Kristofer Davis

Time-lapse gravity monitoring: A systematic 4D approach with application to aquifer storage and recovery

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.

Enhancement of Magnetic Data by Stable Downward Continuation for UXO Application

The magnetic method has been proven to be a successful geophysical tool for the detection of unexploded ordnance (UXO). Aeromagnetic surveys are advantageous since they can acquire data over large areas. The downside is that magnetic anomalies due to multiple metallic targets can overlap significantly due to flight height restrictions. Such overlap combined with the acquisition noise may significantly decrease the signal-to-noise ratio of data. These adverse effects can mask the true level of contamination at a site during the initial assessment based on the magnetic method as well as decrease the overall effectiveness of discrimination during the active clearance stage. We propose a method to ameliorate these difficulties using stable downward continuation, which reconstructs the field at a lower observation height from the observed data. The stable algorithm formulates the downward continuation as an inverse problem and incorporates the expected power spectrum of UXO anomalies. The power spectrum preserves the spectral properties and subdues the amplification of high-frequency noise. Synthetic and field examples show that the algorithm can reliably reconstruct the magnetic anomaly at the ground surface within the limitation imposed by the noise. The reconstructed field exhibits significant enhancement compared to the original data.

Automatic detection of UXO magnetic anomalies using extended Euler deconvolution

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.

Rapid gravity and gravity gradiometry terrain corrections via an adaptive quadtree mesh discretization

We present a method for modelling the terrain response of gravity gradiometry surveys utilising an adaptive quadtree mesh discretization. The data- and terrain-dependent method is tailored to provide rapid and accurate terrain corrections for draped and barometric airborne surveys. The surface used in the modelling of the terrain effect for each datum is discretized automatically to the largest cell size that will yield the desired accuracy, resulting in much faster modelling than full-resolution calculations. The largest cell sizes within the model occur in areas of minimal terrain variation and at large distances away from the datum location. We show synthetic and field examples for proof of concept. In the presented field example, the adaptive quadtree method reduces the computational cost by performing 351 times fewer calculations than the full model would require while retaining an accuracy of one Eötvös for the gradient data. The method is also used for the terrain correction of the gravity field and performed 310 times faster compared with a calculation of the full digital elevation model.

Survey design and model appraisal based on resolution analysis for 4D gravity monitoring

SUMMARY Time-lapse gravity surveys directly detect mass changes and offer unique means for monitoring the dynamics of the subsurface. As wider application of the method is emerging, survey design and model appraisal are key steps in developing a meaningful interpretation. We first examine what the optimal station spacing should be, in theory and in inversion-based simulations given a scale-length feature. We then examine the resolving power if the data spacing has been established in order to have an appropriate model mesh.

Effects of low-pass filtering on the calculated structural index from magnetic data

Euler and extended Euler deconvolution applications use an assumed structural index (SI) or calculate the SI, respectively, for magnetic anomaly data within a specified window. The structural index depends on the source type: specifically, the rate at which the field produced by the source decays. We have examined the effects that the application of low-pass filtering to magnetic data has on estimating the SI. Using a simple low-pass filter, we derived the SI for filtered-field solutions directly over, and away from, a target based on the magnetic potential of a vertical dipole SI ¼ 2 ðÞ . We validated this approach by applying extended Euler deconvolution to synthetic and field examples. In general, filtered magnetic data will decrease the numerically determined SI to a value lower than the theoretical one. The slope and cutoff wavelength of the filter directly affect the estimated SI solutions. The results prove that one must take into account filtering for the application of Euler deconvolution to locate dipole anomalies for unexploded ordnance detection.

Joint processing of total-field and gradient magnetic data

Abstract The processing of aeromagnetic data to account for levelling has been improved using gradient data. Utilising multiple magnetometers allows measurements of magnetic gradients and minimises the diurnal variation and other common-mode noise. We develop an equivalent source technique for jointly processing total-field and gradient data that makes use of a well known but rarely used relationship between the derivatives of the magnetic field and the derivative of its source to relate both datasets to a common equivalent source distribution. This approach treats the observed gradients as an additional and independent dataset instead of being just supplemental information. The direct result of joint processing is a set of enhanced data that incorporates information from both types of observed data as well as a higher signal-to-noise ratio. The methodology of the joint equivalent source processing technique is presented and demonstrated with a field example. Our method diminishes higher frequency noise, accentuates mid-frequency signals, and has higher resolution than that of total-field data alone.

Time-lapse gravity monitoring of an aquifer storage recovery project in Leyden, Colorado

‡Summary We present a case study on using time-lapse micro-gravity surveying to monitor an aquifer storage recovery project. An abandoned coal mine is being developed into an underground water reservoir in Leyden, Colorado. Excess water from surface sources is poured into the reservoir during winter and then retrieved for use in the summer. Efficient operation of the storage-recovery process requires knowledge of water concentration and movement within the mine shafts as well as well in the hosting geologic units. Three micro-gravity surveys were carried out to monitor the process over the course of ten months during the initial water injection stage. We will present the survey design, data acquisition, and preliminary results of the monitoring experiment.

3D Joint Inversion of Gradient And Total-field Magnetic Data

Recently, the processing of aeromagnetic data to account for leveling has developed into the use of gradients. Utilizing multiple magnetometers allows for the measurement of magnetic gradients and minimizes the diurnal variation and other common-mode noise. We extend the use of these data to quantitative interpretation through 3D inversion. We make use of a well-known relationship between the derivatives of the magnetic field and the derivative of its source to relate both data sets to a common source distribution. Our approach treats the observed gradients as an additional and independent dataset instead of being just supplemental information. These data are incorporated to spatially constrain the recovered model that will fit the total-field and gradient data. We present the methodology of the joint inversion technique and demonstrate it with a synthetic and field example.

Joint Processing of Total-field And Gradient Magnetic Data Using Equivalent Sources

The use of two to four magnetometers in airborne magnetic surveys is becoming more frequent, allowing for the observation of the total-field as well as its horizontal gradients (Redford, et al., 2006). There are advantages to these data types because of the increased and complementary information content in them. Yet little has been done to take full advantage of these data beyond simple enhancement of the total-field anomaly. As a first step, we have developed an equivalent source processing algorithm by making use of a well-known but little used relationship between the derivative of the total-field with the derivative of its source. Utilizing gradient and totalfield data allows for a constructed susceptibility model for total-field data, constrained by the observed gradients. In this paper, we discuss using gradients in order to construct a more representative dataset through equivalent source processing. The methodology of the joint equivalent source processing technique is presented and a synthetic example is shown.