Bandwidth requirements for shallow, high-resolution seismic reflection

The optimum bandwidth for shallow, high-resolution seismic reflection differs from that required for conventional petroleum reflection. An understanding of this issue is essential for correct choice of acquisition instrumentation. Numerical modelling of simple Bowen Basin coal structures illustrates that, for high-resolution imaging, it is important to accurately record all frequencies up to the limit imposed by earth scattering. On the contrary, the seismic image is much less dependent on frequencies at the lower end of the spectrum. These quantitative observations support the use of specialised high-frequency geophones for high-resolution seismic imaging. Synthetic seismic inversion trials demonstrate that, irrespective of the bandwidth of the seismic data, additional low-frequency impedance control is essential for accurate inversion. Inversion provides no compelling argument for the use of conventional petroleum geophones in the high-resolution arena.

Work at hand: an exploration of gesture in the context of work and everyday life to inform the design of gestural input devices

This research explores gestures used in the context of activities in the workplace and in everyday life in order to understand requirements and devise concepts for the design of gestural information appliances. A collaborative method of video interaction analysis devised to suit design explorations, the Video Card Game, was used to capture and analyse how gesture is used in the context of six different domains: the dentist's office; PDA and mobile phone use; the experimental biologist's laboratory; a city ferry service; a video cassette player repair shop; and a factory flowmeter assembly station. Findings are presented in the form of gestural themes, derived from the tradition of qualitative analysis but bearing some similarity to Alexandrian patterns. Implications for the design of gestural devices are discussed.

On the relationship between Bayesian error bars and the input data density

We investigate the dependence of Bayesian error bars on the distribution of data in input space. For generalized linear regression models we derive an upper bound on the error bars which shows that, in the neighbourhood of the data points, the error bars are substantially reduced from their prior values. For regions of high data density we also show that the contribution to the output variance due to the uncertainty in the weights can exhibit an approximate inverse proportionality to the probability density. Empirical results support these conclusions.

Regression with input-dependent noise: A Gaussian process treatment

Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.