1. HOW DO WE MANAGE DATA? Virginia T. McLemore New Mexico Bureau of Geology and Mineral Resources, New Mexico Tech, Socorro, NM

2. PREVIEW n Purpose n Develop an exploration plan n Available data n Sample theory n Show example of databases for NM n Long-term database goals n Summary n Unresolved issues

3. WHAT IS THE PURPOSE?

4. Purpose—continued n to make informed decisions about –exploration –resource development and management –water supplies –land use –environmental impacts –natural hazard assessment –waste disposal

5. EXPLORATION PLAN

6.  What is the problem?  What are the background conditions?  What is the source of the mineralization?  What are the pathways affected?  What are the desired final results?  Is the site in compliance with environmental laws?

7. COMPONENTS OF A SAMPLING PLAN Define questions and objectivesDefine questions and objectives Develop site conceptual modelsDevelop site conceptual models Collect available pre-existing data Collect available pre-existing data Costs and potential consequences of not samplingCosts and potential consequences of not sampling Identify types of data and information neededIdentify types of data and information needed Define confidence level and quantity of data required to answer questionsDefine confidence level and quantity of data required to answer questions Design the sampling planDesign the sampling plan

8. COMPONENTS—continued n Develop protocols n Conduct an orientation or pilot study before implementation n Conduct sampling plan n Analyze and manage data (interpretation) n Make decisions (risk management) n Educate and inform the parties involved

9. 1. DEFINE QUESTIONS AND OBJECTIVES n Identify sources, transport, and effects of mineralization. n Validate predicative models. n Validate exploration/mitigation/remediation/reclamation efforts. n Establish background or existing conditions. n Identify impacted areas vs. pristine areas. n Potential use of water in operations

10. 2. DEVELOP EXPLORATION CONCEPTUAL MODELS Review existing data n Climatic data n Physical data n Geology (mineralogy) n Hydrogeology (Surface-ground water interaction) n Mining history and impacts of mine workings n Biology n Other data available We suggest that a watershed or district approach be taken.

11. 3. COSTS AND POTENTIAL CONSEQUENCES OF NOT SAMPLING n Avoid being data rich but information poor. n Public perceptions of risk. n Perceptions of chemicals associated with the mining industry, such as cyanide. n Some long-term and widespread environmental problems should be considered relatively high-risk even if the data on which the risk assessment is based are somewhat incomplete and uncertain.

12. 4. IDENTIFY TYPES OF DATA AND INFORMATION NEEDED n What sampling media (solid, liquid, biological/wetlands, air)? n What are sources, transport mechanisms, and receptors? n What type of sample is to be collected and is it representative? n What field measurements are required? n What is the feasibility of sampling?

13. 5. DEFINE CONFIDENCE LEVEL AND QUANTITY OF DATA REQUIRED TO ANSWER QUESTIONS n What is the confidence level needed? n How much data are required?

14. 6. DESIGN THE SAMPLING PLAN n QA/QC n Data format n Safety issues (OSHA vs. MSHA vs. local, state vs. good neighbor/employer) n Sample location, number of samples, and frequency of sampling, proper labeling of samples (site specific) n What constituents or parameters are required for each media

15. 7. DEVELOP PROTOCOLS n Collection techniques n Sample collection n Observational field data n Modify sampling plan and deviations n Opportunistic sampling n Contamination n Handling/transport n Preservation and storage (from field to laboratory)

16. 7. DEVELOP PROTOCOLS—continued n Sample pre-treatment in the laboratory n Filtration n Sample preparation n Sample separation n Archival/storage n Analytical procedures and techniques

17. 8. ORIENTATION OR PILOT STUDY n Clear understanding of target type n Understanding of surficial environments n Nature of dispersion from mineralized areas n Sample types available n Sample collection procedures n Sample size requirements

18. 8. ORIENTATION OR PILOT STUDY- continued n Sample interval, depth, orientation, and density n Field observations required n Sample preparation procedures n Sample fraction for analyses n Geochemical suite for analyses n Data format for interpretation

19. 9. CONDUCT SAMPLING PLAN (PROGRAM IMPLEMENTATION)

20. 10. ANALYZE AND MANAGE DATA n Reporting data n Presentation of data n Interpretation n Data interpretation approaches –Statistical –Spatial –Geochemical –Geological

21. 10. ANALYZE AND MANAGE DATA— continued n Reporting and dissemination n What becomes of data (storage) n Common data formats n Use the data n Reliability and limitations of findings n Evaluate the data (statistics)

