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The first grant of land rights in Australia was pursuant to the Aboriginal Land Rights (Northern Territory) Act 1976. That Act has been at the core of the dispute between land rights and miners' rights. The mining industry believes that the Act, in giving a right of veto to Aborigines with respect to exploration and mining on Aboriginal land, has been responsible for a stagnation in exploration amd mining activity in the Northern Territory. In February 1985 the Commonwealth Government released for public comment its preferred model for national land rights legislation. The model has been opposed by the mining industry because it believes, first, that land rights is a matter which should be left to the States and, secondly, that the detailed mechanisms contained in the model constrain the industry in the manner of the Northern Territory legislation. Aboriginal leaders have opposed the model because it does not, in their view, measure up to undertakings given by the Commonwealth Government and, in addition, it provides for amendments to the Northern Territory legislation which are likely to result in the loss of the Aboriginal veto right. Are land rights and miner's rights compatible? The answer is yes provided all parties, including Governments, are prepared to talk and compromise and Governments recognise that the grant of land rights and any control by the Aboriginal people of exploration and mining on their land is not a substitute for raising the living standards of Aborigines as a significant group of disadvantaged Australians.

Jan 1, 1986

By D. L. Blakeman, Trimble. J. W., S. W. Banning

Noranda's Grey Eagle mine and mill, in the Siskiyou Mountains of northern California, began shakedown operations in the late fall and early winter of 1982. This paper describes some of the unique features in mineralogy, milling, and water treatment operation at Grey Eagle. In addition, major start-up problems in the mill and water treatment plant are discussed along with measures taken to correct these problems.

Jan 1, 1986

By Toussaint LF

A brief history of the mineral sands industry is given from a radiation protection standpoint, with gamma radiation and inhalation of radioactive dust being identified as the main sources of radiation exposure of persons working in the plants. The principal sources of gamma radiation exposure are identified and the history of early bagging methods is discussed. The concept of ALARA is introduced and the impact of the "bulka bags" (and more effective monazite storage facilities) are detailed as illustrative examples of dose minimization, together with the requirements for the transport of monazite. The dose from radiation received from inhaled dust is dependent on a number of parameters of the dust, some of which are not well known. ICRP 30 (1978) makes recommendations relating to the intake of radionuclides by workers. These recommendations are discussed with respect to the inhalation of dust containing thorium. The possible pathways of radioactive material throughout the body are also described. The effect of having parameters that differ from ICRP 30 is discussed and suggestions are made relating to measurements that can be carried out to more accurately determine these parameters. Since in some instances the exposures to dust yield personal doses which may exceed the annual ICRP 30 based limits, it is necessary to define more accurately parameters of the dust so as to determine more accurately personal Doses.

Jan 1, 1986

International tenders for contract mining opportunities require the Contractor to make an assessment of risk for an operation with a life span of up to twenty-five years usually based on data made available by the Client. This paper seeks to address how the Contractor assesses the data and quantifies the risks associated with the project. The areas of risk are common to most projects, but in the--Lontractor's case, risk assessment is carried out on the Client's data base and within a compressed time frame of usually less than six months. Incorporation of that risk assessment into the contract documentation is also discussed. Practical examples are given in a number of areas.

