on proven, reliable and readily available natural resources, including coal. This means the coal industry is positioned to play a pivotal role in the administration’s stated energy policy, and highwall mining could be an integral factor in helping the industry optimize the extraction of ore to make the most of this moment. The White House noted in a statement that the United States’ “abundance of energy and natural resources … have historically powered our Nation’s economic prosperity” and vowed to remove regulations that have hindered developing these resources — regulations that resulted in job reductions and higher energy costs for U.S. consumers, while weakening national security. “There has been an increase in electricity prices in the United States of over 30 percent in the past four years,” said Rich Nolan, president and chief executive officer of the National Mining Association (NMA). “This administration wants to focus on that, inflation and on increasing electricity availability to help lower the price over time by incentivizing energy policy. Coal is definitely a high priority for this administration.” Part of the White House’s strategy is to facilitate investment in coal-fired plants. The shift comes at a time when the reliance of coal-powered energy plants has been waning. According to the U.S. Energy Information Administration, 7.5 GW of electrical capacity from coal-fired plants was retired in 2024, following the retirement of 22.3 GW of electric generating power the two years prior.

Jun 1, 2025

By F. Wang, P. Rosario, A. Vazirizadeh

Advances in data science, machine learning, and artificial intelligence are transforming mining and mineral processing, and making them more algorithm intensive. The paradigm is shifting from one of detection and control to one of prediction and optimisation. To build up reliable prediction and optimisation models these steps should be passed: collect, aggregate, cleanse, and process large amounts of complex, structured, and unstructured data as a first stage. Next, the right tools must be used to extract meaningful features from the operational data for the build-up of a prediction model. A grinding circuit is one of the challenging areas in terms of determining its overall efficiency and the prediction of product quality. In a processing plant, some of the KPIs are typically unmeasurable or infrequently measured for real-time application, e.g. ore grindability and grind size. However, these variables are required to calculate the grinding circuit efficiency and to model the operation performance. In this paper, advanced analytic tools have been used to extract one selected KPI (cyclone overall P80 or final grind size) and its relationship with measured variables for an industrial dataset. The results illustrate that the performance of the grinding circuit can be predicted for real-time monitoring by means of measured data. Keywords: Grinding circuit, data-driven, machine learning, process modelling, hybrid modelling

Jan 1, 2020

By M. Szumny, P. Mertuszka, K. Fuławka

Multi-face destress blasting is the basic method of reducing rock bursts hazard in Polish copper mines. Blasting operations of this type are carried out regularly, twice a day in order to minimize the geomechanical threat while maintaining high extraction efficiency. Unfortunately, the assessment of the effectiveness of this type of work until recently was based solely on determining whether a given destress blasting triggered a seismic tremor or not. Information on the characteristics of seismic waves generated by blasting has so far been omitted in analyzes of this type. The lack of reliable knowledge about the generated seismic effect makes the introduction of constructive corrective actions impossible, which in turn significantly reduces the triggering potential of conducted destress blasting. This study presents the results of comprehensive work aimed at increasing the effectiveness of active rock burst prevention in Lubin underground copper mine. For this purpose, an innovative system for continuous monitoring of seismic waves induced by multi-face blasting was developed, and a new method for seismic assessment of the effectiveness of the destress activities was developed. Based on the monitoring data and the comprehensive analysis of waveforms, the effectiveness of individual blasting operations was determined, and on this basis, modifications to the drilling and blasting patterns aimed at increasing the generated seismic effect were proposed. The use of innovative methods of monitoring and data analysis made it possible to increase the effectiveness of the conducted activities, in terms of the magnitude of seismic vibrations, up to 250%. It should be emphasized that the introduced modifications were aimed at increasing the effectiveness of blasting operations while maintaining the current level of costs. As a result, the proposed solution turned out to be highly effective, significantly affecting the safety of the work carried out in Polish copper mines. Thus the proposed approach is planned to be implemented in other Polish copper mines belonging to KGHM in the near future

