6 MT-Proc (original) (raw)
Nine (9) high-resolution of aeromagnetic and radiometric data of the Benue Trough and adjacent Anambra Basin, were evaluated to understand the subsurface geological structure of the area. Various data enhancement/derivative techniques like analytic signal, first and second vertical derivatives, horizontal derivative and tilt derivative were employed in delineating magnetic lineaments, edges, lithological boundaries, and contact zones within the area. The results obtained revealed magnetic lineaments, shear zones and shallow faults, trending mostly in NE-SW, E-W, and NW-SE directions. The ternary image generated from Radiometric data using oasis Montaj software highlights the lithology of Asu River Group, Eze-Aku Formation, Agwu Shale, Nkporo Shale, Nsukka Formation and Ajali Sandstone. This shows an improved version of the geological mapping of the study area
In seismic surveying, sound waves are mechanically generated and sent into the earth (figure-1) some of this energy is reflected back to recording sensors, sensors are measuring devices that record accurately the strength of this energy and the time sound waves has taken for this energy to travel through the various layers in the earth's crust and back to the location of the sensors. These recordings are then taken and, using specialised seismic data processing, are transformed into visual image of subsurface of earth in the seismic survey area. Just as doctors use x-ray to "see" into the human body indirectly, geoscientist uses seismic surveying to obtain a picture of the structure and nature of the rock layers indirectly
A machine learning tool for interpretation of Mass Transport Deposits from seismic data
Scientific Reports
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 well-known hydrocarbon producing region off New Zealand 30 and mainly lies to the west of the North Island (Fig. 1). The basin is ~ 60 km wide and extends ~ 350 km in the NNE direction from south of the Taranaki peninsula to the offshore west of Auckland 31. The basin forms part of the overriding Australian plate and lies about ~ 400 km west of the Hikurangi Trough, where the Pacific plate is subducted 32−33. Tectonic evolution of the basin includes extension during the Late Cretaceous to Early Eocene, followed by compression during the Late Eocene and back-arc extension from the Late Miocene to Recent 32. Towards the end of Miocene, the basin accumulated large sediment influx, known as the Giant Foresets, deposited in shelf to basin succession 27,33−35. MTDs are widespread within this basin and have been recognised throughout the offshore in TB. Among these deposits, a submarine MTD, called the Karewa MTD (~ 25 km long and 4 km wide) lies within open