Gravity gradiometry resurfaces

Twelve years ago, reading a passage from the submarine novel The Hunt for Red October by Tom Clancy was as dose as any exploration geophysicist got to gravity gradiometry. This early technique in Gulf Coast exploration, which faded from use with the development of modern gravity instrumentation in the 1930s, had been relegated to brief historical sections of introductory texts. In the 1970s, driven by both navigation and missile launching requirements, the U.S. Navy spent hundreds of millions of dollars developing a system to measure gravity gradients; this system was somewhat more complex than the fictional one Clancy installed on the Red October. The end of the Cold War triggered the introduction of classified military technology to exploration geophysics and other fields. Three years ago the U.S. Navy began to explore civilian applications for submarine gravity gradient technology. This article describes gravity gradients, the developing uses of gravity gradiometry in exploration, and future possibilities for the technique

Evidence for excess pore pressures in southwest Indian Ocean sediments

Brown clay cores from the Madagascar and Crozet basins show the following evidence of excess pore pressures: large amounts of flow-in, increasing average sedimentation rate with age, and nonlinear temperature gradients. Additionally, many hilltops in these basins have no visible sediment cover. The bare hilltops may result from periodic slumping caused by excess pore pressures. Calculated excess pore pressures which equal or exceed the overburden pressure were inferred from water fluxes predicted by nonlinear temperature gradients and laboratory permeability measurements by using Darcy's law. Since pore pressures which exceed the overburden pressure are unreasonable, we attribute this discrepancy to laboratory measurements which underestimate the in situ permeability. The widespread presence of overpressured sediments in areas of irregular topography provides a process for resuspension of clay-sized particles. This mechanism does not require high current velocities for the erosion of clay and therefore can be applied to many areas where no strong currents are evident. Carbonate-rich sediments from the Madagascar Ridge, the Mozambique Ridge, and the Agulhas Plateau had almost no flow-in and occurred in areas where all topography was thickly draped with sediment. Since the age and tectonic location of the ridges and plateaus preclude water circulation in the basement, we attribute these differences between the brown clay and the carbonate-rich material to an absence of significant excess pore pressures in the plateau and ridge sediments

Anomalous heat flow in the northwest Atlantic: A case for continued hydrothermal circulation in 80-M.Y. crust

A detailed study of a 60×150 km area at 60°W, 24°N at the eastern end of the Nares Abyssal Plain indicates that hydrothermal circulation is still active in the 80 m.y. B.P. oceanic crust. The 58 heat flow measurements made at five stations in the area have revealed (1) constant heat flow over the abyssal plain (56 mW m−2), (2) a cyclic heat flow over the abyssal hills (mean of 77 mW m−2), and (3) a large anomaly of 710 m W m−2 over one of several small domes which protrude from the abyssal plain. The domes are 0.5–1.0 km in diameter near the top and rise 50 m above the level of the abyssal plain. They are recognized from surface echo sounders by an abrupt disappearance in the abyssal plain subbottom reflectors, but on near-bottom pinger records they appear as steep-walled structures which are covered by ∼10 m of sediment (compared to ∼75 m on the surrounding abyssal hills). From analogy with active ridge crests, these features are probably small volcanoes. The heat flow anomaly over one of the domes is matched well by a finite element convection model with the following characteristics: (1) recharge at one basement outcrop and discharge at another, (2) 300 m of sediment fill between outcrops, and (3) permeabilities of 10−10 cm2 for basalt and 10−13 cm2 for sediment. In other words, we believe that there is very effective convective heat transfer within the crust and out of the relatively permeable, thinly sedimented basement dome, resulting in the local high heat flow. Overall, the results from the Nares survey vividly show the age independent muting effect of sediment on the surface manifestation of crustal convection. In our survey area the mode of heat transfer varies from purely conductive in the more thickly sedimented abyssal plain areas (∼300 m sediment cover) to moderate amplitude convection pattern beneath the abyssal hills (∼75 m sediment cover) to a very large thermal anomaly over the small dome or ‘chimneylike’ structure (∼10 m sediment cover). The domes are possibly active analogues to the presently inactive basement chimney drilled at DSDP site 417A

Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression

As our society gains a better understanding of how humans have negatively impacted the environment, research related to reducing carbon emissions and overall energy consumption has become increasingly important. One of the simplest ways to reduce energy usage is by making current buildings less wasteful. By improving energy efficiency, this method of lowering our carbon footprint is particularly worthwhile because it reduces energy costs of operating the building, unlike many environmental initiatives that require large monetary investments. In order to improve the efficiency of the heating, ventilation, and air conditioning (HVAC) system of a Manhattan skyscraper, 345 Park Avenue, a predictive computer model was designed to forecast the amount of energy the building will consume. This model uses Support Vector Machine Regression (SVMR), a method that builds a regression based purely on historical data of the building, requiring no knowledge of its size, heating and cooling methods, or any other physical properties. SVMR employs time-delay coordinates as a representation of the past to create the feature vectors for SVM training. This pure dependence on historical data makes the model very easily applicable to different types of buildings with few model adjustments. The SVM regression model was built to predict a week of future energy usage based on past energy, temperature, and dew point temperature data