Jan 1, 2024

By C Zhang, R Liang, I Canbulat, S Saydam, B Li

The integration of digital twins in mining represents a significant advancement towards enhancing operational efficiency and decision-making. Central to this development is the role of visualisation, a crucial element in mining digitalisation. This paper aims to develop a data-driven visualisation solution for the construction of mining digital twins, highlighting how this innovation revolutionises the representation of physical mining environments. It spotlights the shortcomings of conventional visualisation methods, particularly their inability to accurately depict complex underground environments and their predominantly historical orientation. To address these challenges, this paper proposes a novel, data-driven approach. This approach extensively utilises CAD files, encompassing data from geological surveys to intricate mine plans, thereby significantly elevating the precision of digital twins. It supports real-time updates and enables flexible interaction with a variety of data types. Moreover, it introduces a comprehensive data diagram alongside the adaptation of a 3D tile index. This strategy ensures the efficient rendering of extensive mining environments, allowing for a more dynamic and interactive digital twin model. In conclusion, the implementation of data-driven visualisation in the construction of mining digital twins marks a pivotal advancement in the industry’s digital transformation. It promises a more detailed, accurate, and dynamic depiction of mining operations.

Sep 1, 2024

By Xin J

In China, the State-run mines are currently practising an information card management system, to collect data about mine production and safety condition. Since large volumes of data will be obtained every day, efficiently and effectively processing data and sorting out valuable information will take enormous resources of manpower, if by hand. Therefore, an intelligent database system with easy data inputting was developed to assist information processors in data classifying, filing and retrieving. For analyzing and improving production performances, reliability models and production functions were embodied. The system was implemented on AST-286 in DBASE M. Preliminary applications have proved effective and successful.

Jan 1, 1991

By Deborah A. McCombe

"There are a number of important dates that must be disclosed when a Qualified Person (QP) is preparing a technical report for securities purposes. The dates are discussed in NI43-101 and the Form of the Technical Report, NI43-101F1. •,Effective date of the technical report •,Possible revision date of the report •,Effective date of mineral resources and mineral reserves •,Date of the personal inspection (site visit) •,Signature date of the date report (execution date) •,Date of the certificate of the QP •,Date of the consent of the QP The effective date of the report is basically the cut-off date for the information that is included in the technical report. The effective date should be close to the signature date. If there is too long a period between these dates, the QP runs the risk of new material information becoming available and then the technical report would no longer be current. The effective date is disclosed on the title page of the report and on the signature page. If a technical report needs to be revised to correct problems or disclosure deficiencies identified by the regulators, the QP will normally need to include a revision date on the title page of the report to distinguish the revised version from the original version. In these circumstances, the regulators will request that the original date of the report remain on the title page and the revised date will be below it. When a technical report must be revised, the QP must resign and date the signature page of the report and provide a new certificate and consent."

Jan 1, 2003

By McDougall I, Claoue-Long J

A combination of high precision 40Ar/39Ar and U-Pb dating has been used to determine the age of hydrothermal minerals and host rocks in a number of Lachlan Fold Belt (LFB) gold deposits in New South Wales, Au4 ralii In particular, this investigation demonstrates that the UAr/ Ar technique of K-Ar dating can be used to determine the timing of mineralization in Palaeozoic systems.A close temporal link between mineralization and Late Ordovician to Early Silurian magmatism is indicated by the dating work. Hydrothermal sericite from the Goonumbla p8rph gy Cu deposit and Glendale Au prospect yieldsAt/ Ar step heating ages between 437.9 ¦ 1.1 and 441.2 ¦ 1.1 Ma (16), and4 minjrnum age of about 425 Ma has been obtained by Ar/ Ar dating of hydrothermal amphibole from the Browns Creek Au-Cu skarn deposit. Hydrothermal aluniI8 frog the Gidginbung epithermal Au deposit yields reset 44 Ar/ Ar ages of 401.0 ¦ 1.3 to 417.3 ¦ 1.3 Ma (16). U-Pb dating by ion microprobe on magmatic zircons from the vicinity of the Goonumbla and Gidginbung deposits gives ages of 438 ¦ 3.5 Ma (2 6), and 435 ¦ 2.5 Ma (2 (7) respectimly, W a minimum age of 435 Ma has been obtained by Ar/ Ar dating of magmatic phases from volcanic rocks adjacent to the Browns Creek skarn deposit. Magmatic episodes near the Sheahan-Grants gold skarnpost give ages of 479.2 ¦ 1.1 and 467.9 ¦ 4o1 by 'Ar/ 9Ar dating of magmatic amphibole. Most Ar/3yAr age spectra show the effects of partial Ar loss at between 360-420 Ma, probably due to heating caused by granitoid emplacement.The radiometric dating work in this paper suggests a restricted metallogenic epoch in the Late Ordovician-Early Silurian LFB, NSW, of similar duration to that found in the Andean margin and the western Pacific island arcs around the Pacific Rini.