Jan 1, 1986

By Ted Schwinden

It's my pleasure to welcome the Rocky Mountain Coal Institute to Montana. Now that you've ended a 74 year history of meeting outside our borders, I hope you'll be back more often. Even if I can't convince you to come back before 2060, when another 74 years will be up, I'm confident the Kalispell and Flathead Lake area will do the persuading for me. In recent weeks, I've made a number of speeches around the state pointing out just how wrong the revenue projections in Montana's state government made in April of '85 were when we came to April of '86. Since the middle of this month, Montana's legislators have been in Helena struggling to balance a state budget that, by the end of fiscal year '87, threatened to be some $88 million in the red. Other western states reliant on natural resource and agricultural economies have found themselves caught in the same deflationary spiral. The "window of opportunity" I proposed and the 1985 Montana Legislature passed, couldn't have been opened at a more difficult time. What was envisioned as an incentive to expand Montana's coal production, and our tax base, ran aground on low oil prices and excess hydroelectric power. De- spite the one-third severance tax rebate offered on new coal production, year-to-date figures published in the June 2nd Coal Week show that production in Montana is down almost 10 per- cent from last year. Again, we're not alone. Production in Wyoming is down 8 percent and in North Dakota, it has dropped 13 percent. An over- all 3 percent production decline in western states looks even worse when contrasted against a 12 percent increase for your eastern competitors. Montana's "window of opportunity" had the potential to be a significant signal to your industry. That potential was quickly tempered by the inability of Arab nations to stabilize world energy prices. If any of us had forgotten, the last twelve months have reminded western states that the well-being of our natural resource economies has a tremendous impact on the ability of all levels of government to provide services. The past year has also driven home that in the world of business and in the business of the world, risk-taking is still the name of the game. The Western Governors recognize the problems facing this industry - and our states - and we're prepared to fight for the future of western coal. In the past two decades, legitimate environmental concerns have created a vast body of new legislation affecting coal producers. The industry has been required to meet new reclamation standards - tough regulations relating to ground water, air quality and wildlife habitat. To the industry's credit, coal companies have hired professionals with the technical know-how to do it right. Rather than considering reclamation laws to be an unreasonable burden, today's coal company is more likely to view its reclamation efforts as a source of corporate pride. In a sense, the industry and the agencies which regulate it have matured together . . . although at times OSM is reluctant to admit that either of us capable of or willing to play by the rules. Certainly some abuses exist and OSM's oversight has been helpful in ensuring parity between those states willing to comply and those that would rather defy federal law. Nonetheless, penalizing the more modem and responsible western coal industry for abuses that occur elsewhere is both unfair and counterproductive. Federal penalties range from "cruel and unusual" reporting requirements to the proposed electricity tax that would effectively force utilities and ratepayers who have already met the sulphur emission standards to subsidize those who have dodged their cleanup responsibilities. As a member of the Alliance for Acid Rain Control, I have joined corporate leaders, academicians, environmental representa-

Jan 1, 1986

By Brotzmann K, Turner RE, Philp DK, Rusden RC

New approaches in the last decade have led to the supplemental use of raw coal as a direct energy source in several iron and steelmaking processes. The dependence on more expensive coke and electricity is being progressively reduced. The Technology Venture of Klockner Werke and CRA has pionered several of these developments and is pushing ahead to processes for scrap melting and direct smel- ting of iron where coal is the ONLY energy source. Such complex developments depend strong- ly on the experience gained in the simpler but related processes for coal use already in operation. The longer term implications of a successful coal-based direct smelting process for the world steel industry are substantial.

Jan 1, 1986

By R. A. Bilonick

Sampling is used in all scientific, engineering, marketing, and financial disciplines. The scientific theory of probabilistic sampling is a well established field, providing a general framework for basic methods that can be applied in fields that would appear to have little in common. The revolution in sampling occurred, in part, as a response to the need for quality control in industry during the Second World War. Since this time probabilistic sampling methods have been replacing pre-scientific non-probabilistic methods of sampling. This has not been the case, however, in the mineral industries in the United States. No general agreement on a theory applicable to all sorts of particulate materials has been reached. As a result, non-probabilistic methods are still widely used and the measurement of sampling precision - the hallmark of scientific sampling - is unknown. Much of the research on particulate material sampling that has been carried out over the last 40 years can be characterized by a lack of theoretical reasoning and rigor, an unwillingness to acknowledge previous work, and a lack of integration with existing, established scientific sampling theory. Non-standard statistical terminology is used and often standard statistical terms are misused. The confusion that has resulted is reflected in ASTM and IS0 sampling standards and in the practice of particulate material sampling.

Jan 1, 1986

By Douglas F. Stevenson

Your committee doesn't assign any easy topics to us speakers. How do you define "successful" in what is essentially a losing game? I am reminded of the fellow who returned from a one-week vacation in Las Vegas, and proudly stated, "I only lost $150." To him, that was "success." I am afraid that workers' compensation is a losing game for employers, too, and success has to be defined in terms of how much or how little is lost. We have no chance of producing a profit for our employer. We will narrow down to some suggestions about conducting a workers' compensation program at the plant level, but first I want to discuss the general legal, social and legislative climate in which your plant program operates. Workers' compensation is a statutory program with its general parameters established by the legislature. These parameters are then amplified by the boards and commissions established to administer workers' compensation. What influences the legislature and the boards and commissions as they establish the framework in which we must function?