Correlated sediment thickness, temperature gradient and excess pore pressure in a marine fault block basin

Measurements of temperature gradient and excess pore pressure in the surficial sediment of a fault block basin in the Guatemala Basin correlate with sediment thickness. The temperature gradient is smaller and the excess pore pressure gradient is more negative in areas of thinner sediment. This correlation is explained by postulating downward pore water advection within the sediments, with flow velocities on the order of 10−9 to 10−8 m/s in the thinnest sediments and much less flow in the thickest sediments. Sediment physical properties and pore water chemistry also support this interpretation. Since the conductive heat flow of the basin as a whole is less than one third that predicted by sea floor spreading models, the oceanic basement may be the site of a vigorous hydrothermal circulation system. The pore water advection in the sediments may be driven by this larger scale circulation

Elemental and mineralogical analyses using geochemical logs from the Cajon Pass Scientific Drillhole, California, and their preliminary comparison with core analyses

Estimates of elemental and mineralogical abundances from geochemical logs are compared to preliminary chemical and modal analyses from cores in the Cajon Pass Scientific Drillhole. Accuracies of log-computed weight percent oxide and mineralogy determinations range from 10 to 30%

Estimation of System Reliability Using a Semiparametric Model

An important problem in reliability engineering is to predict the failure rate, that is, the frequency with which an engineered system or component fails. This paper presents a new method of estimating failure rate using a semiparametric model with Gaussian process smoothing. The method is able to provide accurate estimation based on historical data and it does not make strong a priori assumptions of failure rate pattern (e.g., constant or monotonic). Our experiments of applying this method in power system failure data compared with other models show its efficacy and accuracy. This method can be used in estimating reliability for many other systems, such as software systems or components

Improving Efficiency and Reliability of Building Systems Using Machine Learning and Automated Online Evaluation

A high percentage of newly-constructed commercial office buildings experience energy consumption that exceeds specifications and system failures after being put into use. This problem is even worse for older buildings. We present a new approach, 'predictive building energy optimization', which uses machine learning (ML) and automated online evaluation of historical and real-time building data to improve efficiency and reliability of building operations without requiring large amounts of additional capital investment. Our ML approach uses a predictive model to generate accurate energy demand forecasts and automated analyses that can guide optimization of building operations. In parallel, an automated online evaluation system monitors efficiency at multiple stages in the system workflow and provides building operators with continuous feedback. We implemented a prototype of this application in a large commercial building in Manhattan. Our predictive machine learning model applies Support Vector Regression (SVR) to the building's historical energy use and temperature and wet-bulb humidity data from the building's interior and exterior in order to model performance for each day. This predictive model closely approximates actual energy usage values, with some seasonal and occupant-specific variability, and the dependence of the data on day-of-the-week makes the model easily applicable to different types of buildings with minimal adjustment. In parallel, an automated online evaluator monitors the building's internal and external conditions, control actions and the results of those actions. Intelligent real-time data quality analysis components quickly detect anomalies and automatically transmit feedback to building management, who can then take necessary preventive or corrective actions. Our experiments show that this evaluator is responsive and effective in further ensuring reliable and energyefficient operation of building systems

Geochemical Logging in the Cajon Pass Drill Hole and Its Application to a New, Oxide, Igneous Rock Classification Scheme

A new elemental oxide classification scheme for crystalline rocks is developed and applied to geochemical well logs from the Cajon Pass drill hole. This classification scheme takes advantage of measurements of elements taken by a geochemical logging tool string. It uses K_2O versus SiO_2/Al_2O_3 to distinguish between granites, granodiorites, tonalites, syenites, monzonites, diorites, and gabbros. Oxide measurements from cores are used to calibrate the elemental abundances determined from the well logs. From these logs, a detailed lithologic column of the core is generated. The lithologic column derived from the well log classification scheme is compared with a lithologic column constructed from core samples and well cuttings. In the upper 1295 m of the well, agreement between the two columns is good. Discrepancies occur from 1295 to 2073 m and are believed to be caused by the occurrence of rock types not distinguished by the classification scheme and/or the occurrence of secondary minerals. Despite these discrepancies, the well log-based classification scheme helps to distinguish changes in rock type and shows potential as an aid to the construction of lithologic columns in boreholes of crystalline rocks