Abstract:The Neogene section in the northern Taranaki Basin, offshore New Zealand, displays an interaction among prograding clinoforms, listric growth faults formed at the base of slope and mass transport deposits that fill the growth fault depocentres. This study focuses on one of these systems, the Karewa Fault and mass transport deposit (MTD), in order to understand the genetic relationship between the fault and the MTD in its hangingwall depocentre, i.e. did the MTD fill existing accommodation space? Did the MTD tr… Show more
“…Armstrong, Mohrig, Hess, George, & Straub, 2014;Gagliano, 2005). Their large-scale, structural and stratigraphic characteristics have been largely revealed by remote-sensing, acoustic and seismic imaging, mainly because of the impact of changing fault dips, and marked impedance of the related fault rock types on the seismic signal King, Back e, Morley, Hillis, & Tingay, 2010;Panpichityota, Morley, & Ghosh, 2018). Field-based studies, specifically aimed to provide insights on the micro to mesoscale (i.e.…”
Section: O R I G I N a L A R T I C L E Abstractmentioning
When, in March 2011, I entered the building of the University Centre in Svalbard (UNIS) my first thought was:"I want to work here". This dream came true only a year later when I started my long but very rewarding journey through the PhD project. Firstly, I would like to thank my supervisor Snorre Olaussen for offered chance to develop this exciting project in Arctic rift basin geology, his patience and trust. Transition from research conducted in structural geology of metamorphic rocks to basin analysis was not easy and Snorre always served with the guidance, help and tutoring. Huge acknowledgments go to Alvar Braathen, who has introduced me to the outcrops of Billefjorden and Edgeøya, and always offered great support and advice. William Helland Hansen and Jan Inge Faleide are acknowledged for their supervision on the project. I strongly appreciate the scientific freedom and multiple possibilities for development and collaboration I had during the research.
“…Armstrong, Mohrig, Hess, George, & Straub, 2014;Gagliano, 2005). Their large-scale, structural and stratigraphic characteristics have been largely revealed by remote-sensing, acoustic and seismic imaging, mainly because of the impact of changing fault dips, and marked impedance of the related fault rock types on the seismic signal King, Back e, Morley, Hillis, & Tingay, 2010;Panpichityota, Morley, & Ghosh, 2018). Field-based studies, specifically aimed to provide insights on the micro to mesoscale (i.e.…”
Section: O R I G I N a L A R T I C L E Abstractmentioning
When, in March 2011, I entered the building of the University Centre in Svalbard (UNIS) my first thought was:"I want to work here". This dream came true only a year later when I started my long but very rewarding journey through the PhD project. Firstly, I would like to thank my supervisor Snorre Olaussen for offered chance to develop this exciting project in Arctic rift basin geology, his patience and trust. Transition from research conducted in structural geology of metamorphic rocks to basin analysis was not easy and Snorre always served with the guidance, help and tutoring. Huge acknowledgments go to Alvar Braathen, who has introduced me to the outcrops of Billefjorden and Edgeøya, and always offered great support and advice. William Helland Hansen and Jan Inge Faleide are acknowledged for their supervision on the project. I strongly appreciate the scientific freedom and multiple possibilities for development and collaboration I had during the research.