22. 11. MAKE DECISIONS (RISK MANAGEMENT)

23. 12. Educate and inform the parties involved

24. SAMPLING MEDIA A variety of sampling media can be tested –solid –liquid –air –biological –other media

25. AVAILABLE DATA

26. n Location (= GIS, point and polygon data) n Production, reserves, resource potential n Geologic n Geochemical (rock, water, ect.) n Well data n Historical and recent photographs n Mining methods, maps n Ownership n Other data

27. OTHER DATA n Igneous rocks database n Core and cuttings archive n Geochronology database n Mine maps n GIS-type data –geology –geophysics –topography –remote sensing –well locations (cuttings, core, logs)

28. ENVIRONMENTAL DATA n Commodities produced and present n Potential hazardous materials n Evidence of potential acid drainage n Hydrology n Receiving stream n Reclamation n Mitigation status n Sensitive environments n Chemical data (both solids and water)

29. Relational database in ACCESS that will ultimately be put on line with GIS capabilities n ACCESS is commercial software and this design can be used by others n metadata (supporting definitions of specific fields) can be inserted into the database n ACCESS is flexible and data can be easily added to the design

30. GIS n Geologic Information System –Arc Map –Arc Catalog

31. SAMPLE THEORY

32. What is a sample?

33. n Portion of a whole n Portion of a population

34. Sample Collection n Completeness – the comparison between the amount of valid, or usable, data you originally planned to collect, versus how much you collected. n Comparability – the extent to which data can be compared between sample locations or periods of time within a project, or between projects. n Representativeness – the extent to which samples actually depict the true condition or population that you are evaluating

35. “All analytical measurements are wrong: it’s just a question of how large the errors are, and whether they are acceptable” (Thompson, 1989).

36. DEFINTIONS n Precision – the degree of agreement among repeated measurements of the same characteristic. Precision is monitored by multiple analyses of many sample duplicates and internal standards. n Accuracy – measures how close your results are to a true or expected value and can be determined by comparing your analysis of a standard or reference sample to its actual value. Analyzing certified standards as unknown samples and comparing with known certified values monitors accuracy.

37. The difference between precision and accuracy

39. QUALITY CONTROL/QUALITY ASSURRANCE n QC is referred to a program designed to detect and measure the error associated with a measurement process. QC is the program that ensures that the data are acceptable. n QA is the program designed to verify the acceptability of the data using the data obtained from the QC program. QA provides the assurance that the data meets certain quality requirements with a specified level of confidence.

40. QUALITY CONTROL/QUALITY ASSURRANCE n What is the purpose of your project? n What do you need the analyses for and how accurate should they be? n Where are the results going to be released or published? n What is the mineralogy? n What are appropriate certified standards (may need to develop lab standards)? n What are the detection limits (both upper and lower)? –Analytical errors vary from element to element, for different ranges of concentration, and different methods n Duplicate or more analyses of standards and unknowns verses duplicate runs of same sample

41. QUALITY CONTROL/QUALITY ASSURRANCE n Analyze a separate set of standards rather than standards used for calibration n Send samples and standards to other laboratories n Establish written lab procedures n Are blanks and field blanks used and analyzed? n What are the custody procedures (collection date, preservation method, matrix, analytical procedures)? n Does the chemical analyses make geological sense? Is it consistent with the mineralogy and type of mineral deposit? n Sometimes there is more paper work than making sure the data is accurate n What do you do if there are problems with QA/QC?

42. TYPES OF ERRORS n Systematic verses bias (constant, unintentional) n Random errors (unpredicted but nonsystematic errors, imprecise practices) n Gross or illegitimate errors (procedural mistakes) n Deliberate errors

43. MEASUREMENT ERRORS n Wrong sample n Wrong reading n Transposition or transcription errors n Wrong calibration n Peak overlap n Wrong method n Contamination n Losses n Inattention to details n Sampling problems n Instrument instability n Reagent control n Variability of blank n Operator skill n Sample variability

44. Why do we need full chemical analyses on some solid samples? n Identification of lithology n Identification and abundance of mineral species n Identification, rank, and intensity of alteration n Prediction of composition of waters within rock piles n Chemical and mineralogical zonation of rock piles n Be able to compare, contrast, and coordinate all phases of the project with each other and with existing work (common thread)

45. Standard Operating Procedures n Develop SOPs prior to initiation of project n SOPS should be written and changed to reflect changing procedures—only if procedures can be changed n SOPs are a written record of procedures in use n Everyone follows SOPs

46. Exploration n Generally looking for anomalies n Some value above background n Looking for anomalies in pathfinder elements n Looking for alteration halos WHAT IS A PATHFINDER ELEMENT?

