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By Richard P. Sheldon

In the last several decades, techniques of data collection have been rapidly evolving. Automated spectroscopic techniques in the chemical analysis of rocks for example have advanced to the point that we can analyze samples faster than we can grind them. Remote sensors of LANDSAT give us intensity measurements in four color bands of every acre of the U.S. every 19 days. Common depth point seismic instrumentation systems used at sea for surveying and networks of seismometers arrayed around the country for earthquake studies likewise are generating vast amounts of data. These along with many other earth science data generating systems have caused a veritable data explosion. How do we deal with these enormous amounts of geologic data presently being produced as well as those produced in the past, and to be produced in the future? There is an immediate need to acquire and utilize past and present basic data in order to address today's national earth science problems. Also data being collected and stored now must be compatible with future data in order for us to be able to undertake the wide spectrum of analysis that must be made in the future. This is a big order! In order to organize and to be able to analyze these data, the USGS has embarked on a wide ranging program of compatible data base construction and the development of computer technology, based on interactive computing, to facilitate resource assessments and analyses. These activities are analogous to two sides of a coin. One side deals with the logic, philosophy, and mechanics of information storage and retrieval; and the other side deals with the sensible application of computer technology to the treatment of data. These

Jan 1, 1977

By Rudolf E. Greuer, Linneas W. Laage, Xinton Chang

Data or input files are used to describe the mine's ventilation system and physical characteristics necessary for ventilation calculations as well as the physical or mechanical disturbance to be modeled and simulated Data files are organized into lines of categories or groups of information Each line contains multiple sets of information or data items Data lines are read by the program, and the data items provide information to the program variables The data file is in American Standard Code for Informa¬tion Interchange (ASCII) format, sometimes called a DOS text format Creation or modification of a data file is accomplished with text editor software Most word pro¬cessors are also capable of making the required ASCII type files A sample data file and its associated output are included in appendix B. When selecting software for editing the data files, the user should be aware that the software will also be used for viewing the output of the simulation The output file can be quite large (several megabytes), if a lengthy simulation is performed The output can be sent to a printer for a hardcopy fisting, however, the user should also be aware that most printers currently used with PC's print at rates of 100 to 300 characters per second and large output files may take several hours to print Viewing the output with a text editor can save much time and paper DATA FILE MANAGEMENT The data files describing the event and mine to be simulated can reside either on a floppy disk or in a directory on the hard disk drive When the program starts, the user is prompted for the data file location and name The user response is in the form of a standard DOS path and file name For example, if the data file is on a floppy disk located in drive B and the disk has a directory MYMINE with the data file MYDATA DAT in that directory, then, at the prompt

Jan 1, 1990

By Christoph Muller, Geoffrey Liggins, Mohan Hensman

Modern commercial blasting is as much a technical process as it is a business one. As with most processes both aspects of blasting are driven by and generate new data. The time-critical nature of commercial blasting in mining and construction projects, dictate the timely exchange of information so as not to impede production or commerce. Up-to-date information about the blast to be undertaken can help suppliers: meet customers’ expectations to address challenging ground conditions; react to a market where quickly changing orders occur regularly; and address as-drilled blast patterns as opposed to baseline plans. “Real-time” data exchange into and out of the blasting process supports the whole-of-mine data management that the industry is migrating to.

Jan 1, 2010

By M Reisi, H Waterer, M Savelsbergh, F Engineer

The Hunter Valley Coal Chain Coordinator (HVCCC) Limited is the primary planning organisation responsible for the export of coal through the Port of Newcastle, Australia. HVCCC stakeholders include producers (mining companies), rail track owners, above rail operators, and terminal operators. The HVCCC is tasked with coordinating the activities of all the corresponding parts of the coal export supply chain so as to maximise the system efficiency. The HVCCC also has a forward looking planning role: with demand for coal expected to double in the next decade, strategic capacity planning is a core activity for the HVCCC. Optimisation and simulation models are key aids in such activity. They are used to suggest and evaluate infrastructure expansions and operating policy changes. These models require input data in the form of shipping stems, which are arrival streams of ships at the port, together with their cargo types and composition, and date and time of arrival.Although everyone is familiar with the expression ægarbage in equals garbage outÆ, relatively little research is dedicated to the issues surrounding input data generation. In this research we seek to redress this, and have developed a method for generating high quality shipping data. Shipping stems used as input should be realistic, reflecting key characteristics of historical patterns. They should accurately represent future demand scenarios. Yet they should also have controllable parameters that can be used to test what-if scenarios, such as are required by the HVCCC to answer questions about future infrastructure or operational plans.Here we review our process for achieving this, and give a case study analysing the effects of different shipping stems on the performance of the coal chain, as measured by a capacity assessment tool. The latter is an optimisation model that takes shipping stem and infrastructure data as input, and calculates key supply chain performance metrics. The potential benefits of alternative demand management strategies are analysed by measuring the performance of the coal chain given different shipping stems obtained via the stem generatorÆs controllable parameters.