Jan 1, 1986

By Michel Mellinger

This paper presents preliminary results of a project aimed at obtaining a better understanding of the genesis of gold mineralization in northern Saskatchewan using lithogeochemistry. The properties sampled were: Weedy Lake (B Zone), Waddy Lake (Komis Zone) and Wedge Lake (Twin Zone) in the La Range belt, and the Rio mine in the Flin Flon area. Although the three vein-type deposits rudied here (Weedy Lake. Waddy Lake, Rio deposit) occur in different geological situation . a common gold deposition mechanism appears to have been active at all three location . Hydrothermal fluids carrying sulphur in solution in exces of gold thio-complexes moved through fractured host-rocks until they reached a site where pyrite precipitation wa triggered. As pyrite precipitated, the a/ aAu activity ratio of the fluid decreased. until a critical thre hold was reached and gold thio-complexe were de tabilized . resulting in the co-precipitation of gold and pyrite. The conditions triggering the precipitation of pyrite appear to have been: the abundance of iron in host-rock ~ilicates at Waddy Lake. and temperature decrease upon the reaching of structural trap~ at both Weedy Lake and the Rio deposit. with additional involvement of carbonatizationrelated reactions between fluid and host-rocks for the latter. In addition to pyrite precipitation , potassic alteration of the mafic metavolcanics is observed at Waddy Lake. while at the Rio deposit, the extensive carbonatization of the host-rocks i coupled with the deposition of chlorite that may have been remobilized from the altered mafic host rocks. In both the B Zone at Weedy Lake and the Komis Zone at Waddy Lake, a di tinct vanadium signature give some clue about the direction in which the hydrothermal fluids have moved . and the interpretation made is con istent with the geological ituation in either case. As for the Wedge Lake deposits, the distinct base metals-precious metals a sociation within a sulphide-facies iron formation confirm the volcanic exhalative origin of this gold mineralization .

Jan 1, 1986

By Brian W. Mackenzie

The paper addresses five questions concerning the economic of gold exploration in the western Canadian shield. Can historical footprints be used to project future gold prices for exploration planning? Experience suggests that gold price forecast are too clo ely tied to current market events to provide meaningful guidelines. What range of breakeven gold price will typically be required to ju tify gold exploration in Canada? Minimum price requirements. initially asses ed at $540 to $1080 per ounce, probably lie in the range of $480 to $760 per ounce (expressed in 1985 Canadian dollar). Within Canada, what role do regional conditions and taxation policies play in determining the viability of prospective gold exploration targets? Striking interprovincial differences are found to exist, given the same geological circum tance -Nova Scotia, ew Brunswick, and Quebec rank towards the top in term of motivating exploration, Briti h Columbia provides the least investment incentive, while Saskatchewan and Manitoba place around the middle of the Canadian league. How can minimum acceptable tonnage-grade conditions for gold targets in the western shield be established and, then, constructively applied to provide exploration planning guidelines? Minimum target conditions, realis tically defined in terms of a broad band of doubt, can be used judiciously to help balance the direct costs of uneconomic exploration with the opportunity costs associated with rejecting economic deposits. How does the average cost per ounce of gold discovered in the western shield compare with the overall Canadian experience? Average discovery costs for the we tern shield, apprai ed at $100 per ounce, are shown to compare favourably with the Canada-wide record.

Jan 1, 1986

By Rodney R. Koch

The phys ica l properties of gold should faci li tate its detection. However, the average concentration of gold in the ea rth's cru st precl udes the ability of geophys ical methods to detect the metal directly. In order to effectively pursue geophys ical detection and location of gold mineralization there must also be geological input - i.e. a model. This will lead to the selection of the appropriate geophy ical techn ique(s) in the fo llow-up programs. Interpretation of the fie ld survey data is of con iderable importance in expl oration geophys ics. Analytical techniques such as cont inuation, fi ltering, and spectral analys is, are now routinely empl oyed in the analys is of survey data to aid the interpretation process. The uccessful application of geophysical methods and the use processing techniques is demonstrated by two recent Saskatchewan discoveries. The first, the Star Lake 21 Zone, with reserves of 230,000 tons grading bener than 17.14 git Au associated with a disseminated sulphide quartz-bearing shear zone yielded geophysical anomalies that were distinctive, obvious, and would have lead directly to its discovery. The econd , the Laurel Lake Prospect, with ore grade intersections of 28.0I git Au and 52 .46 git Ag· over 5.49 m, resulted in numerous geophysical anomalies. However, none of these anomalies demonstrated association with the mineralization until processed results indicated that structural features crosscuning regional trends were important cont rol mechanisms for the mineral ization.

