Derivation of base-line geochemistry, petrography, and isotopic data for the host rocks to the Lucky Strike deposit and comparison with data from other alteration zones, Buchans Mining Camp, Newfoundland

The Buchans ore bodies of central Newfoundland represent some of the highest grade VMS deposits ever mined. These Kuroko-type deposits are also known for the well developed and preserved nature of the mechanically transported deposits. The deposits are hosted in Cambro-Ordovician, dominantly calc-alkaline, bimodal volcanic and epiclastic sequences of the Notre Dame Subzone, Newfoundland Appalachians. Stratigraphic relationships in this zone are complicated by extensively developed, brittledominated Silurian thrust faulting. Hydrothermal alteration of host rocks is a common feature of nearly all VMS deposits, and the recognition of these zones has been a key exploration tool. Alteration of host rocks has long been described to be spatially associated with the Buchans ore bodies, most notably with the larger in-situ deposits. This report represents a base-line study in which a complete documentation of the geochemical variance, in terms of both primary (igneous) and alteration effects, is presented from altered volcanic rocks in the vicinity of the Lucky Strike deposit (LSZ), the largest in-situ deposit in the Buchans camp. Packages of altered rocks also occur away from the immediate mining areas and constitute new targets for exploration. These zones, identified mostly by recent and previous drilling, represent untested targets and include the Powerhouse (PHZ), Woodmans Brook (WBZ) and Airport (APZ) alteration zones, as well as the Middle Branch alteration zone (MBZ), which represents a more distal alteration facies related to Buchans ore-formation. Data from each of these zones were compared to those from the LSZ in order to evaluate their relative propectivity. Derived litho geochemical data served two functions: (i) to define primary (igneous) trends and (ii) secondary alteration trends. Primary trends were established using immobile, or conservative, elements (i. e., HFSE, REE, Th, Ti0₂, Al₂0₃, P₂0₅). From these, altered volcanic rocks were interpreted in terms of composition (e.g., basalt - rhyodacite) and magmatic affinity (e.g., calc-alkaline vs. tholeiitic). The information suggests that bimodality is a common feature of all zones, with most rocks plotting as either basalt/andesite or dacite (or rhyodacite); andesitic senso stricto compositions are rare. Magmatic affinities are more varied and complex, but indicate that all units are arc volcanic sequences. Rocks from the LSZ/MBZ represent a transitional to calc-alkalic sequence, however, a slight shift in key geochemical discriminants occurs between the foot-wall to the hanging-wall. Specifically, mafic and felsic lavas of the foot-wall are of transitional (or mildly calc-alkaline) affinity whereas the hanging-wall rocks are relatively more strongly calc-alkaline as indicated by enriched LREE/HREE and higher ZrN, NbN and other ratios in the latter. The geochemical variations also serve as a means to separate the units (at least the felsic rocks) into hanging-wall and foot-wall sequences, therefore providing a valuable exploration tool. Volcanic rocks from the WBZ/PHZ (and probably the APZ) are more typical of tholeiitic to transitional suites, yielding flatter mantlenormalized REE patterns and lower ZrN ratios. Thus, the relationships between the immediate mining area (represented by LSZ/MBZ) and the Buchans East (PHZ/WBZ) and the APZ are uncertain. Host rocks for all zones consist of mafic to felsic volcanic rocks, though the proportion of pyroclastic and epiclastic rocks, is greatest at the LSZ. Phenocryst assemblages and textures are common in all zones, with minor exceptions, and are not useful for discrimination purposes. Felsic rocks from all zones are dominated by sericiteclay+/- silica alteration, whereas mafic rocks are dominated by chlorite- quartz- sericite alteration. Pyrite is ubiquitous in all moderately altered rocks and minor associated base metal sulphides occur locally. The exception is at Lucky Strike, where stockwork quartzveining contains abundant base-metal mineralization and barite. Rocks completely comprised of chlorite (chloritite) also occur in the LSZ foot-wall. In addition, K-feldspar alteration occurs in felsic volcanic rocks at the MBZ associated with Zn-Pb-Ba and, notably, without chlorite. This zone represents a peripheral, but proximal, zone of alteration induced by lower temperature hydrothermal fluids, presumably with little influence from seawater. Alteration geochemistry was interpreted from raw data as well as from mass balanced (recalculated) data derived from immobile element pairs. The data from the LSZ/MBZ indicate a range in the degree of alteration from only minor to severe modification of precursor compositions. Ba tends to show a strong positive correlation with K₂0, although most Ba occurs as barite. With respect to mass changes, Al₂0₃, Ti0₂ and P₂0₅ were shown to be immobile. Nearly all rocks display mass loss of Na₂O, CaO, and Sr reflecting feldspar destruction. These trends are usually mirrored by K₂0-Rb and MgO addition, indicating sericitic and chloritic alteration, respectively. More substantial gains ofK₂0 often occur in rocks with K-feldspar alteration, whereas a few samples also displayed excessive MgO enrichment and represent chloritites. Fe₂0₃ indicates both chlorite and sulphide formation. Si0₂ addition is almost always the case for the altered mafic rocks as silica often infills amygdules and replaces the finer tuffaceous material. The felsic rocks display more variability in Si0₂. Silicic, sericitic and chloritic alteration trends were observed from the other zones, but not K-feldspar, chloritite, or barite. Microprobe analysis of chlorites, sericites and carbonates indicate: (i) sericites from all zones are defined as muscovite and are not phengitic; (ii) at the LSZ, chlorites ranged from Fe-Mg chlorites (pycnochlorite) to Mg-rich chlorite (penninite), with the latter occurring in the stockwork zone and more proximal alteration facies; (iii) chlorites from the WBZ were typical of those from the more distal alteration facies of the LSZ, plotting as ripidolite to pycnochlorite; (iv) conversely, chlorite from the PHZ plot with Mg-Al-rich compositions (chlinochlore to penninite); and (v) carbonate species from each zone are also varied, with calcite occurring in each zone, in addition to dolomite and ankerite in the PHZ and WBZ, respectively. Lead isotope ratios for galena separates from the different various zones, when combined with data from older studies, tend to cluster into four distinctive fields. Overall, the data plot on a broad mixing line and indicate evolution in a relatively low-μ environment. Data from sulphide stringers in altered MBZ rocks, as well as from clastic sulphides (Sandfill prospect), plot in the Buchans ore field, as do the data for galena from altered rocks in the APZ. Samples from the Buchans East area are even more primitive than the Buchans ores, with lead from the PHZ plotting with the Connel Option prospect and data from the WBZ matching that of the Skidder prospect. A sample from a newly discovered debris flow-type sulphide occurrence (Middle Branch East) yields lead isotope ratios that are slightly more radiogenic than Buchans and plot with the Mary March alteration zone. Data within each cluster are interpreted to represent derivation from individual hydrothermal systems in which metals were derived from a common source.

