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Quantitative seismic attribute analysis using artificial neural networks and seismic stratigraphic interpretation of lower to middle Miocene sediments offshore New Jersey

Posted on:2013-07-09Degree:M.SType:Thesis
University:Rutgers The State University of New Jersey - New BrunswickCandidate:Karakaya, SarpFull Text:PDF
GTID:2450390008971638Subject:Geology
Abstract/Summary:
This study comprises two parts intended to improve understanding of the lower and middle Miocene depositional history of the New Jersey continental shelf. The first, lower Miocene-based part, aims to determine lateral variations in lithofacies between holes drilled by IODP Expedition 313 using seismic attributes and artificial neural networks. The second provides detailed seismic sequence stratigraphy of mid-Miocene successions.;Neural networks are used in the first part to search for a relationship between seismic attributes and gamma log measurements of the lower Miocene section. Using this relationship, the networks generate 'pseudo gamma logs' that predict lateral changes in lithofacies based on accompanying changes in seismic attributes. A successful test of the technique is demonstrated using 3D seismic data and 6 closely-spaced gamma ray logs from the Denver Basin. A similar application to lower Miocene successions offshore NJ is unsuccessful, most likely due to an insufficient number of wells, complexity of lithofacies variations between wells up to 12 km apart, and/or an incorrect selection of attributes.;In the second part, candidate sequence boundaries are identified in a grid of high-resolution, densely spaced profiles. In addition to a more detailed history than derived from prior studies, this part reveals previously unreported records of sediment erosion and possible global climate influence on the middle Miocene stratigraphic evolution offshore New Jersey. Eleven candidate sequence boundaries, three not documented by previous studies, are identified. System tract positions of each sequence are determined, while only one transgressive system tract and no lowstand fans are observed. Age estimates based on published studies show that the 11 mid Miocene sequences reported here span the interval between ∼11.8-12.9 Ma, suggesting an average interval between each of 100 kyr. Clinoform rollovers prograded SE during the development of the oldest sequence of the study area beginning at a time that coincides with a major shift in delta18O towards heavier values (represented by Mi4) and at about the time of the permanent East Antarctic ice sheet development. Grain size distribution of the prograding clinoforms is predicted by extrapolating IODP Expedition 313's lithostratigraphic analysis of lower Miocene succession.
Keywords/Search Tags:Miocene, Lower, Neural networks, Seismic, New, Using, Offshore, Part
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