Jan 1, 1986

By Gerald C. Smith

Introduction Newmont Mining Corp.'s Carlin Gold Mining Co. mines gold ore and waste material from four open-pit mines in Northeastern Nevada. Combined production rate is 24.5 Mt/a (27 million stpy). The company is producing gold bullion from the 2.4 kt/d (2600 stpd) Carlin Mill No. 1. and the 2.3 kt/d (2600 stpd) Maggie Creek heap leaching plant. In August 1985, the 6.3 kt/d (7000 stpd) Carlin Mill No. 2 at Gold Quarry came on-line. The total combined throughput of 11.2 kt/d (12,000 stpd) will make Carlin Gold Mining one of the largest US gold producers. Feasibility studies are underway to increase the total gold out-put from Gold Quarry. This will be done by adding a continuous dump-leaching process that will recover gold from lower grade ore. History In 1961, Newmont exploration geologists located claims in the Lynn Creek mining district near Carlin, NV. An exploration drilling program revealed significant gold mineralization. A stepped-up drilling program revealed an ore body with an estimated 10 Mt (11 million st) of ore containing 109 t (3.5 million oz) of gold. Test results in 1963 allowed metallurgists to develop the flowsheet and design for a 1.8-kt/d (2000-stpd) countercurrent decantation (CCD) cyanide plant. Carlin Gold Mining Co. was formed to operate the property. Carlin purchased its first mining equipment in 1965. This consisted of two Bucyrus-Erie 2.7 m3 (3.5 cu yd) shovels and nine 27.2 t (30 st) Wabco Haulpak trucks, two Inger¬soll-Rand track-mounted drills, one Chicago Pneumatic T-650 rotary drill, one rubber tired Michigan 280 dozer, one Caterpillar D-8 36A track dozer, one Caterpillar 16 motor grader, and the necessary ancillary equipment. The fleet was capable of mining 3.2 Mt/a (3.5 mil¬lion stpy) working two eight-hour shifts, five days a week. In 1967, one P&H 1500 3.8 m3(5 cu yd) diesel electrical shovel and three 59 t (65 st) Dart trucks were added to the fleet, along with a Chicago Pneumatic rubber tire mounted rotary drill. This equipment addition increased the mining capabilities to 5.7 Mt/a (6.3 million stpy). Three separate pits, the Main, East, and West, within a 1.6-km (1-mile) radius of the Carlin mill were being mined. The crew consisted of 46 hourly men and three supervisors. This equals 497 t/ total hourly manshift (548 st) or 407 t/total manshift (515 st). Rising gold prices in the early 1970s allowed lower grade deposits to be mined at greater distances from the mill. The Boot-strap pit, 19.3 km (12 miles) north of Carlin, was started in 1973. Two Hough 45.4 t (50 st) front-end loaders, four Wabco Haulpak 45.4 t (50 st) trucks, and a second track type D-8H dozer were added. The higher grade ore was trucked to the Carlin mill using three