Sep 26, 2011

By S. Dessureault, J. Porter, M. Woodhall

Information and communication technology (ICT) is seen as a key source of future productivity improvements in mines. The information technology infrastructure (ITI) necessary to achieve these productivity improvements will come in the form of data integrated from multiple source systems throughout the mining value chain. Two pure approaches are available: supplier-controlled or operations controlled data structures; each strategy has its own advantages and disadvantages. Case studies involving the South African mining industry are used to demonstrate the advantages of using hybrid-controlled data structures. Depending on the level of operational control, new skills are also necessary to be learnt by mining professionals including: information engineering; creating consistent and quality data; and process improvement techniques that use the integrated data for improving the operation.

Jan 1, 2004

By A. K. K. Rompel, G. K. Chunnett

Introduction Structure appears to be of secondary importance to the Bushveld Complex when viewed from a distance. However, a closer look already reveals that large fault zones bound the Bushveld Complex. These fault zones are deep-seated crustal lineaments of continental magnitude such as the Thabazimbi-Murchison Lineament, the Johannesburg-Barberton Lineament, the Palala Fault zone and the Rustenburg fault. Furthermore, the Bushveld Complex is transected by large faults such as the Vlakfontein Fault, visible in the Pilanesberg, the Steelport Fault and the Laersdfrift Fault in the Eastern Bushveld, the Brits Graben, and a multitude of dyke generations (Figure 1). When pursuing structures on a project scale, the lack of outcrop, particularly in the western part of the Bushveld, is of no help for the exploration geologist. Consequently, the first dataset that needs to be considered is the Landsatimagery. Older Landsat TM 5 or newer Landsat ETM 7 will immediately reveal areas of outcrop for field visits, and, after further processing, indicate the geological units, in particular the platiniferous Critical and the Main Zone of the Rustenburg Layered Suite (Figure 2). Airborne magnetic surveys are the subsequent dataset necessary for any decent interpretation of the geology under the ?Black Turf? cover. Currently Anglo Platinum acquires aeromagnetic surveys with a flight height of 20 m and a line spacing of 50 m. These result in high-resolution imagery absolutely crucial for dyke delineation and dip modelling, stratigraphic layer detection, IRUP detection and possibly pothole discovery and fault delineation (Figures 3 and 4). However, the aeromagnetic surveys and the Landsat imagery reveal structures very much confined to the surface and may be hundreds of metres above the platiniferous reef horizons. This may lead to inaccuracies. When determining the underground structures for shaft sinking and mine planning, a 3D seismic survey is advisable. Too many shafts have been sunk in the Witwatersrand Basin for gold and in the Bushveld for platinum without sufficient information of the structure beneath. Recent 3D seismic surveys in the Bushveld have proven the technology and visualized the reef horizons and led to an interpretation of the underground structure (Figures 5 and 6). [ ]

Jan 1, 2004

By S. Goodman

Successful exploration for and exploitation of a mineral deposit requires an accurate understanding of the deposit architecture in 3D, and of the development of the mineralizing system through time, which requires a thorough comprehension of the structural framework of the deposit. The integration of geological, geophysical and geochemical datasets to arrive at a 4D structural framework is well-established and has been successfully applied by SRK Consulting in projects from deposit to regional scale. In some cases, we can go beyond this, applying the SEEBASETM technique, which utilizes a ?bottom-up? approach to basin analysis. SRK?s approach primarily relies on the interpretation of magnetic and gravity data, calibrated with many other datasets including mapped geology, topography, event histories, wells/drillholes and seismic data. The SEEBASETM model is a culmination of this data integration and is produced through an iterative process of structural interpretation and geophysical modeling, where an interpretation of basement composition and 4D structure is used to constrain depth-to-basement geophysical modeling, and the modeled depths are then used in conjunction with the structural interpretation to constrain contouring of the basement topography. The result is a rigorously calibrated, 3D view of the basement surface, which can be used to understand the basement and basin architecture, and thus serves as a predictive exploration model. This technique, which was developed for use in the petroleum industry, is now finding success as a predictive tool in mineral exploration.

