Electromagnetic induction data parameterized in time dependent object intrinsic polarizabilities allow discrimination of unexploded ordnance (UXO) from false targets (scrap metal). Data from a cart-mounted system designed for discrimination of UXO with 20 mm to 155 mm diameters are used. Discrimination of UXO from irregular scrap metal is based on the principal dipole polarizabilities of a target. A near-intact UXO displays a single major polarizability coincident with the long axis of the object and two equal smaller transverse polarizabilities, whereas metal scraps have distinct polarizability signatures that rarely mimic those of elongated symmetric bodies. Based on a training data set of known 1 targets, object identification was made by estimating the probability that an object is a single UXO. Our test survey took place on a military base where both 4.2" mortar shells and scrap metal were present. The results show that we detected and discriminated correctly all 4.2" mortars, and in that process we added 7%, and 17%, respectively, of dry holes (digging scrap) to the total number of excavations in two different survey modes. We also demonstrated a mode of operation that might be more cost effective than the current practice.

No abstract

No abstract

No abstract

[1] Electric and magnetic fields in the (10 −4 -1.0) Hz band were monitored at two sites adjacent to the San Andreas Fault near Parkfield and Hollister, California, from 1995. A data window (2002-2005, enclosing the 28 September 2004 M6 Parkfield earthquake, was analyzed to determine if anomalous electric or magnetic fields or changes in ground conductivity occurred before the earthquake. The data were edited, removing intervals of instrument malfunction, leaving 875 days in the 4 year period. Frequent, spikelike disturbances were common but were not more frequent around the time of the earthquake; these were removed before subsequent processing. Signal-to-noise amplitude spectra, estimated via magnetotelluric processing, showed the behavior of the ultralow frequency fields to be remarkably constant over the period of analysis. These first-order plots make clear that most of the recorded energy is coherent over the spatial extent of the array. Three main statistical techniques were employed to separate local anomalous electrical or magnetic fields from the dominant coherent natural fields: transfer function estimates between components at each site were employed to subtract the dominant field, and look deeper at the "residual" fields; the data were decomposed into principal components to identify the dominant coherent array modes; and the technique of canonical coherences was employed to distinguish anomalous fields which are spatially broad from anomalies which occur at a single site only, and furthermore to distinguish anomalies present in both the electric and magnetic fields from those present in only one field type. Standard remote reference apparent resistivity estimates were generated daily at Parkfield. A significant seasonal component of variability was observed, suggesting local distortion due to variations in near-surface resistance. In all cases, high levels of sensitivity to subtle electromagnetic effects were demonstrated, but no effects were found that can be reasonably characterized as precursors to the Parkfield earthquake.

The magnetotelluric method employs co-located surface measurements of electric and magnetic fields to infer the local electrical structure of the earth. The frequency-dependent "apparent resistivity" curves can be inaccurate at long periods if input data are contaminated-even when robust remote reference techniques are employed. Data despiking prior to processing can result in significantly more reliable estimates of long period apparent resistivities. This paper outlines a two-step method of automatic identification and replacement for spike-like contamination of magnetotelluric data; based on the simultaneity of natural electric and magnetic field variations at distant sites. This simultaneity is exploited both to identify windows in time when the array data are compromised, and to generate synthetic data that replace observed transient noise spikes. In the first step, windows in data time series containing spikes are identified via intersite comparison of channel 'activity' -such as the variance of differenced data within each window. In the second step, plausible data for replacement of flagged windows is calculated by Wiener filtering coincident data in clean channels. The Wiener filters -which express the time-domain relationship between various array channels -are computed using an uncontaminated segment of array training data. Examples are shown where the algorithm is applied to artificially contaminated data, and to real field data. In both cases all spikes are successfully identified. In the case of implanted artificial noise, the synthetic replacement time series are very similar to the original recording. In all cases, apparent resistivity and phase curves obtained by processing the despiked data are much improved over curves obtained from raw data.2

This paper utilizes 10 stations of co-located seismometer, QuakeFinder/infrasound to observe co-seismic signatures triggered by the 6 February 2016 M 6.6 Meinong Earthquake. Each QuakeFinder system consists of a 3-axes induction magnetometer, an air conductivity sensor, a geophone, and temperature/relative humidity sensors. There are no obvious charges in the positive/negative ions, the temperature, and the humidity, while the magnetometer, the geophone, and infrasound data detect clear co-seismic signatures, similar to seismic waves recorded by seismometers. The magnetometers register high-frequency pulsations, like seismic waves, and superimpose with low-frequency variations, which could be caused by the magnetometer shaking/tilting and/or the underground water level change, respectively, upon the arrival of seismic waves. The spectrum centering around 2.0 Hz of the co-seismic geophone fluctuations is similar to that of the seismic waves. However, the energy of co-seismic geophone fluctuations (also magnetometer pulsations) yields an exponential decay to the distance of a station to the epicenter, while the energy of the seismic waves is inversely proportional to the square of the distance. This suggests that the mechanisms for detecting seismic waves of the QuakeFinder system and seismometers are different. In general, the geophone and magnetometer/infrasound system are useful to record high-and low-frequency seismic waves, respectively.

A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these ''training events'' are represented in matrix form and transpose-multiplied to generate timedomain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window (approximately the width of the training event) is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigenvectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled (50 Hz) data from six observatories, each equipped with threecomponent induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.