Jan 11, 1985

M.S. Azun I have many objections to the content of the author's paper. Before discussing it, however, I would like to repeat the property of semivariogram function. Second order stationary properties of regionalized variables (ReV's) such as semivariogram function y(h) are perfectly known in geostatistics. Also, the kriging equations in the language of mathematical statistics using second order stationary properties are well understood. However, the way to use the sample (estimated) semivariogram function in any one of the kriging procedures is vague. The sample semivariogram function is given as follows: [ ] where N is the total number of samples, Xi is the sample value at the i - th location, Xi+h is the sample value at the i + h - th location, and h is the distance among the samples. An estimation variance of sample semivariogram function of first lag is smaller than that of higher order lag. The theoretical semivariogram function reaches the variance of samples asymptotically. But this is not easily observable because of the larger variation involved in the estimate of semivariogram function. In general, an estimation procedure is done for h = 0, 1, 2, up to the greatest integer less than N/2, even though sample semivariogram function can be computable through N-1. After estimating semivariogram function, the critical question of how to model sample semivariogram function arises. As seen in the above equation, sample semivariogram function is discrete and can be smoothed by the model being selected. Therefore, modeling of sample semivariogram function is the most important step in geostatistics. It not only smoothes a discrete function but also affects the results of the kriging procedure. When the only aim is to model the semivariogram function, which is the basic point of the author's paper, one can employ any fitting techniques, such as curve fitting, or any arbitrary functions, which are called submodels in the paper. The term "arbitrary function" is used rather than "submodel" because there is no basic understanding of developing them. The author suggests that the sum of those submodels can also be used for the modeling of sample semivariogram function. The combination of any arbitrary functions brings many problems instead of giving an insight of the domain structure considered. The author used two arbitrary functions and the nugget effect in response to sample semivariogram function (Fig. 10). For the same example, he stated that the parameters involved in the mixed arbitrary function model can be accepted when the discrepancy between sample semivariogram function and the model is small visually. For verifying the fitting behavior of any selected model, one should not be contented with the visual satisfactory. Some statistical measure such as goodness of fit has to be used. The author's practice is no more than an exercise in curve fitting without any fundamental understanding or conceptualization of the underlying physical mechanism. Furthermore, the selection of any model is not an easy task if the purpose is the search for the "best" response to the observed second order properties of ReV's. I suggest that the Markovian model (Azun, 1983), on the basis of a theoretical understanding of underlying mechanism, which gives more information about the occurrence of regionalized variables, is used to respond all properties of ReV's. There are a lot of problems for modeling of one-dimensional sample semivariogram function. Thus, it is not appropriate to go to higher order dimensional sample semivariogram function modeling. In the meantime, I would recommend that one can connect the values of standardized sample semivariogram function rather than simple values of semivariogram function in the two-dimensional estimation. The standardized values can be computed in dividing the semivariogram function value by the number of sample pairs involved in each lag regardless of the directions. In conclusion, geostatistics is an interdisciplinary area in mining that uses the principles of mathematical statistics. Thus, it should not violate any probabilistic and statistical rules. When Matheron was developing the theory of geostatistical study in the early years of geostatistics, many mining people had a reservation accepting the geostatistical tools. However, this does not mean that we, the geostatisticians, might try to convince those people using some "strange" tools or rules as some authors implied (Baafi and Kim, 1984). Instead, we have to develop and explain the geostatistical tools staying only in the framework of statistical concepts and properties. References Azun, M.S., 1983, "Stochastic Process Modeling of Spatially Distributed Geostatistical Data," Columbia University, Ph.D. Thesis. Baafi, E.Y., and Kim, Y.C., 1984, "Discussion - Comparison of Different Ore Reserve Estimation Methods Using Conditional Simulation," Mining Engineering, Vol. 36, No. 3, p. 280. Reply by J.M. Rendu The interactive method proposed by Rendu allows practitioners to develop semivariogram models that take into account not only the numerical information obtained by sampling, but also highly significant additional information that often cannot be quantified. The geology of the deposit - including hypotheses concerning its genesis, sampling methods, assaying methods, and mathematical methods used to calculate the semivariograms - all have an influence on the numerical results obtained and on how these results should be interpreted. If all the information concerning the spatial distribution of values in a mineral deposit was contained in the sample values, it could be argued that statistical techniques alone would produce optimum models. However, this is rarely, if ever, the case. Methods that allow the user to take into account his experience and his geologic understanding of the deposit should not be rejected for the sake of theoretical statistical purity.

