Enhanced backscatter is studied in closed finite systems which have ballistic internal propagation and diffuse scattering at rough boundaries. Weak localization arguments indicate that the mean-square response at the source should be twice as large as it is at other points. It is argued, however, that the actual ratio is three. Numerical solutions are found to agree with a more detailed theory and to show that the enhanced return factor is two at moderate times, but three at late times comparable to the modal density.

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Field measurements and theoretical studies have been made of pressure surges-momentary variations in fluid pressure-produced by movement of pipe in mud-filled boreholes. Pressure measurements were recorded by five pressure gauges located at various positions in the borehole. An important positive pressure peak was found to occur as the casing moved with maximum velocity. Important negative peaks were found as the casing was lifted from the slips and as brakes were applied to stop pipe movement. A rigorously formulated theory has successfully predicted the sequence and magnitudes of these positive and negative surges and has established (l hasis for understanding how they occur.Both the measurements and theory indicate that the most important pressure surge is usually due to viscous drag of the flowing mud. The theory of viscous-drag pressure surges has been approximated by simplified graphs and calculation procedures to facilitate ready use in field operations. Comparison of measured results with those prediGted by the simplified theory shows that the magnitude of this surge can be predicted accurately.

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Macro-meteorological models predict optical turbulence as a function of weather data. Existing models often struggle to accurately predict the rapid fluctuations in C n 2 in near-maritime environments. Seven months of C n 2 field measurements were collected along an 890 m scintillometer link over the Severn River in Annapolis, Maryland. This time series was augmented with local meteorological measurements to capture bulk-atmospheric weather measurements. The prediction accuracy of existing macro-meteorological models was analyzed in a range of conditions. Next, machine-learning techniques were applied to train new macro-meteorological models using the measured C n 2 and measured environmental parameters. Finally, the C n 2 predictions generated by the existing macro-meteorological models and new machine-learning informed models were compared for four representative days from the data set. These new models, under most conditions, demonstrated a higher overall C n 2 prediction accuracy, and were better able to track optical turbulence. Further tuning and machine-learning architectural changes could further improve model performance.