Search USGS⌕ Search
Home/Archive/USGS

USGS · 70019237

Harmonic Analysis of Sedimentary Cyclic Sequences in Kansas, Midcontinent, USA

D. F. Merriam·J.E. Robinson
View original source ↗Export metadata ↓

Abstract

Several stratigraphic sequences in the Upper Carboniferous (Pennsylvanian) in Kansas (Midcontinent, USA) were analyzed quantitatively for periodic repetitions. The sequences were coded by lithologic type into strings of datasets. The strings then were analyzed by an adaptation of a one-dimensional Fourier transform analysis and examined for evidence of periodicity. The method was tested using different states in coding to determine the robustness of the method and data. The most persistent response is in multiples of 8-10 ft (2.5-3.0 m) and probably is dependent on the depositional thickness of the original lithologic units. Other cyclicities occurred in multiples of the basic frequency of 8-10 with persistent ones at 22 and 30 feet (6.5-9.0 m) and large ones at 80 and 160 feet (25-50 m). These levels of thickness relate well to the basic cyclothem and megacyclothem as measured on outcrop. We propose that this approach is a suitable one for analyzing cyclic events in the stratigraphic record.

Explore related subjects

Mathematical GeologyArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D. F. Merriam, J.E. Robinson. 1997. Harmonic Analysis of Sedimentary Cyclic Sequences in Kansas, Midcontinent, USA. https://doi.org/10.1023/a%3A1022301417788

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

KEEP EXPLORING

Related USGS reports

USGS2007

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↗
USGS2006

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↗
USGS2002

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↗