Jan 11, 1985

By Russell L. Wood

Introduction It is the morning after and there is one monumental hangover among many, if not most, of the oil companies that invested in mining in the past decade. How an oil company looks at mining depends on the time period. A few years back, around the time of investment, they were probably looking at a balance sheet of their own that was beginning to go wild. Many of their commodity prices already increased two-, three-, or fourfold, with no end in sight. There was a need to invest their earnings someplace before the stockholders or the government got too interested. And that someplace needed to be an industry where their own management skills could be applied. In mining there was an industry amazingly similar to their own. Both worked with nonrenewable natural resources, dealt with the same types of government agencies, and had the same type of critics and pressures from the general population. Oil executives saw mining as an industry that was somewhat backward as compared with their own. They also saw how a generous ration of the capital and management skills, so handsomely bestowed upon the oil industry, could be used to great advantage. But, looking at mining through the eyes of an oil company today, this is what might be seen: • An industry that has been spectacularly unprofitable for the past few years; • An industry of many commodities, any of which can go bad on short notice for protracted periods, for a variety of unpredictable and uncontrollable reasons, or for no reason at all; • A labor-intensive industry, often beset by labor problems. And with only limited potential for further economic mechanization or automation in today's primitive mining environment; • An industry confronted with external, foreign, and political problems, with no reasonable expectation of near-term relief; and • An industry that does not appear to have many "up" periods in its immediate future. And, there is little chance for either big, fast bucks or long periods of large, steady earnings. What Went Wrong? The visions of a few years back seemed as correct then as today's viewpoints seem to be now. What happened to change these views? What went wrong? The simplest and easiest answer is that if the economy was normal back then, nothing would have gone wrong. This is an easy answer but, unfortunately, probably incorrect. Let us take a broad overview. Certain events enabled and prompted oil companies to invest heavily in mining. These same events were major factors in precipitating the worldwide recession that has grievously injured mining and has brought those mining investments into disrepute. Taking a somewhat narrower, but still broad overview, the simi-

Jan 3, 1985

By E. Tulcanaza

M.S. Azun I do not at all agree with the basic points of the author's conclusion. The use of lognormal or normal model to respond to the attribute distribution function should be carefully questioned. If fitting a distribution function of regionalized variables is the main purpose, one may employ one of certain well-known families of distributions, such as Tukey's lambda functions. For instance, the distribution function of gold assay values studied by Krige (Krige, 1978) may fit the lognormal model well, but one should not exclude that the basic idea of using the distribution function of observed data is to have insight of the physical mechanism that gives rise to the regionalized variables. In Applied Geostatistics, there are many examples considering only lognormal or normal model for the distribution function of geostatistical data even if the observed distribution function has more than one mode. Of course, using lognormal or normal model always brings easy computation for the statistical properties of attribute under investigation. Still, I object to adopting any of those models without having any "statistical inference." As seen from the author's paper, lognormal and normal model produce the same average grade (0.746% copper) and almost the same sample variance. Therefore, which model explains the probabilistic behavior of ore deposit is always an important question that should be replied by the geostatistician using not the common sense but the statistical and probabilistic methods. For kriging procedures, such as linear kriging, one should describe either second order stationary properties or bivariate properties of regionalized variables. Second order properties, such as correlogram function or semivariogram function, are the inputs of the so-called kriging equations. Selecting one of any useable models for the observed second order properties, such as spherical model, is not easy since the models are not based on the correlations structure of the regionalized variables. How those models being used in Applied Geostatistics can be distinguished is another important problem. One may develop many fitting models to respond to the observed second order properties of regionalized variables. However, I suggest that the model should be based on the probabilistic behavior of geologic process. The author used spherical model for copper, wolfram, and silver grades from isotropic sector (Fig. 2, 4, 3b) and exponential model for silver grades obtained along raises in isotropic sector (Fig. 3a). It seems that those semivariograms given in Fig. 3b for isotropic sector and Fig. 4 may be described by the random model implying that regionalized variables do belong to renewal process (Azun, 1983). If I knew the total number of data points used in estimation, I may prove that my conclusion is correct. Recalling the test for the first order sample correlogram value, the test procedure is introduced such that the independence is rejected if [ ] where Za/2 is the a/2 percentile of standard normal random variable and N is the total number of samples used in estimation (Azun, 1983). For the other observed semivariogram functions I suggest the author might try to use the so-called "Markovian model" describing not only the correlation structure of regionalized variables, but also the physical mechanism that produces the regionalized variables. The Markovian model for correlogram function is, [ ] The Markovian model for the other second order properties of ReV are also derived. T ve B, in the above equation, show, for example, structural change and mineralizational variation in a considered deposit, respectively (Azun, 1983). In selecting any model, it is not easy to search for the "best" response to the observed second order properties of ReV's. The Markovian model, based on a theoretical understanding of underlying mechanism, gives more information about the occurrence of regionalized variables and respond to all properties. Random model and the so-called hole effect structure can be easily defined as a special form of the Markovian model (Azun, 1983). An estimation of any second order properties of regionalized variables may be computed through (N-1) lag. However, the dependency between the regionalized variables may be deemed to have "died out" after the so-called stationarity width (range). In practice, the estimation is carried out through 25% of the total number samples available. Therefore, the large fluctuations around the zero level and variance for correlogram or covariogram function and semi-variogram function, respectively, cannot be observed. Since the number of pairs involved in the estimation of higher second order properties is small, the estimation variance at those lags is large. There is no need to show higher order estimated second order properties. After using one of any kriging procedure for block estimation, the distribution function of average grades has the same mean as the drilling sample grades but

