Skip to main content
SpringerLink
Log in

Statistical analysis of metallic anomaly patterns at former air force bombing ranges

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

This paper summarizes the findings of a statistical analysis of the locations of metallic anomalies detected at the Pueblo Precision Bombing Range Number 2 in Otero County, Colorado, and at the Victorville Precision Bombing Range in San Bernardino County, California. The purpose of the study is to explore whether statistical properties of the pattern of anomaly locations can be used to discriminate areas likely to contain unexploded ordnance (UXO) left over from previous bombing practice from those unlikely to contain UXO. Techniques for discriminating areas with and without UXO are needed because historic records have left an incomplete account of previous military training activities, so that locations historically used for target practice are often unknown. This study differs from previous research on metallic anomaly data at former military training ranges in that it analyzes the spatial pattern of the discrete locations of the anomalies, rather than the average number of anomalies per unit area. The results indicate that differences in spatial pattern may be a distinguishing feature between areas that were used for target practice and those that are unlikely to contain UXO, even when a large number of ferrous rocks and other inert metallic anomalies are present. We found that at both of the former bombing ranges, the anomaly patterns in sample areas that are distant from all known bombing targets are consistent with a complete spatial randomness pattern, while those near the target areas fit a radially symmetric, bivariate Gaussian pattern. Furthermore, anomaly location patterns generated by surveys with airborne metal detectors have the same statistical properties as the patterns generated by surveys with on-ground detectors, even though the airborne systems detect only a subset of the anomalies found by the ground-based detectors. Thus, pattern information revealed by airborne surveys with metal detectors may be useful in identifying areas where careful searches for UXO are needed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. All data analyzed in this paper were provided in spreadsheet form by Dr. Herb Nelson, Naval Research Laboratory, on behalf of the DOD ESTCP.

  2. The sample area distant from known targets has boundaries 616,100 m < X < 617,100 m and 4,172,500 m < Y  < 4,173,500 m (in Universal Transverse Mercator system coordinates); the helicopter-mounted detectors located 241 anomalies in this area. The sample area around the northern target (Target 3) has boundaries 616,666 m < X < 617,666 m and 4,177,077 m < Y < 4,178,077 m; the airborne detectors found 3,227 anomalies in this area.

  3. The location of the target center was provided by Dr. Herb Nelson of the Naval Research Laboratory and DOD ESTCP. The 250-m standard deviation was estimated from the mean distances between the target center and the anomalies surrounding it.

  4. Only part of the Pueblo site was surveyed with both airborne and on-ground metal detectors. The study areas shown in Fig. 1 were not fully surveyed with ground-based sensors, so the data analyzed in this section represent different areas than those in the previous section of this paper.

  5. The airborne system can locate items with precisely known coordinates to within 2 cm in the horizontal (x) direction and 4 cm in the vertical (y) direction (Sky Research 2006). However, no magnetometer, whether airborne or ground-based, can perfectly locate the center of a buried ferrous object. Therefore, one must set a detection radius in order to define a probability of detection.

References

  • Cressie NAC (1993) Statistics for spatial data. Wiley, New York

    Google Scholar 

  • Defense Science Board (2003) Report of the Defense Science Board Task Force on Unexploded Ordnance. DOD, Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics, Washington

    Google Scholar 

  • Diggle PJ (2003) Statistical analysis of spatial point patterns. Oxford University Press, New York

    Google Scholar 

  • Diggle PJ, Besag J, Gleaves JT (1976) Statistical analysis of spatial point patterns by means of distance methods. Biometrics 32:659–667

    Article  Google Scholar 

  • Engelhardt M, Singh A, Singh AK (2000) Evaluation of US army corps of engineers statistical UXO sampling and characterization methodologies. Contract no. 68-C5-0091l. N.C.: EPA National Exposure Research Laboratory, Research Triangle Park

    Google Scholar 

  • Fanning A (1999) UXO calculator: a new approach to determining unexploded ordnance (UXO) Density at ordnance sites. US Army Engineering and Support Center, Huntsville, Ala. http://www.juxoco.army.mil/Technicalreps/Forum99/A_Fanning.pdf

  • Fields T (1999) April 22 letter to Ms. Sherri W. Goodman, Deputy Under Secretary of Defense (Environmental Security), Department of Defense. EPA, Office of Solid Waste and Emergency Response, Washington

