Maysam Abedi; Kiomars Mosazadeh; Hamid Dehghani; Ahmad MadanchiZare
Abstract
We have applied an automatic interpretation method of potential data called AN-EUL in unexploded ordnance (UXO) prospective which is indeed a combination of the analytic signal and the Euler deconvolution approaches. The method can be applied for both magnetic and gravity data as well for gradient surveys ...
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We have applied an automatic interpretation method of potential data called AN-EUL in unexploded ordnance (UXO) prospective which is indeed a combination of the analytic signal and the Euler deconvolution approaches. The method can be applied for both magnetic and gravity data as well for gradient surveys based upon the concept of the structural index (SI) of a potential anomaly which is related to the geometry of the anomaly sources. With AN-EUL method, both the depth and the approximate geometry (or SI) of the causative sources can be deduced. A realistic model for UXO to be simulated by a simple shape body is a prolate spheroid. The method is applied for both synthetic potential data assuming a collection of causative UXO sources replicating various sizes placed at different depths. In both cases, the estimated depth and the SI of the synthetic UXOs approximately correspond to the synthetic model parameters. The location detection of the causative sources is based upon the Blakely automatic picking algorithm. For both data sets, since the anomaly responses of the small UXOs are affected by noise, the estimated SI is a bit disturbed but the locations correspond to the real ones. The Blakely algorithm also identifies weak anomalies that are due to noise in data; thus, a post-processing of the estimated SI of the automatically detected sources may be needed to prevent false alarm sources in UXO exploration. Two field data sets have been provided to demonstrate the capability of the applied methods in UXO detection.
Maysam Abedi; Kiomars Mosazadeh; Hamid Dehghani; Ahmad MadanchiZare
Abstract
This paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detection of unexploded ordnance (UXO). The magnetic anomalies arising from multi-source UXO can overlap at a height over the ground surface, while causative sources may not be readily ...
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This paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detection of unexploded ordnance (UXO). The magnetic anomalies arising from multi-source UXO can overlap at a height over the ground surface, while causative sources may not be readily separated due to low level of signal-to-noise ratio of the observed data. To effectively the magnetic method work in the cleanup stage of contaminated area with UXO, the magnetic anomalies of UXO sources should be enhanced in order to separate the locations of different sources. The stable downward continuation of magnetic data can increase the signal-to-noise ratio which subsequently causes the separation of UXO sources by enhancing the signals. We formulate the downward continuation as a linear ill-posed deconvolution problem in this study. To obtain a reasonable downward continued field, it is stabilized in a Fourier domain to regularize the downward solution using the edge-preserved (or total-variation) algorithm. The L-curve method is used to choose the optimum value of the regularization parameter which is a trade-off between the misfit and the solution norms in the cost function of optimization problem. A synthetic magnetic field is constructed from isolated multi-source UXO anomalies, whose results show that the data can be stably downward continued by the proposed method. Likewise, a field data set has been provided to demonstrate the capability of the applied method in UXO detection.