Jan 3, 1985

By R. Kent Comann

Introduction This article describes ways to maximize employment potential for those looking for a job in the minerals industry. Advice on the right way to prepare a resume is followed by tips on where and how to look for a job and ways to make the best of an interview. Information on current salaries is also included. RESUME NAME: Give home and college addresses and telephone numbers, ADDRESS: if still in school. Otherwise, give home address and TELEPHONE: telephone plus business telephone number. JOB OBJECTIVE: What do you want to do? EDUCATION: List degree(s), name of college(s), location(s), and date(s) degree(s) received or to be awarded. Do not list your high school but do include postgraduate education, training, or seminars. WORK EXPERIENCE: List your most recent experience first, and the rest in reverse chronological order. Include dates (month and year) and company names. First show the entire time spent with each employer, then list the various jobs (with dates and duties) you may have had with this employer. MILITARY SERVICE: List dates, branch of service, and duties. If you did not serve, delete this section. COLLEGE ACTIVITIES: Show organizations, sports, and offices held. Only list these if you are a graduating senior, otherwise delete this section. PERSONAL DATA: List age, marital status, height, weight (optional), foreign languages spoken, professional memberships, and health (if good or excellent, otherwise do not mention it in the resume). If you prefer a certain location and would not accept a job anywhere else, list the location desired. Otherwise, do not restrict yourself. Also list special skills, hobbies, or interests that will help sell you to the company. Be brief in this section and use good judgement as to what you say. REFERENCES: Furnished on request. Resume Guidelines There are some things to remember when preparing a resume. These include: • Limit your resume to one page, unless you have a lot of experience. Then it should be a maximum of two pages. • Use 8.5 x 11 plain bond paper, white or off-white. Stay away from colored paper, odd size paper, and cute resumes. They are counter-productive. • Make certain your resume is typed professionally. Be sure to check for spelling, grammar, and typographical errors. • Have your resume reproduced by copier or printing shop. Do not use carbon copies. • Keep your sentences and remarks short and punchy. Use action verbs. • Have a good layout that is easy to read, brief, and uncluttered. Allow some white space in your resume. There are also some things you should not do when preparing a resume. These include the following: • Do not include a photograph or mention your religion, race, or sex. • Do not leave gaps of time unaccounted for in your resume. • Do not include your college transcripts with your resume or list all the courses you took in college. • Do not list all the articles you have written. Only list one or two, if appropriate for, say, a research and development job. • Do not include reference letters with your resume. Any good, prospective employer will make telephone reference checks. • Do not use "Curriculum Vitae," use "Resume." • Do not include unnecessary data such as your social security number, passport number, or PE registration number. If you are a PE or EIT, be sure to mention that in your resume. Do not mention US citizenship or US work permit unless you have a foreign education or are not a native US citizen. • Do not include present salary or salary requirements. This will come out in the interview. • Do not use the word "I" in your resume or refer to yourself in the third person. • Do not lie, distort, exaggerate, or editorialize in your resume. • Do not have your resume prepared by an outside resume service. This is unnecessary, costly, and generally not effective.