“…Armstrong, Mohrig, Hess, George, & Straub, ; Gagliano, ). Their large‐scale, structural and stratigraphic characteristics have been largely revealed by remote‐sensing, acoustic and seismic imaging, mainly because of the impact of changing fault dips, and marked impedance of the related fault rock types on the seismic signal (Back & Morley, ; King, Backé, Morley, Hillis, & Tingay, ; Panpichityota, Morley, & Ghosh, ). Field‐based studies, specifically aimed to provide insights on the micro to mesoscale (i.e.…”
The Late Triassic outcrops on southern Edgeøya, East Svalbard, allow a multiscale study of syn-sedimentary listric growth faults located in the prodelta region of a regional prograding system. At least three hierarchical orders of growth faults have been recognized, each showing different deformation mechanisms, styles and stratigraphic locations of the associated detachment interval. The faults, characterized by mutually influencing deformation envelopes over space-time, generally show SW-to SE-dipping directions, indicating a counter-regional trend with respect to the inferred W-NW directed progradation of the associated delta system. The down-dip movement is accommodated by polyphase deformation, with the different fault architectural elements recording a time-dependent transition from fluidal-hydroplastic to ductile-brittle deformation, which is also conceptually scale-dependent, from the smaller-(3rd order) to the larger-scale (1st order) end-member faults respectively. A shift from distributed strain to strain localization towards the fault cores is observed at the meso to microscale (<1 mm), and in the variation in petrophysical parameters of the litho-structural facies across and along the fault envelope, with bulk porosity, density, pore size and microcrack intensity varying accordingly to deformation and reworking intensity of inherited structural fabrics. The second-and third-order listric fault nucleation points appear to be located above blind fault tip-related monoclines involving cemented organic shales. Close to planar, through-going, first-order faults cut across this boundary, eventually connecting with other favourable lower-hierarchy fault to create seismic-scale fault zones similar to those imaged in the nearby offshore areas. The inferred large-scale driving mechanisms for the first-order faults are related to the combined effect of tectonic reactivation of deeper Palaeozoic structures in a far field stress regime due to the Uralide orogeny, and differential compaction associated with increased sand sedimentary input in a fine-grained, water-saturated, low-accommodation, prodeltaic depositional environment. In synergy to this large-scale picture, small-scale causative factors favouring second-and third-order faulting seem to be related to mechanicalrheological instabilities related to localized shallow diagenesis and liquidization fronts.--
“…The MTD has been defined from seismic data by computing a hybrid-or meta-attribute, which has been generated by combining a set of other seismic attributes that are specific to MTD using a machine learning approach. Though the outcome is generally validated with the existing geology or well information or available literatures or petroleum reports 36,38 , the performance of the neural model is checked here by visual inspection. Comparison of seismic section (Fig.…”
Section: Discussionmentioning
confidence: 99%
“…Among these deposits, a submarine MTD, called the Karewa MTD (~ 25 km long and 4 km wide) lies within the northern TB offshore. Panpichityota et al 36 used manual method coupled with attribute analysis for mapping the Karewa MTD with a view to understand their geometric relation with the Karewa fault within the study area. Figure 1 Location of the study area in the northern Taranaki Basin, offshore New Zealand.…”
Section: Introductionmentioning
confidence: 99%
“…the northern TB offshore. Panpichityota et al 36 used manual method coupled with attribute analysis for mapping the Karewa MTD with a view to understand their geometric relation with the Karewa fault within the study area.…”
Machine learning is a tool that allows machines or intelligent systems to learn and get equipped to solve complex problems in predicting reliable outcome. the learning process consists of a set of computer algorithms that are employed to a small segment of data with a view to speed up realistic interpretation from entire data without extensive human intervention. Here we present an approach of supervised learning based on artificial neural network to automate the process of delineating structural distribution of Mass Transport Deposit (MTD) from 3D reflection seismic data. The responses, defined by a set of individual attributes, corresponding to the MTD, are computed from seismic volume and amalgamated them into a hybrid attribute. this generated new attribute, called as MTD Cube meta-attribute, does not only define the subsurface architecture of MTD distinctly but also reduces the human involvement thereby accelerating the process of interpretation. the system, after being fully trained, quality checked and validated, automatically delimits the structural geometry of MTDs within the Karewa prospect in northern Taranaki Basin off New Zealand, where MtDs are evidenced. Mass Transport Deposits (MTDs), occurring in different tectonic and depositional settings, are defined as gravity induced slope failure deposits that include creeps, slides, slumps and debris flows 1−6. These deposits are internally deformed and associated with variable shape and size. During slope failure, masses tend to flow downslope over a shearing surface, called the basal shear surface (BSS) that forms the base of the MTDs. BSS preserves the record of all erosional and deformational activities experienced by these deposits or masses during their translation. Their interpretation is crucial, as such deposits during translation over the instable slope may lead to several catastrophic submarine events e.g., landslides, tsunamis, avalanches and thus possess precursory threats for subsea installations 7−15. Several authors 16−25 attempted to study the MTDs in order to understand their evolution, geomorphic character and possible trigger mechanisms responsible for slope failure. The use of modern techniques e.g., reflection seismic (2D/3D), side scan sonar, bathymetry etc. added value for their detailed investigation. In reflection seismic, the MTDs are first identified by mapping their top and BSS, and then interpreted from cross-sections and attribute maps 23−27. For this, the seismic attributes such as the root-mean square (RMS) amplitude, dip magnitude and coherency have been used for the interpretation of this geologic feature 23−26. Though the single attribute technology has been successful in interpreting MTDs from seismic data, several authors 28−29 demonstrated the downside of such approach, where a single attribute hardly ever responds to a particular geological target (see sections "Initial Interpretation" and "From Seismic Attributes to Meta-attributes" in the Supplementary Note for detailed explanation). The Taranaki Basin (TB) is a we...
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