Jan 1, 1990

By Robert G. Ditchburn

The NW caldera hydrothermal vent field at Brothers volcano, of the Kermadec-Tonga arc, has associated massive sulfide mineralization. This includes an abundance of black smoker chimneys that contain significant concentrations of copper, lead, zinc and gold (de Ronde et al., 2005; de Ronde et al, 2010, under review). [ ] The oldest volcanic massive sulphide (VMS) samples recovered so far from Brothers have been dated using radiometric methods at GNS Science and found to be c. 1000 years old. By contrast, a volcanic cone that has formed in the southern part of the caldera has no associated VMS. Only elemental sulphur and Fe-oxide crusts are present here, samples of which have been analysed for their trace elements. From d34S values of sulfates and native sulfur, and the volume of gas (dominated by CO2 and H2S) discharging at the cone site, elements being deposited here are considered more directly magmatic in origin (de Ronde et al., 2005; 2010 under review). It has been discovered that deep-seated changes in volcanic activity at Brothers affect the NW caldera and cone site simultaneously, though the mineralogy at each site is so very different. Thus, the cone site is being studied to determine if elements being deposited there can be used as predictors of potential future VMS mineralisation as similar sites have been found at other volcanoes along the Kermadec arc and elsewhere. The frequency of these deep-seated magmatic events is considered important in the formation of Cu-Au-rich mineralization along arcs. The analysis of Fe within resulting hydrothermal plumes (surveyed by surface vessels), and trace elements in rock and mineral samples, are used to help determine the influence of magmatic contributions to the hydrothermal system. To verify that plumes are a reliable proxy for hydrothermal events depositing iron on the seafloor, Fe-oxide crusts from Brothers volcano cone have been dated to see if their emplacement coincides with highly Fe-enriched plumes in the water column. The radiometric dating techniques developed for barite-rich VMS, i.e. using 228Ra and 226Ra, were unsuitable for the Fe-oxide crusts because 210Pb was the only isotope detected by gamma spectrometry. Therefore, new techniques using 210Pb, 10Be and arsenic have been established.

Jan 1, 2010

By William Gleason

Since 2004, Dave Kanagy has served as the executive director and chief executive officer of SME. In that role, he has worked with 22 SME Presidents, traveling the world to represent the Society. His prestigious one-million-mile status with United Airlines is a testament to these efforts. He has been to six of seven continents while leading the Society on a journey from a struggling association in 2004 to the largest and arguably the most influential professional mining society in the world. In the following pages, some of those past SME Presidents reflect on their time with Kanagy and paint the picture of a visionary leader who was willing to make the hard decisions for the betterment of the Society while remaining open to new ideas and opportunities. One of his first accomplishments as the executive director was to streamline the SME Boad of Directors from 29 members to nine, making it much more effective. With a keen financial sense, he was able to add value while cutting excess to expand the Society in every measure. As the leader of SME, Kanagy helped facilitate much of the Society’s growth and innovation as the world’s largest mining related organization. He has a B.S. degree in industrial education from the University of Maryland, an M.S. degree in technology education from Eastern Illinois University and is a certified association executive by the American Society of Association Executives. Under his leadership, several unique programs were created or advanced, including OneMine.org, the world’s foremost online mining research library. He enhanced legacy programs and increased collaboration with international organizations. Kanagy is the recipient of prestigious awards, including the Colorado Society of Association Executives’ Professional of the Year award and the Mining and Metallurgical Society of America’s Gold Medal award.

Feb 1, 2026