    Google Scholar 

  • Hathaway J, Roberts B, McKenna S, Pulsipher B (2007) Application of statistically based site characterization tools to the victorville precision bombing range Y and 15 for the ESTCP wide area assessment demonstration. ESTCP Project H200325. Final Report. Department of Defense, ESTCP, Washington

    Google Scholar 

  • Kaluzny SP, Vego SC, Cardoso TP, Shelly AA (1998) S+ spatial stats: user’s manual for Windows and UNIX. Insightful Corp, Seattle

    Google Scholar 

  • Lawson AB, Denison DGT (eds) (2002) Spatial cluster modeling. Chapman & Hall/CRC Press, Boca Raton

    Google Scholar 

  • MacDonald JA, Knopman DK, Lockwood JR, Cecchine G, Willis H (2004) Unexploded ordnance: a critical review of risk assessment methods. MR-1674-A. RAND, Santa Monica

    Google Scholar 

  • MacDonald JA, Small MJ (2006) Assessing sites contaminated with unexploded ordnance: statistical modeling of ordnance spatial distribution. Environ Sci Technol 40(3):931–938

    Article  CAS  Google Scholar 

  • McKenna SA (2001) Application of a doubly stochastic Poisson model to the spatial prediction of unexploded ordnance. In: Presented at International Association of Mathematical Geology Annual Meeting, Cancun, 6–12 September 2001

  • McKenna SA, Saito H (2003) FY ‘03 progress report, SERDP project UX-1200: Bayesian approach to UXO site characterization with incorporation of geophysical information. Sandia National Laboratory, Albuquerque

    Google Scholar 

  • McKenna SA, Saito H, Goovaerts P (2002) Estimating the spatial distribution of UXO from limited data using geostatistics. In: Presented at the UXO countermine forum, Orlando, pp 3–6

  • Nelson H (2006) The role of validation in wide-area assessment. Presentation at the SERDP/ESTCP Symposium. Marriott Wardman Park, Washington, DC

    Google Scholar 

  • Ostrouchov G, Doll WE, Wolf DA, Beard LP, Morr MD, Butler DK (2003) Final report: spatial statistical models and optimal survey design for rapid geophysical characterization of UXO sites. SERDP Project CU-1201. Oak Ridge National Laboratory, Oak Ridge

    Google Scholar 

  • QuantiTech Inc. (1995) Grid statistical sampling based methodology (GridStats) version 1.23: user’s manual. QuantiTech, Huntsville

    Google Scholar 

  • Saito H, McKenna SA, Goovaerts P (2005a) Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites. Environ Ecol Stat 12:7–25

    Article  Google Scholar 

  • Saito H, McKenna SA, Zimmerman DA, Coburn TC (2005b) Geostatistical interpolation of object counts collected from multiple strip transects: ordinary kriging versus finite domain kriging. Stochast Environ Res Risk Assess 19:71–85

    Article  Google Scholar 

  • Saito H, McKenna SA (2007) Delineating high density areas in spatial Poisson fields from strip transect sampling using indicator geostatistics. J Environ Manage 84(1):71–82

    Article  Google Scholar 

  • Sky Research Inc. (2006) Wide-area assessment interim report for phases I and II LiDAR, orthophotography, and helicopter MTADS at pueblo precision bombing range #2. DOD, Environmental Security Technology Certification Program, Washington

  • Tuley M, Dieguez E (2005) Analysis of airborne magnetometer data from tests at Isleta Pueblo, New Mexico, February 2003. Institute for Defense Analyses, Alexandria

    Google Scholar 

  • US Army Corps of Engineers (1995) Archives search report findings Pueblo PBR #2, Otero County, Colorado. US Army Corps of Engineers, Huntsville

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina, Chapel Hill, NC, USA

    Jacqueline A. MacDonald

  2. Department of Engineering and Public Policy, Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Mitchell J. Small

Authors
  1. Jacqueline A. MacDonald
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mitchell J. Small
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacqueline A. MacDonald.

Rights and permissions

Reprints and permissions

About this article

Cite this article

MacDonald, J.A., Small, M.J. Statistical analysis of metallic anomaly patterns at former air force bombing ranges. Stoch Environ Res Risk Assess 23, 203–214 (2009). https://doi.org/10.1007/s00477-007-0206-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-007-0206-2

Keywords

Search

Navigation