Jan 2, 1985

By R. J. Roman

Introduction This paper attempts to describe the characteristics of the industrial minerals segment of the minerals industry that make it different from the other segments. Also described are the trends, strengths, and shortcomings in the industrial minerals segment. This may help professionals in the minerals industry determine if there is a place for them in industrial minerals. Industry Overview It is difficult to generalize when describing the industrial minerals industry. Some products, such as cement and potash, are produced exclusively by large, well capitalized and technically sophisticated companies. Others, such as sand and gravel, are generally produced by small, family-owned companies operating on a shoestring budget. Still other products, such as feldspar, are produced by both types of companies. Not only are the companies vastly different, the products and marketing of their products are also different. Industrial mineral products are sold based on their physical characteristics (abrasives), or their chemical characteristics (trona), or both (glass sand). Marketing can be direct by the producing plant, a corporate office handling products form several plants, or through distributors. The geologic, mining, and processing skills needed can be minimal or sophisticated. There can even be large differences in the technical skills needed in two plants producing the same product. One feldspar producer may only need to mine and grind his ore, while another may use a delicate flotation process. One easy generalization is that there are industrial mineral operations in all geographic locations. A second is that industrial minerals offers more opportunities to entrepreneurs than the metals field because of the variety in the sizes and types of operations. Industry Growth Industrial minerals must be considered a mature industry. Consequently, little exciting growth can be expected. Over the past 30 years, industry sales have grown at a rate of 6.2%, compared to a 6.7% growth rate of the GNP (Kliff, 1978). A 3.8% growth rate has been forecast for the remainder of this century. The fortunes of those plants producing a product for local consumption will depend on the prosperity of the local economy. The fortunes of those plants that sell into the national market will depend on their competitiveness in their industry. Major industrial mineral products are shown in Table 1. The first three commodities account for 66% of the industrial minerals market. Typically, deposits of stone, sand and gravel, and the limestone, silica, and clay needed for cement are easy to find and mine. Role of Mining Engineers and Geologists Because a sophisticated techcical knowledge of geology or mining is not required, very few geologists and mining engineers are employed in industrial minerals. Geologists working for these companies function less as geologists than managers and executives dealing with permitting and land acquisition. Likewise, mining is done mostly by open-pit methods. It is generally supervised by an individual experienced in the operation and maintenance of heavy equipment, not a mining engineer trained in rock mechanics or computer methods of mine planning. What of the geologists and mining engineers employed by the industrial minerals industry? Many gained their positions as a result of their experience dealing with the regulatory agencies that control the operations of all mines and mills. Their technical expertise in geology or mining becomes only a side benefit. Finding and mining industrial minerals does not generally require the concentrated efforts of geologists and mining engineers. But the processing of the ores does

Jan 2, 1985

By Donald E. Zipperian

It has been recognized that froth flotation of mineral particles utilizing rising flow of micro size air bubbles, counter-current to quiescent falling flow of particles in a column type cell, provides a unique mechanical atmosphere conducive to extension of froth flotation process to much coarser and finer particles sizes than possible with mechanically agitated and aerated cells. 1,2,3,4. This paper describes metallurgical results obtained at several plant installations of large capacity commercial size Deister FLOTAIRE® Column Flotation Cells. These cells utilize air aspiration into surface tension lowered water to generate micro size bubbles. Operating costs, power, wear, and scaling characteristics of rougher, cleaner, and scavenger units are compared with mechanical units. Problems encountered and scale up criteria developed when increasing from 0.20m(8inch) diameter pilot cells to 2.42m(96inch) commercial cells are discussed.

Jan 1, 1985

By G. E. Tooker

This paper (1) presents new data on the use of horizontal curves to optimize the alignment of belt conveyors. The operating concept of a horizontal curve is simple. As an element of the belt passes through the curve, it is motivated to drift toward the center of the curve by unbalanced tension forces in the belt. This lateral drift is resisted by the forces of friction and gravity, which in turn can be stated as a function of drift. Resisting forces, as a function of drift, are stated in terms of variables such as the amount of load on the belt, and parameters such as the trough angle, tilt angle, and super elevation angle, and number of rolls in the idler. The trick is to determine that the belt will drift to a position of equilibrium within predictable limits, without physically constraining the edge of the belt. In practice this prediction is not easy to make without the aid of a computer. The motivating force is easy to calculate, but its value is continually changing throughout the angle subtended by the curve arc. The resisting forces are constant throughout the curve for a given drift and load condition, but the calculations are very complex. Data in this paper will assist the conveyor designer in making preliminary calculations for horizontal curves. Presented is the equation for calculating the motivating force, and a family of curves that show the resisting force as a function of lateral drift for some commonly encountered conveyor design criteria.

Jan 1, 1985

By Robert J. Collins

Computer-aided design drafting systems have been employed in plant design for nearly ten years. Current and future systems will include computer-aided engineering as well. This paper will explore the history and development of such systems including computer and workstation hardware, graphics software, and plant design applications software. The paper will also address future systems and will present both the system vendor and the mine mill designer's view. Specific examples of particularly successful applications will be included.

Jan 1, 1985