Geology Reports⌕ Search

USGS · 70014969

Determination of total, commonality, and uniqueness of interpreted structural elements from remotely sensed data in Alaska

Abstract

Statistical analysis is conducted to determine the unique value of real- and synthetic-aperture side-looking airborne radar (SLAR) to detect interpreted structural elements. SLAR images were compared to standard and digitally enhanced Landsat multispectral scanner (MSS) images and to aerial photographs. After interpretation of the imagery, data were cumulated by total length in miles and by frequency of counts. Maximum uniqueness is obtained first from real-aperture SLAR, 58.3% of total, and, second, from digitally enhanced Landsat MSS images, 54.1% of total. ?? 1986 Plenum Publishing Corporation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G.H. Rosenfield. 1986. Determination of total, commonality, and uniqueness of interpreted structural elements from remotely sensed data in Alaska. https://doi.org/10.1007/bf00898281

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Declustering 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 semivariogram. Consideration of the distance to the nearest neighbor allows preparation of resampled sets that produce comparable results to those from previously proposed methods. Clustered sampling of size 140, taken from an exhaustive sampling, is employed to illustrate this approach. ?? International Association for Mathematical Geology 2007.

Mathematical Geology↗

Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

Mathematical Geology↗

Comparison of two probability distributions used to model sizes of undiscovered oil and gas accumulations: Does the tail wag the assessment?

Undiscovered oil and gas assessments are commonly reported as aggregate estimates of hydrocarbon volumes. Potential commercial value and discovery costs are, however, determined by accumulation size, so engineers, economists, decision makers, and sometimes policy analysts are most interested in projected discovery sizes. The lognormal and Pareto distributions have been used to model exploration target sizes. This note contrasts the outcomes of applying these alternative distributions to the play level assessments of the U.S. Geological Survey's 1995 National Oil and Gas Assessment. Using the same numbers of undiscovered accumulations and the same minimum, medium, and maximum size estimates, substitution of the shifted truncated lognormal distribution for the shifted truncated Pareto distribution reduced assessed undiscovered oil by 16% and gas by 15%. Nearly all of the volume differences resulted because the lognormal had fewer larger fields relative to the Pareto. The lognormal also resulted in a smaller number of small fields relative to the Pareto. For the Permian Basin case study presented here, reserve addition costs were 20% higher with the lognormal size assumption. ?? 2002 International Association for Mathematical Geology.

Mathematical Geology↗