The quality of financial information in the extractive industries: a value relevance analysis

The extractive industry is characterized by high levels of risk and uncertainty. These attributes create challenges when applying traditional accounting concepts (such as the revenue recognition and matching concepts) to the preparation of financial statements in the industry. The International Accounting Standards Board (2010) states that the objective of general purpose financial statements is to provide useful financial information to assist the capital allocation decisions of existing and potential providers of capital. The usefulness of information is defined as being relevant and faithfully represented so as to best aid in the investment decisions of capital providers. Value relevance research utilizes adaptations of the Ohlson (1995) to assess the attribute of value relevance which is one part of the attributes resulting in useful information. This study firstly examines the value relevance of the financial information disclosed in the financial reports of extractive firms. The findings reveal that the value relevance of information disclosed in the financial reports depends on the circumstances of the firm including sector, size and profitability. Traditional accounting concepts such as the matching concept can be ineffective when applied to small firms who are primarily engaged in nonproduction activities that involve significant levels of uncertainty such as exploration activities or the development of sites. Standard setting bodies such as the International Accounting Standards Board and the Financial Accounting Standards Board have addressed the financial reporting challenges in the extractive industry by allowing a significant amount of accounting flexibility in industryspecific accounting standards, particularly in relation to the accounting treatment of exploration and evaluation expenditure. Therefore, secondly this study examines whether the choice of exploration accounting policy has an effect on the value relevance of information disclosed in the financial reports. The findings show that, in general, the Successful Efforts method produces value relevant information in the financial reports of profitable extractive firms. However, specifically in the oil & gas sector, the Full Cost method produces value relevant asset disclosures if the firm is lossmaking. This indicates that investors in production and non-production orientated firms have different information needs and these needs cannot be simultaneously fulfilled by a single accounting policy. In the mining sector, a preference by large profitable mining companies towards a more conservative policy than either the Full Cost or Successful Efforts methods does not result in more value relevant information being disclosed in the financial reports. This finding supports the fact that the qualitative characteristic of prudence is a form of bias which has a downward effect on asset values. The third aspect of this study is an examination of the effect of corporate governance on the value relevance of disclosures made in the financial reports of extractive firms. The findings show that the key factor influencing the value relevance of financial information is the ability of the directors to select accounting policies which reflect the economic substance of the particular circumstances facing the firms in an effective way. Corporate governance is found to have an effect on value relevance, particularly in the oil & gas sector. However, there is no significant difference between the exploration accounting policy choices made by directors of firms with good systems of corporate governance and those with weak systems of corporate governance.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

