We studied time-lapse gravity surveys applied to the monitoring of an artificial aquifer storage and recovery (ASR) system in Leyden, Colorado. An abandoned underground coal mine has been developed into a subsurface water reservoir. Water from surface sources is injected into the artificial aquifer during winter for retrieval and use in summer. As a key component in the geophysical monitoring of the artificial ASR system, three microgravity surveys were conducted over the course of ten months during the initial water-injection stage. The time-lapse microgravity surveys successfully detected the distribution of injected water as well as its general movement. Quantitative interpretation based on 3D inversions produced hydrologically meaningful density-contrast models and imaged major zones of water distribution. The site formed an ideal natural laboratory for investigating various aspects of time-lapse gravity methodology. Through this application, we have studied systematically all steps of the method, including survey design, data acquisition, processing, and quantitative interpretation.

We have developed an algorithm for the automatic detection of prospective unexploded ordnance (UXO) anomalies in total-field or gradient magnetic data based on the concept of the structural index (SI) of a magnetic anomaly. Identifying magnetic anomalies having specific structural indices enables the direct detection of potential UXO targets. The total magnetic field produced by a dipolelike source, such as a UXO, decays with inverse distance cubed and therefore has an SI of three, whereas the gradient data have an SI of four. The developed extended Euler deconvolution method based on the Hilbert transform provides a reliable means for calculating the spatial location, depth, and SI of compact and isolated anomalies; it has enabled us to perform automatic anomaly selection for further analysis. Our method first examines the anomaly decay and selects possible UXO anomalies based on the expected SI. We refine the result further by post-Euler amplitude analysis using the relative source strength of the anomalies selected in the first stage. The amplitude analysis statistically identifies weak anomalies that are due to noise in the data. This enhances the final result and eliminates automatic picks that fall within the noise level. We have demonstrated the effectiveness of the method using synthetic and field data sets.

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S U M M A R YMany geophysical inverse problems involve large and dense coefficient matrices that often exceed the limitations of physical memory in commonly available computers. The repeated multiplications of such matrices to vectors during processing or inversion require an immense amount of computing power. These two factors pose a significant challenge to solving largescale inverse problems in practice and can render many realistic problems intractable. To overcome these limitations, we develop a new computational approach for this class of problems by combining an adaptive quadtree or octree model discretization and wavelet transforms on reordered parameter sets. The adaptive mesh discretizes the model region according to the required resolutions based on localized anomalies. Hilbert space-filling curves and similar ordering of the reduced parameter set then enable a higher compression of the coefficient matrix by forming its sparse representation in the 1-D wavelet domain. This combination can reduce the storage requirement by 100 to 1000 times and, therefore, also speeds up the computation during the processing stage by the same factor. As a result, problems can now be solved that were computationally prohibitive. We present the algorithm and illustrate its effectiveness with an example from equivalent source construction in potential-field processing.

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