Seafloor imaging east and south of Australia

Project Summary
Data from three recent cruises on N.O. L’Atalante are used in collaboration with AGSO to use backscatter and bathymetry data for seafloor classification, and to reconstruct the tectonic and sedimentary history of selected areas, also based on 3.5 kHz, seismic reflection, gravity and magnetic data.

Australian Geological Survey Organization
Environment Australia 

Project Participants
Dr R. D. Müller, The University of Sydney
Dr Michael Hughes, The University of Sydney
Mr Phil Symonds, Australian Geological Survey Organization
Jonathan Bathgate, The University of Sydney, Hons student
Nik Smith, The University of Sydney, Hons student
Boris Turpeaud, The University of Sydney, Overseas visiting student
Maria Sdrolias, The University of Sydney, PhD student

The geological interpretation of backscatter imagery currently often relies on the level of experience of the interpreter and is therefore a subjective and time consuming method of analysis. In an effort to improve this procedure we are developing automated imae classification routines for the interpretation of backscatter data using artificial neural networks. As test area we are using a survey in the Great Australian Bight (E130º – E131º) that was conducted for Environment Australia. This area has been declared a marine park, as it is free of anthropogenic disturbance. It includes a range of depths (~400m – ~3500m) and seafloor environments that can be classified and used as standards for pattern recognition processes.

The second area analysed as part of this project is the Norfolk Basin. It is divided by a saddle located at the Norfolk ridge at 29S 168E, where Norfolk Island is the surface expression of the raised topography, and continues ESE until it reaches the Three Kings Ridge at 30S 173E. At its Western end, the ridge begins as a large bulge in the eastern edge of the Norfolk Ridge, which extends from 168E to 168.5E. At the end of this bulge, the topography drops off dramatically, and the ridge becomes less pronounced, and more defined by volcanic features. These volcanoes tend to be elongate in the saddle direction, and often have become flat-topped guyots. The nature of these volcanoes may give insight into the evolution of the Norfolk basins. The third area analysed is the South Tasman Rise and the Tasmanian shelf, based on multibeam data collected in 1994 and 1999/2000.

Analysis of Marine Multi-beam Data

The ocean floor, which covers approximately 70% of the Earth’s surface, still contains vast areas which are largely unexplored. In truth less of the ocean floor has been mapped than either of the surfaces of the Moon or Venus, which has been mapped in recent years by NASA’s Magellan spacecraft. Developments over the last 20 years have produced tools which allow the research community and industry to map the ocean floor in greater detail and over more extensive area than previously possible. These tools are exclusively remote sensing ones and are extremely varied. Their application covers a wide range of the electromagnetic spectrum and include ship-borne gravity and magnetics; satellite measurement of sea surface height and temperature; and seismic data. reflection and refraction, as well as sidescan sonar and swath bathymetry techniques which produce images of the seafloor. All of these methods, their associated processing techniques and the interpretations they produce are invaluable for research into the processes which shape the ocean floor.

R Dietmar Müller, Geology and Geophysics, University of Sydney
Phil Symonds, Australian Geological Survey Organization

Project aims
We employ state of the art textural image analysis and computer programs in the form of neutral networks to develop an automated technique for seafloor classification. Research into textural image analysis is a fairly recent field as is the use of neural networks but their application to sea floor remote sensing is a new frontier. Textural image analysis techniques have been developed for medical, recognition of man-made structures and patterns and whilst there is a reasonable bank of knowledge for standard statistical methods, involving gray level, probability distribution functions of different forms there has been little application of the techniques to seafloor characterization. In more recent times novel approaches to textural analysis involving unitary sinusoidal transforms have been developed for texture analysis and classification. Textural analysis can be used in two ways, (1) to extract features or (2) classify an image. Once an image has been analyzed and classified, by looking at as small an area as is possible it is then necessary to use these classes and classify the entire image rather than isolated areas. Neutral networks are being used for this task due to their capability to be trained.