The Economic Case for Electric Mining Equipment and Technical Considerations Relating to their Implementation

Underground hardrock mining can be very energy intensive and in large part this can be attributed to the power consumption of underground ventilation systems. In general, the power consumed by a mine’s ventilation system and its overall scale are closely related to the amount of diesel power in operation. This is because diesel exhaust is a major source of underground air pollution, including diesel particulate matter (DPM), NO2 and heat, and because regulations tie air volumes to diesel engines. Furthermore, assuming the size of airways remains constant, the power consumption of the main system increases exponentially with the volume of air supplied to the mine. Therefore large diesel fleets lead to increased energy consumption and can also necessitate large capital expenditures on ventilation infrastructure in order to manage power requirements. Meeting ventilation requirements for equipment in a heading can result in a similar scenario with the biggest pieces leading to higher energy consumption and potentially necessitating larger ventilation tubing and taller drifts. Depending on the climate where the mine is located, large volumes of air can have a third impact on ventilation costs if heating or cooling the air is necessary. Annual heating and cooling costs, as well as the cost of the associated infrastructure, are directly related to the volume of air sent underground. This thesis considers electric mining equipment as a means for reducing the intensity and cost of energy consumption at underground, hardrock mines. Potentially, electric equipment could greatly reduce the volume of air needed to ventilate an entire mine as well as individual headings because they do not emit many of the contaminants found in diesel exhaust and because regulations do not connect air volumes to electric motors. Because of the exponential relationship between power consumption and air volumes, this could greatly reduce the amount of power required for mine ventilation as well as the capital cost of ventilation infrastructure. As heating and cooling costs are also directly linked to air volumes, the cost and energy intensity of heating and cooling the air would also be significantly reduced. A further incentive is that powering equipment from the grid is substantially cheaper than fuelling them with diesel and can also produce far fewer GHGs. Therefore, by eliminating diesel from the underground workers will enjoy safer working conditions and operators and society at large will gain from a smaller impact on the environment. Despite their significant potential, in order to produce a credible economic assessment of electric mining equipment their impact on underground systems must be understood and considered in their evaluation. Accordingly, a good deal of this thesis reviews technical considerations related to the use of electric mining equipment, especially ones that impact the economics of their implementation. The goal of this thesis will then be to present the economic potential of implementing the equipment, as well as to outline the key inputs which are necessary to support an evaluation and to provide a model and an approach which can be used by others if the relevant information is available and acceptable assumptions can be made.

UTILIZATION OF GIS AND SPATIAL ANALYSIS TECHNIQUES IN ARTISANAL AND SMALL SCALE MINING TO LOCATE A CENTRALIZED PROCESSING CENTRE

One of the global phenomena with threats to environmental health and safety is artisanal mining. There are ambiguities in the manner in which an ore-processing facility operates which hinders the mining capacity of these miners in Ghana. These problems are reviewed on the basis of current socio-economic, health and safety, environmental, and use of rudimentary technologies which limits fair-trade deals to miners. This research sought to use an established data-driven, geographic information (GIS)-based system employing the spatial analysis approach for locating a centralized processing facility within the Wassa Amenfi-Prestea Mining Area (WAPMA) in the Western region of Ghana. A spatial analysis technique that utilizes ModelBuilder within the ArcGIS geoprocessing environment through suitability modeling will systematically and simultaneously analyze a geographical dataset of selected criteria. The spatial overlay analysis methodology and the multi-criteria decision analysis approach were selected to identify the most preferred locations to site a processing facility. For an optimal site selection, seven major criteria including proximity to settlements, water resources, artisanal mining sites, roads, railways, tectonic zones, and slopes were considered to establish a suitable location for a processing facility. Site characterizations and environmental considerations, incorporating identified constraints such as proximity to large scale mines, forest reserves and state lands to site an appropriate position were selected. The analysis was limited to criteria that were selected and relevant to the area under investigation. Saaty’s analytical hierarchy process was utilized to derive relative importance weights of the criteria and then a weighted linear combination technique was applied to combine the factors for determination of the degree of potential site suitability. The final map output indicates estimated potential sites identified for the establishment of a facility centre. The results obtained provide intuitive areas suitable for consideration

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.

