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When airborne electromagnetic (AEM) data is acquired as a streamed or time-series data set, the great redundancy in the data favours compression as a first step in processing. Traditional data compression schemes are time windowing and spatial averaging. An alternative, more efficient data compression scheme is to transform time or frequency domain data to time-constant tau space, which has the effect of removing the waveform dependence of the AEM response.When there are many local anomalies and a variable background, the next stage of rapid processing is to transform the response to a conductivity-depth image (CDI) to facilitate geological interpretation of the background response. Use of the full time range of recorded data, particularly the inclusion of on-time data, improves the stability of the CDI process.The final AEM data processing step for mineral explora tion is to assess the likelihood that any local anomaly corresponds to a desired economic target. This step involves the extraction of target geometry and conductivity informa tion from the AEM data. The only economically feasible route at the present time is to parameterise both the data (using inductive and resistive limits) and the model to allow inversion of the local anomalies. A fit to one or two plate like conductors can be achieved in seconds; fits to a block like body take minutes on a fast PC. A significant research challenge remains to speed up and stabilise this process. APPENDIX A: MATHEMATICAL BASIS OF ARBITRARY WAVEFORM DECOMPOSITIONThis brief summary includes some sign corrections from the article by Stolz and Macnae(1998): Representation of time-domain step and frequencydomain responseThe step function response of an isolated conductor can be exnressed as:

This article describes an automatic detector for marine mammal vocalizations. Even though there has been previous research on optimizing automatic detectors for specific calls or specific species, the detection of any type of call by a diversity of marine mammal species still poses quite a challenge--and one that is faced more frequently as the scope of passive acoustic monitoring studies and the amount of data collected increase. Information (Shannon) entropy measures the amount of information in a signal. A detector based on spectral entropy surpassed two commonly used detectors based on peak-energy detection. Receiver operating characteristic curves were computed for performance comparison. The entropy detector performed considerably faster than real time. It can be used as a first step in an automatic signal analysis yielding potential signals. It should be followed by automatic classification, recognition, and identification algorithms to group and identify signals. Examples are shown from underwater recordings in the Western Canadian Arctic. Calls of a variety of cetacean and pinniped species were detected.

This article presents a method for reducing the computation time required for estimating cumulative sound exposure levels. Sound propagation has to be computed from every source position to every desired receiver location; so if there are many source positions, then the problem can quickly become computationally expensive. The authors' solution to this problem is to extract all possible source-receiver pathways and to cluster these with a self-organizing neural net. Sound propagation is modeled only for the cluster centroids and extrapolated for the entire geographic region. The tool is illustrated for the example of a marine seismic survey over a tropical coral reef. Resident fish species were expected not to flee the reef, but to stay among the corals for the entire duration of the survey. In such cases, the modeling of cumulative sound exposure levels is sometimes requested as part of environmental impact assessments. The tool developed combines a seismic source model, a near-field sound propagation model, and a far-field sound propagation model. The neural network reduces the computation time by a factor of 55. The cost is an error in modeled received levels of less than -1+/-3 dB re 1 microPa(2) s.

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Seismic anisotropy in sedimentary environments is significant-microseismic waveforms often show strong shear-wave splitting, with differences reaching 40% between horizontally and vertically-polarized shear-wave velocities. Failure to account for this anisotropy is shown to result in large microseismic event location errors. A method is presented here for determining the five elastic parameters of a homogeneous, vertical transverse-isotropic (VTI) model from calibration shot data. The method can also use data from mininginduced seismic events, which are then simultaneously located. This simple model provides a good fit to arrival times from coal-environment data, and results in dramatic shifts in interpreted event locations.

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Environmental assessments of manmade noise and its effects on marine mammals need to address the question of how noise interferes with animal vocalizations. Seeking the answer with animal experiments is very time consuming, costly, and often infeasible. This article examines the possibility of estimating results with software models. A matched filter, spectrogram cross-correlation, critical band cross-correlation, and a back-propagation neural network detected a beluga vocalization in three types of ocean noise. Performance was compared to masked hearing experiments with a beluga whale [C. Erbe and D. M. Farmer, Deep-Sea Res. II 45, 1373-1388 (1998)]. The artificial neural network simulated the animal data most closely and raised confidence in its ability to predict the interference of a variety of noise source with a variety of vocalizations.