May 1, 2003

Today?s Topics ? Data to decision value chain ? Practical Considerations ? Example ? Before / After DSS ? Quick Decision Support Demo Practical Considerations ? More data is not necessarily better ? Organization and process silos ? Good vs. ?optimal?, but impractical Solutions

May 1, 2008

By Michael G. Nelson, Amy Richins, Ashley Hodgson, W. Pratt Rogers

"Sociotechnical systems are everywhere in the modern industrial world. Sociotechnical systems describe how humans interact with technology throughout a variety of processes. These systems are prevalent in modern industrial settings, such as operating mines. Safety and health management systems (SHMSs) are complex sociotechnical structures designed with the aim of managing and reducing occupational risk. U.S. mining companies are voluntarily adopting SHMSs to supplement regulatory safety compliance efforts. Like all complex sociotechnical systems there are a wide variety of approaches and adoption philosophies around a SHMS. Many SHMSs suffer from high levels of redundancies, which manifest themselves in diverse ways: duplicate processes, forms, permits, and tracking mechanisms. These redundancies have severe impacts on the overall system and absorb critical culture and leadership “energy”. A recent study initiated by the University of Utah and sponsored by the Alpha Foundation is investigating the effectiveness of SHMSs across 15 different U.S. mining sites. A large amount of data has been captured to help characterize elements of SHMSs and correlate their impacts on safety outcomes. Many variables were assessed in the study. Those of importance to this paper are the variables describing the robustness of the sociotechnical aspects of the SHMS. Two such variables are the overall integration of the SHMS within the whole system and how data is managed. Data management in this context deals with the approach to capturing, analyzing, and acting on data within the system. As well, systems are integrated around controlled, data-sharing mechanisms. It has been found that wide variety of approaches has been taken to integrate the SHMS within the governing management philosophy at a given site. A series of case studies is presented that develop some specific best practices for proper data management and integration of SHMSs.Introduction and Project Background Managing occupational health and safety can be done in many different ways, and with many different approaches, but is often based on the host country’s regulatory and industrial culture. However, many companies have extended their effort beyond the regulatory requirements of their host nation and have implemented Safety and Health Management Systems. The goal of these efforts is to reduce and eliminate occupational risk. Many health and safety practitioners have a hard time justifying various elements of health and safety management. There is a lack of institutional knowledge that measures and assesses the effectiveness of these Safety and Health Management System (SHMS) elements. To better understand the effectiveness of SHMSs the Alpha Foundation has funded a research project with the University of Utah. The title of the project is “Characterization and Effectiveness of Safety and Health Management Systems in the U.S. Mining Industry.” The research team will assess the SHMSs of 15 United States mine sites. The outcome of the project will be a method to characterize the strength of the SHMS elements, assess a culture and leadership index, and finally establish correlations between elemental risk and safety outcomes. The study is currently in progress. However, many important outcomes have already been observed, particularly on the data management practices within an SHMS."

Jan 1, 2017

By Laurel E. Blackman

The DFI Project Information Management Systems (PIMS) Committee cooperated with the US Army Corps of Engineers (USACE) to develop a specification for PIMS used on USACE projects. Once completed the Committee developed a more generic version of this specification for general use, which is contained in this document. This first edition has simply removed the USACE-related requirements from the document. The committee will continue to work on this specification to enhance the content and also incorporate recent technological development for these systems. It is the Committee's intention that a second edition will be issued in 2022.