Semantic Mining based on graph theory and ontologies. Case Study: Cell Signaling Pathways

In this paper we use concepts from graph theory and cellular biology represented as ontologies, to carry out semantic mining tasks on signaling pathway networks. Specifically, the paper describes the semantic enrichment of signaling pathway networks. A cell signaling network describes the basic cellular activities and their interactions. The main contribution of this paper is in the signaling pathway research area, it proposes a new technique to analyze and understand how changes in these networks may affect the transmission and flow of information, which produce diseases such as cancer and diabetes. Our approach is based on three concepts from graph theory (modularity, clustering and centrality) frequently used on social networks analysis. Our approach consists into two phases: the first uses the graph theory concepts to determine the cellular groups in the network, which we will call them communities; the second uses ontologies for the semantic enrichment of the cellular communities. The measures used from the graph theory allow us to determine the set of cells that are close (for example, in a disease), and the main cells in each community. We analyze our approach in two cases: TGF-β and the Alzheimer Disease.

Data Mining in Promoting Flight Safety

The incredible rapid development to huge volumes of air travel, mainly because of jet airliners that appeared to the sky in the 1950s, created the need for systematic research for aviation safety and collecting data about air traffic. The structured data can be analysed easily using queries from databases and running theseresults through graphic tools. However, in analysing narratives that often give more accurate information about the case, mining tools are needed. The analysis of textual data with computers has not been possible until data mining tools have been developed. Their use, at least among aviation, is still at a moderate level. The research aims at discovering lethal trends in the flight safety reports. The narratives of 1,200 flight safety reports from years 1994 – 1996 in Finnish were processed with three text mining tools. One of them was totally language independent, the other had a specific configuration for Finnish and the third originally created for English, but encouraging results had been achieved with Spanish and that is why a Finnish test was undertaken, too. The global rate of accidents is stabilising and the situation can now be regarded as satisfactory, but because of the growth in air traffic, the absolute number of fatal accidents per year might increase, if the flight safety will not be improved. The collection of data and reporting systems have reached their top level. The focal point in increasing the flight safety is analysis. The air traffic has generally been forecasted to grow 5 – 6 per cent annually over the next two decades. During this period, the global air travel will probably double also with relatively conservative expectations of economic growth. This development makes the airline management confront growing pressure due to increasing competition, signify cant rise in fuel prices and the need to reduce the incident rate due to expected growth in air traffic volumes. All this emphasises the urgent need for new tools and methods. All systems provided encouraging results, as well as proved challenges still to be won. Flight safety can be improved through the development and utilisation of sophisticated analysis tools and methods, like data mining, using its results supporting the decision process of the executives.

Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.

Building cloud-based healthcare data mining services

The linkage between healthcare service and cloud computing techniques has drawn much attention lately. Up to the present, most works focus on IT system migration and the management of distributed healthcare data rather than taking advantage of information hidden in the data. In this paper, we propose to explore healthcare data via cloud-based healthcare data mining services. Specifically, we propose a cloud-based healthcare data mining framework for healthcare data mining service development. Under such framework, we further develop a cloud-based healthcare data mining service to predict patients future length of stay in hospital.

More favorable combination of circumstances could hardly have been desired : a bottom to top examination of the Pittsburgh and Boston Mining Company's Cliff Mine

The Cliff Mine, an archaeological site situated on the Keweenaw Peninsula of Michigan, is the location of the first successful attempt to mine native copper in North America. Under the management of the Pittsburgh & Boston Mining Company from 1845-1879, two-third of the Cliff’s mineral output was in the form of mass copper, some pieces of which weighed over 5 tons when removed from the ground. The unique nature of mass copper and the Cliff Mine’s handling of it make it one of the best examples of early mining processes in the Keweenaw District. Mass copper only constituted 2% of the entire product of the Lake Superior copper districts, and the story of early mining on the Peninsula is generally overshadowed by later, longer running mines such as the Calumet & Helca and Quincy Mining Companies. Operating into the mid-twentieth century, the size and duration of these later mines would come to define the region, though they would not have been possible without the Cliff’s early success. Research on the Cliff Mine has previously focused on social and popular history, neglecting the structural remains. However, these remains are physical clues to the technical processes that defined early mining on the Keweenaw. Through archaeological investigations, these processes and their associated networks were documented as part of the 2010 Michigan Technological Archaeology Field School’s curriculum. The project will create a visual representation of these processes utilizing Geographic Information Systems software. This map will be a useful aid in future research, community engagement and possible future interpretive planning.