Ricardo A. Olea, Ph.D.
Ricardo Olea is a Research Mathematical Statistician with the USGS Geology, Energy & Minerals (GEM) Science Center in Reston, VA.
Ricardo has extensive experience in quantitative modeling in the earth sciences and public health, primarily in the areas of petroleum geology and engineering, coal resource assessment, geostatistics, classical statistics, compositional data modeling, economic evaluation, well log analysis, marine geology, medical geography, geophysics, and geohydrology.
Professional Experience
2006 to Present: U.S. Geological Survey. Statistical Support: Coal Resource Assessment Methodology Implementation; Mathematical Model Enhancement; Exploitation of subsurface geologic resources in the land loss in coastal Louisiana; Advance the application of statistics to the earth sciences world-wide; and Reducing the risk of explosions at underground coal mines
Department of Petroleum Engineering, Stanford University
Marine Geology Section, Baltic Research Institute, University of Rostock, Warnemünde, Germany
Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina Chapel Hill
Research Scientist, Kansas Geological Survey, Lawrence, Kansas
Exploration Seismologist, Geostatistician, Log Analyst, Economic Analyst, and Reservoir Engineer, National Oil Company of Chile (ENAP)
Education and Certifications
Ph.D. Engineering, Chemical and Petroleum Engineering, University of Kansas
Mining Engineer Degree, University of Chile
Affiliations and Memberships*
Compositional Data Association, Member
Society of Petroleum Engineers, Member
American Association of Petroleum Geologists, Member
International Association for Mathematical Geosciences, Member
Sigma Xi and serves as an Associate Editor of the Springer journal Stochastic Environmental Research and Risk Assessment
International Association for Mathematical Geology (IAMG), Secretary General (1992–96) and President (1996–2000)
Honors and Awards
IAMG Krumbein Medal - Science and Professional Contributions, 2004
Science and Products
Inference of distributional parameters from compositional samples containing nondetects
Experimental geostatistical model of a continuous gas accumulation, Rocky Mountains, Utah
Kolmogorov-Smirnov test for spatially correlated data
Measuring CO2 emissions from coal fires in the U.S.
Basic Statistical Concepts and Methods for Earth Scientists
Recent results on the spatiotemporal modelling and comparative analysis of Black Death and bubonic plague epidemics
Declustering of clustered preferential sampling for histogram and semivariogram inference
CORRELATOR 5.2 - A program for interactive lithostratigraphic correlation of wireline logs
Subdivision of Holocene Baltic sea sediments by their physical properties [Gliederung holozaner ostseesedimente nach physikalischen Eigenschaften]
Singularity and Nonnormality in the Classification of Compositional Data
Kriging: Understanding allays intimidation
Compensating for estimation smoothing in kriging
Non-USGS Publications**
Martín-Fernández, J. A., Palarea-Alabaladejo, J., and Olea, R.A., 2011, Dealing with zeros. In V. Pawlowsky-Glahn and A. Buccianti (eds.), Compositional Data Analysis—Theory and Applications: Wiley & Sons, Ltd, Chichester, UK, p. 43–58.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
- Data
- Publications
Filter Total Items: 83
Inference of distributional parameters from compositional samples containing nondetects
Low concentrations of elements in geochemical analyses have the peculiarity of being compositional data and, for a given level of significance, are likely to be beyond the capabilities of laboratories to distinguish between minute concentrations and complete absence, thus preventing laboratories from reporting extremely low concentrations of the analyte. Instead, what is reported is the detectionAuthorsRicardo A. OleaExperimental geostatistical model of a continuous gas accumulation, Rocky Mountains, Utah
No abstract available.AuthorsRicardo A. OleaKolmogorov-Smirnov test for spatially correlated data
The Kolmogorov-Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, condAuthorsRicardo A. Olea, V. Pawlowsky-GlahnMeasuring CO2 emissions from coal fires in the U.S.
No abstract available.AuthorsAllan Kolker, Mark A. Engle, J.C. Hower, J.M.K. O'Keefe, L.F. Radke, E.L. Heffern, A. ter-Schure, G.B. Stracher, A. Prakash, Yomayra A. Roman-Colon, Ricardo A. OleaBasic Statistical Concepts and Methods for Earth Scientists
INTRODUCTION Statistics is the science of collecting, analyzing, interpreting, modeling, and displaying masses of numerical data primarily for the characterization and understanding of incompletely known systems. Over the years, these objectives have lead to a fair amount of analytical work to achieve, substantiate, and guide descriptions and inferences.AuthorsRicardo A. OleaRecent results on the spatiotemporal modelling and comparative analysis of Black Death and bubonic plague epidemics
Background: This work demonstrates the importance of spatiotemporal stochastic modelling in constructing maps of major epidemics from fragmentary information, assessing population impacts, searching for possible etiologies, and performing comparative analysis of epidemics. Methods: Based on the theory previously published by the authors and incorporating new knowledge bases, informative maps of thAuthorsG. Christakos, Ricardo A. Olea, H.-L. YuDeclustering of clustered preferential sampling for histogram and semivariogram inference
Measurements of attributes obtained more as a consequence of business ventures than sampling design frequently result in samplings that are preferential both in location and value, typically in the form of clusters along the pay. Preferential sampling requires preprocessing for the purpose of properly inferring characteristics of the parent population, such as the cumulative distribution and the sAuthorsRicardo A. OleaCORRELATOR 5.2 - A program for interactive lithostratigraphic correlation of wireline logs
