For a linear elastic Earth the time derivative of the ground force is considered proportional to the far‐field wavelet. Under the assumption that the baseplate is stiff and the bending forces of the baseplate are negligible, the ground force is also approximated by the sum of the accelerations of the baseplate and the reaction mass weighted by the respective masses. Combining these two assumptions, the time derivative of the weighted sum is considered proportional to the far‐field wavelet. This result, often referred to as the far‐field wavelet assumption, although convenient and most often employed is not always valid. We explore its validity using the spectral harmonic ratios of recorded data, which are used extensively in data filtering and analysis of vibratory data. We show that the far‐field wavelet assumption fails particularly for harmonic components of even order. More compact soil after repeated shots further invalidates this assumption. Non‐linear modelling of the ground under the vibrator point may provide a direction towards solving this discrepancy. Finally, we describe a method for the estimation of the harmonic spectral ratios.

Event picking is used in many steps of seismic processing. We present an automatic event picking method that is based on a new attribute of seismic signals, instantaneous traveltime. The calculation of the instantaneous traveltime consists of two separate but interrelated stages. First, a trace is mapped onto the time-frequency domain. Then the time-frequency representation is mapped back onto the time domain by an appropriate operation. The computed instantaneous traveltime equals the recording time at those instances at which there is a seismic event, a feature that is used to pick the events. We analyzed the concept of the instantaneous traveltime and demonstrated the application of our automatic picking method on dynamite and Vibroseis field data.

Radioxenon isotopes measured at radionuclide stations of the Comprehensive Nuclear-Test-Ban Treaty’s (CTBT) International Monitoring System (IMS) may indicate releases from underground nuclear explosions (UNEs) but are often caused by emissions from nuclear facilities. Characterization of CTBT-relevant nuclear events may use the evolution of isotopic activity ratios over time, which goes from the release of an assumed UNE, through atmospheric transport, to sample collections and measurements. A mathematical approach is presented to discuss the characterization of the spatial and temporal relationships between a nuclear explosion and radioxenon measurements. On the one hand, activity concentrations at an IMS station are estimated by using the assumed release scenario regarding a UNE and atmospheric transport modelling. On the other hand, the activities collected in the samples are determined by spectral analysis first and the activity concentrations in the air passing over the IMS station are estimated under an assumption of constant concentration during sampling. The isotopic ratios of activities released from the UNE are related to the isotopic ratios of activity concentrations in the plume of air crossing the IMS station, resulting in a function of the isotopic activity ratio over the time from detonation to sample measurement. The latter is used for discrimination of a nuclear test and estimation of the time of detonation, such as a four radioxenon plot of the activity ratio relationship of 135Xe/133Xe versus 133mXe/131mXe.

This paper presents an intensive-care acquisition and signal processing integrated framework in the area of intensive care units. The framework includes nearly all monitored biosignals in the intensive care, along with metadata and processing results. It is structured on two basic applications, i.e., the acquisition and the database one, running in two different PCs that are connected through a local area network, facilitating real-time data exchange between them. The analytical rundown shows that the proposed framework is a serious effort to give a complete clinical condition of a patient and a form of a diagnostic analysis implement in the intensive care by taking in real-time processing.

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