Investigating the effect of data quality on time domain electromagnetic discrimination
Using field data and numerical simulations we investigate the effect of data quality on time domain electromagnetic discrimination. Data quality decreases when measurements contain responses not accounted for by our mathematical modelling. This can include instrument noise, inaccurately reported position and orientation information, geologic contributions to the signal, and loss of validity of the forward modelling. Survey design is critical to data quality in order to have sufficient sampling of data anomalies, and also to ensure that each target is illuminated such that both the axial and transverse components of the polarization can be excited and measured. For dipole model based discrimination algorithms, success is contingent upon the accuracy with which the components of the polarization tensor can be estimated. Field data from different survey modes are analysed to identify noise sources and provide quantitative estimates of the noise in each survey. Inversion results show that increased noise levels lead to greater spread in recovered parameters. Monte Carlo simulations are performed in order to investigate the importance of other data quality factors. Analysis of inversion results from the simulations show that anomaly size, signal to noise ratio, positioning error, line spacing and station spacing all play a role in the spread of recovered parameters. Through the analysis of our simulation results we propose a figure of merit as a means of quantifying different data quality factors with a single number and relate this number to the accuracy with which parameters can be estimated.
Phenomenolgical model inversion with Fisher information metrics for unexploded ordnance detection
Many of the ongoing efforts to develop strategies for detecting and locating subsurface unexploded ordnance (UXO) use features based on phenomenological models to discriminate between UXO and harmless clutter. The process of generating features requires model inversion to fit the phenomenological model to the measured sensor data. In commonly-used model inversion processes, the standard measures of model fit error do not incorporate the spatial distribution of the data used in the model inversion. This study incorporates the Fisher information in a joint metric optimization to assess the spatial distribution of data and how well the model parameters are supported by the data used in the model inversion. The outcomes of this study indicate that some outliers in the feature space can be mitigated by considering the Fisher information in the model inversion process, resulting in improved unexploded ordnance detection rates in a test using data collected at Camp Sibert, Alabama.
An improved Bayesian decision theoretic approach for land mine detection
A rigorous signal detection theoretic analysis is used to improve detectability of land mines. The development is performed for sensors that integrate time-domain information to provide a single data point (standard metal detector), those that provide a sampled portion of the time-domain waveform, and those that operate at several discrete frequencies. This approach is compared to standard thresholding techniques, and it is shown to provide substantial improvements when evaluated on measured data.