The limited radius of investigation of petrophysical measurements made in boreholes and the relatively large distances between wells result in an incomplete sensing of the subsurface through well logging. CORRELATOR is a program for estimating geological properties between logged boreholes. An initial and fundamental step is the lithostratigraphic correlation of logs in different wells. The methodAuthorsRicardo A. OleaSubdivision of Holocene Baltic sea sediments by their physical properties [Gliederung holozaner ostseesedimente nach physikalischen Eigenschaften]
The Holocene sediment sequence of a core taken within the centre of the Eastern Gotland Basin was subdivided into 12 lithostratigraphic units based on MSCL-data (sound velocity, wet bulk density, magnetic susceptibility) using a multivariate classification method. The lower 6 units embrace the sediments until the Litorina transgression, and the upper 6 units subdivide the brackish-marine Litorina-AuthorsJan Harff, Geoffrey C. Bohling, R. Endler, J.C. Davis, Ricardo A. OleaSingularity and Nonnormality in the Classification of Compositional Data
Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. FortuAuthorsGeoffrey C. Bohling, J.C. Davis, Ricardo A. Olea, Jan HarffKriging: Understanding allays intimidation
In 1938 Daniel Gerhardus "Danie" Krige obtained an undergraduate degree in mining engineering and started a brilliant career centered on analyzing the gold and uranium mines in the Witwatersrand conglomerates of South Africa. He became interested in the disharmony between the poor reliability of reserve estimation reports and the magnitude of the economic decisions that were based on these studiesAuthorsRicardo A. OleaCompensating for estimation smoothing in kriging
Smoothing is a characteristic inherent to all minimum mean-square-error spatial estimators such as kriging. Cross-validation can be used to detect and model such smoothing. Inversion of the model produces a new estimator-compensated kriging. A numerical comparison based on an exhaustive permeability sampling of a 4-fr2 slab of Berea Sandstone shows that the estimation surface generated by compensaAuthorsRicardo A. Olea, Vera PawlowskyNon-USGS Publications**
Buatois, L.A., Mángano, M.G, Olea, R.A., Wilson, M.A., 2016. Decoupled evolution of soft and hard substrate communities during Cambrian Explosion and Great Ordovician Biodiversification Event. Proceedings of the National Academy of Sciences, vol. 113, no. 25, p. 6945−6948 and 28 pp. of Supporting Information.
Schuenemeyer, J. H., Olea, R.A., 2014. Distributional assumptions and parametric uncertainties in the aggregation of geologic resources. In Mathematics of Planet Earth, Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences, Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J.J., Vargas-Guzmán, J.J., editors, p. 49−52.Pardo-Igúzquiza, E., Olea, R. A., Dowd, P.A, 2014. Semivariogram model inference using the median bootstrap statistics. In Mathematics of Planet Earth, Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences, Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J.J., Vargas-Guzmán, J.J., editors, p. 79−82.
Karacan, C. Ö., Olea, R.A., 2014. Coalbed methane production analysis and filter simulation for quantifying gas drainage from coal seams. In Mathematics of Planet Earth, Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences, Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J.J., Vargas-Guzmán, J.J., editors, p. 549−552.
Olea, R.A., Luppens, J.A., Tewalt, S.J., 2014. Moving away from distance classifications as measures of resource uncertainty. In Mathematics of Planet Earth, Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences, Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J.J., Vargas-Guzmán, J.J., editors, p. 585−588.
Olea, R.A., Houseknecht, D.W., Garrity, C. P. and Cook, T.A., 2011, Assessment of shale-gas resources using correlated variables and application to the Woodford play, Arkoma basin, southeast Oklahoma: Special issue on New Quantitative Applications of Geomathematics in Earth Sciences of the Geological Survey of Spain Bulletin, Boletín Geológico y Minero, vol. 122, no.4, p. 483–496.
Martín-Fernández, J. A., Palarea-Alabaladejo, J., and Olea, R.A., 2011, Dealing with zeros. In V. Pawlowsky-Glahn and A. Buccianti (eds.), Compositional Data Analysis—Theory and Applications: Wiley & Sons, Ltd, Chichester, UK, p. 43–58.
Bunnell, J. E., Garcia, L. V., Furst, J. M., Lerch, H., Olea, R. A., Suitt, S. E., and Kolker, A., 2010, Navajo coal combustion and respiratory health near Shiprock, New Mexico. Journal of Environmental and Public Health, vol. 2010, article ID 260525, DOI:10.1155/2010/260525, 14 p.
Olea, R. A., 2009, Crossvalidation of cumulative probabilities for parameter selection in geostatistical estimation and simulation: Proceedings of the 2009 Conference of the International Association for Mathematical Geosciences, http://iamg09.stanford.edu
Martín-Fernández, J. A., Olea, R. A., and Palarea-Albaladejo, J., 2009, Multivariate approach using bootstrapping for the inference of distributional parameters of samples containing compositional values below detection limit. Abstract in Proceedings of the 31st Spanish Congress of Statistics and Operations Research, Murcia, Spain, p. 54, CD-ROM. (ISBN: 978-84-691-8159-1)
Kolker, A , Engle, M. A., Hower, J. C., O’Keefe, J. M. K., Heffern, E. L., Radke, L. F., Prakash, A., ter Schure, A., Román-Colón, Y., and Olea, R., 2009, Measuring CO2 emissions from coal fires in the U.S.: Proceedings, Annual International Coal Conference, Pittsburgh, PA, September, 2009, 7 p.
Olea, R. A., 2008, Basic Statistical Concepts and Methods for Earth Scientists: U.S. Geological Survey, Open-File Report 2008-1017, http://pubs.usgs.gov/of/2008/1017/, 191 p.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
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*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government