Jan 1, 2021

By A D. T Goode

Data Metallogenica (DM) is unique as a global mineral sample collection and deposit database with over 70 000 rock samples and a wide range of descriptive, photographic and measurement data. Subscribers have access to approximately four gigabytes of on-line data with many examples included on the public pages. DM commenced its life on the Pacific rim in 1971 and much recent growth has focused on countries of the Pacific margin, particularly Australia, Fiji, Chile, Indonesia and Peru. Approximately 600 separate deposits and mineral occurrences in this region are represented in DM. The sets of rock samples have been photographed at high resolution and included on the web site, together with unpublished descriptions, deposit cross-sections, bibliographies, field and mine photographs, petrology and reflectance mineralogy. Figures and abstracts from PhD theses are a new addition and the donation of proprietary databases by companies and individuals is an encouraging trend. DM increasingly is being viewed as an acceptable and important repository for æfragileÆ and irreplaceable personal and company data, it is also being recognised in its true light as a high level information system, not solely a collection of rock samples. Data Metallogenica is a not-for-profit venture of AMIRA International that requires self-funding from December 2004. Growth and development must be funded entirely out of subscription and sponsorship revenues because no government subsidy is provided. The Pacific rim is in the mainstream of mining and exploration activities worldwide and those involved have much to gain from a dynamically sustaining DM. The home page at www.datametallogenica.com contains much example data and details of subscription and membership rates.

Jan 1, 2004

By Sylvain Létourneau

Help organizations maximize the benefits of the data that they collect ? perform scientific research in: machine learning, data mining, statistics, artificial intelligence, soft computing, information retrieval, information systems, and natural language processing ? preferred application areas: Equipment Health Management, Bioinformatics, Health, Web Mining ? today?s talk limited to Equipment Health Management While the aircraft is still in the air, IDS: ? clusters relevant WRN, FLR, and snag (pilot) messages ? identifies probable causes based on TSM and provide links to corresponding TSM pages ? assesses MEL conditions (GO, NOGO, GOIF, ?) ? displays relevant maintenance history, suggests fixes based on similar cases ? Commercialization with MxI Technologies Ltd and Air Canada in 2000

May 1, 2008

By E Ben-Dor, D Bakun-Mazor, Y Ben-Ari

Determining the mechanical properties of rocks is important for various civil engineering fields, including the mining of raw materials for aggregates used in the construction and paving industries. Traditionally, the mechanical properties of rocks are obtained through in situ and laboratory tests during geotechnical surveys. However, these time-consuming surveys involve many resources. In contrast, hyperspectral remote sensing methods make it possible to identify the mineralogical composition and crystallographic structure of the rock; properties that control the mechanical properties of the rocks. In this work, we characterise the mechanical properties of carbonate rocks by using a hyperspectral sensor in laboratory conditions. We collected about 150 cylindrical samples of carbonate rock, with a wide range of strength values from several rock outcrops in Israel. We used a point spectrometer in the range of 0.4–2.5 μm and a spectral image sensor in the range of 3.0–12.0 μm, scanning the samples to obtain the signature of their light reflections and spectral emissivity. We then measured the samples’ density, porosity, water absorption, and uniaxial compressive strength (UCS). We used sophisticated data mining to find statistical relationships between the hyperspectral signatures of the samples and their mechanical properties. We used this data to identify the most dominant wavelengths for predicting mechanical properties. We found that the density, porosity, and water absorption of carbonate rocks could be confidently predicted based on spectroscopy data, while the UCS of the rock could also be predicted, but less significantly. The results of the study pave the way for the development of measuring tools for the mechanical properties of rock, based on non-destructive tests of quarrying materials.

Nov 29, 2022

By S. Dessureault, M. Yildirim

More mining companies are now investing in IT, including updating one of the traditional IT process control tools used in the mining industry: real time in-pit truck allocation control and monitoring systems, or fleet management systems (FMS). These systems have been used to maximize the overall mine production by improving equipment utilization and reducing production costs. Most modern operations monitor the in-pit operations using GPS and wireless communications to produce data. Improvements in bandwidth, sensors, and computing power are causing modern systems to become more complex and generate more data. However, human analysis by simple regression or table generation in spreadsheets of the data is becoming impossible. Data mining solves this data management problem. Data mining finds patterns and relationships in very large data sets. This paper describes the application of modern data mining techniques on truck dispatching systems in a real open pit hard-rock copper mine. The analyses conducted included determining how the trucks performed under particular conditions and allocation systems. The long-term benefits of this work are to identify strengths and improvement opportunities in truck assignment systems and establish the skill sets and IT infrastructure needed in undertaking more complex datadriven technology such as a truck dispatcher trainer.

Jan 1, 2007

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

Jan 1, 1915

By A. N. Diehl

IT is the object of this paper (1) to deal with the elements in blast-furnace gas from the standpoint of their importance, and the part they are to play in future consumption, and (2) to give detailed information about the construction and operation of a plant designed among the first in this country for washing gas on a large scale.

Jan 5, 1914

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