MODEL BASED APPROACH TO ZJXO IMAGING USING THE TZME 1 . 0

Time domain electromagnetic (TDEM) sensors have emerged as a field-worthy technology for UXO detection in a variety of geological and environmental settings. This success has been achieved with commercial equipment that was not optimized for UXO detection and discrimination. The TDEM response displays a rich spatial and temporal behavior which is not currently utilized. Therefore, in this paper we describe a research program for enhancing the effectiveness of the TDEM method for UXO detection and imaging. Fundamental research is required in at least three major areas: (a) model based' imaging capability i.e. the forward and inverse problem, (b) detector modeling and instrument design, and (c) target recognition and discrimination algorithms. These research problems are coupled and demand a unified treatment. For example: (i) the inverse solution depends on solution of the forward problem and knowledge of the instrument response; (ii) instrument design with improved diagnostic power requires forward and inverse modeling capability; and (iii) improved target recognition . algorithms (such as neural nets) must be trained with data collected from the new instrument and with synthetic data computed using the forward model. Further, the design of the appropriate input and output layers of the net will be informed by the results of the forward and inverse modeling. A more fully developed model of the TDEM response would enable the joint inversion of data collected from multiple sensors (e.g. TDEM sensors and magnetometers): Finally, we suggest that a complementary approach to joint inversions is the statistical recombination of data using principal component analysis. The decomposition into principal components is useful since the first principal component contains those features that are most strongly correlated from image to image. voltages or emf's are measured in receiver coils. The voltages are induced according to Faraday's law by the time varying scattered fields. Measurements are performed in the time domain after the termination of the primary current waveform in the transmitter loop. The target conductor and ground response signals can be separated since currents from the latter decay much more rapidly than currents in conductors with the large and positive conductivity contrasts that are typical of UXO. The residual effect of the primary field can be separated since it is present only for a very short time after the termination of the current waveform. TDEM sensors (e.g. the Geonics Ltd. EM61) that have been deployed in the field over the last several years for UXO detection measure the induced voltages in only one time gate. In addition, the transmitter and receiver coils of this sensor are all in the horizontal plane. The imaging and discrimination capability of TDEM sensors could be significantly enhanced by recording measurements in receiver coils oriented in three orthogonal directions, and in many different time gates. However, effective use of these measurements would depend on the availability of an appropriate forward and inverse theory with which to interpret them. It would also be desirable to develop a formal and rational basis for designing an instrument that would provide the maximum diagnostic power for broad classes of targets. Finally, the formal inversion approach i sadequate for post-processing of data, but complementary to this, it would be desirable to develop and implement an inference technique that is field robust and can operate in real-time or near realtime. Thus, there are three major problem areas that we consider in this paper: (a) model based imaging capability i.e. the forward and inverse problem, @) detector modeling (including instrument response) and instrument design, and (c) target recognition and discrimination algorithms. We describe a theoretical approximation developed by Habashy et al. (1993) that can be used to compute rapidly and accurately secondary fields for targets with large conductivity contrasts. This work can provide a basis for the forward and inverse theory required to address problem area (a). For problem area (b) we describe-a formalism that can be used to improve the design of -*