47. How do you determine an anomaly?

48. n Knowledge of background –Regional survey –Published background values for various terrains or lithologies n Histograms or cumulative frequency plots of data n Pre-determined thresh hold –Mined grades

49. EXAMPLE McGREGOR RANGE, FORT BLISS, NEW MEXICO

51. Stream Sediments McGregor Range

56. EXAMPLE Luna County, New Mexico

57. Location

58. DATABASES FOR LUNA COUNTY n Districts n Mines (and mills) n Geochemistry n Photographs

59. The term mine is defined here as any mine, prospect, mineralized outcrop, altered area, mill, smelter, or other mining-related facility, including geothermal wells, other mineral wells, excluding petroleum wells.

60. Mine_id in some cases refers to one mine feature (adit, pit, shaft, etc.) and in other cases to several mine features. If a mine occurs in 2 quadrangles or 2 counties, then it receives 2 separate Mine_id numbers. Large mines receive one Mine_id and as many mine_feature id numbers as needed.

61. Mining districts

62. District (miningdist.xls)  District_id  District_or_coal_field  *Aliases  *County  *Type_of_deposit  Year_of_discovery  *Years_of_production  *Commodities_produced  *Commodities_present  *Estimated_cumulative_ production_in original_dollars  *Type_of_deposit  *USGS_classification  *References  *Comments Mines in district (dist_mine.xls)  District_id  District_or_coal_field  Mine_id  Mine_name Annual district production (dist_ann_prod.xls)  District_id  District  County  Year  Commodity  Quantity  Units Estimated production  District_id  District  County  Period of production  Commodity  Quantity  Units  References  Comments Actual production  District_id  District  County  Period of production  Commodity  Quantity  Units  References  Comments Sample table  District_id  Sample_id Photograph table  District_id  Photograph_id Bibliography  District_id  Reference_id  Reference DISTRICTS

63. Mines (lunamines.mdb)  Mine_id  County  District_id  District  Mine_name  *Aliases  *Location  *Township  *Range  *Section  *Subsection  Latitude  Longitude  Utm_easting  Utm_northing  Utm_zone  Location_assurance  *Commodities_produced  *Commodities_present_ not_produced  *Years_of_production  *Development  Operating status  *Production  *Mining_methods  *Ownership Samples table  Mine_id  Sample_id Photographs table  Mine_id  Photograph_id Patented mines  Mine_id  Mineral_survey _number  Patent_number  Year_patented Production  Mine_id  Start  Stop  Year  Commodity  Quantity  Units  Reference Bibliography  Mine_id  Reference_id  Reference Mine site specific data (ponds, mills, ect.)  Mine_id  Feature_id  Type_of_feature  Sample_number  Description  Reference  Comments  Mineral_survey_number.  Patent_number  Year_patented  Mining_history  *Age_host_rock  *Host_formation  *Rock_type  *Structure  *Mineralogy  *Size  *Alteration  *Type_of_deposit  *USGS_classification  *USGS_quadrangle  *Elevation  *Sample_number  *MRDS_number  *Chemical_analyses  *Photograph_number  *Comments  Recommendations  *References  *Inspected_by  *Date_inspectedMINES

65. Sample table  Sample_id  Mine_id  District_id  County  Type of sample  Sample description  Latitude  Longitude  Location description  Depth  Date collected  Collected by  Reference Analyses table  Sample_id  Laboratory  Data Bibliography  Sample_id  Reference_id  Reference GEOCHEMISTRY

67. PHOTOGRAPHS Photogaphs  ID  Mine_id  District_id  PrintNo  ColorOfPrint  NegativeNo  SlideNo  ColorOfSlide  Slides  Image  Division  Date  Photographer  County  Location  Keywords  Caption  ExtendedCaption  CourtesyOf  Collection  Copyright  CopyrightCodeNo  Credit  Comments  ScanImage Actual photographs  Jpegs Bibliography  Photo_ID  Reference_id  Reference

71. Import data into GIS and produce appropriate maps

74. SUMMARY n Team effort –database information –database design and linkages n Steps –Design the database format ASAP –Data input –Use subset of data to test the project –Develop the final product –Use it

75. OTHER ISSUES n How to maintain links n How to update and maintain the databases n How to maintain quality control of the data