Electrical resistivity imaging of hydrologic flow through surface coal mine valley fills with comparison to other landforms

Surface coal mining has altered land cover, near‐surface geologic structure, and hydrologic processes of large areas in central Appalachia, USA. These alterations are associated with changes in water quality such as elevated total‐dissolved solids, which is usually measured via its surrogate, specific conductance (SC). The SC of valley fill effluent streams is a function of fill construction methods, materials, and age; yet hydrologic studies that relate these variables to water quality are sparse due to the difficulty of conducting traditional hydrologic studies in mined landscapes. We used electrical resistivity imaging (ERI) to visualize the subsurface geologic structure and hydrologic flow paths within a valley fill. ERI is a noninvasive geophysical technique that maps spatiotemporal changes in resistivity of the subsurface. We paired ERI with artificial rainfall experiments to track infiltrated water as it moved through the valley fill. Results indicate that ERI can be used to identify subsurface geologic structure and track advancing wetting fronts or preferential flow paths. Our results suggest that the upper portion of the fill contains significant fines, whereas the deeper profile is primarily large rocks and void spaces. Water tended to pond on the surface of compacted areas until it reached preferential flow paths, where it appeared to infiltrate quickly down to >15 m depth in 75 min. ERI applications can improve understanding of how fill construction techniques influence subsurface water movement, and in turn may aid in the development of valley fill construction methods to reduce water quality effects.     

Revisiting the abandoned shorelines of Lake George,Australia: a refined optical dating framework

Abandoned shorelines are an important archive used to constrain past fluctuations in the hydrological balance of lakes around the globe. Within Australia, the shorelines preserved at Lake George, NSW, form one of the few shoreline archives in the south-east of the continent that record palaeoenvironmental conditions throughout the late Quaternary. Here, we examined and tested the lake-level record for Lake George constructed in the 1970s by dating a well-preserved shoreline sequence at Luckdale, on the lake's eastern shore, using single-grain optically stimulated luminescence (OSL) dating. Ten stratigraphic units were identified, and these suggest a late Quaternary highstand for Lake George in MIS 3, with fluctuations superimposed upon an overall drying trend throughout MIS 2 and into the present. At Luckdale, the highest four shoreline-associated units were deposited ~13 to 19 m above lake base and date to between 39 ± 2 and 29 ± 1 ka ago. Our study pushes back the timing of maximum lake depth at Lake George to at least MIS 3, rather than MIS 2. The overall drying trend is supported by similar reductions in both Riverine Plain fluvial activity and other associated lake-level records from within the Murray basin.     

Intragrain oxygen isotope zoning in titanite by SIMS: Cooling rates and fluid infiltration along the Carthage‐Colton Mylonite Zone,Adirondack Mountains,NY, USA

Oxygen isotopes are an attractive target for zoning studies because of the ubiquity of oxygen‐bearing minerals and the dependence of mineral ¹⁸O/¹⁶O ratios on temperature and fluid composition. In this study, subtle intragrain oxygen isotope zoning in titanite is resolved at the 10‐μm scale by secondary ion mass spectrometry. The patterns of δ¹⁸O zoning differ depending on microstructural context of individual grains and reflect multiple processes, including diffusive oxygen exchange, partial recrystallization, grain‐size reduction, and grain growth. Using the chronological framework provided by structural relations, these processes can be related to specific events during the Grenville orogeny. Titanite was sampled from two outcrops within the Carthage‐Colton Mylonite Zone (CCMZ), a long‐lived shear zone that ultimately accommodated exhumation of the Adirondack Highlands from beneath the Adirondack Lowlands during the Ottawan phase (1090–1020 Ma) of the Grenville orogeny. Titanite is hosted in the Diana metasyenite complex, which preserves three sequentially developed fabrics: an early NW‐dipping protomylonitic fabric (S₁) is crosscut by near‐vertical ultramylonitic shear zones (S₂), which are locally reoriented by a NNW‐dipping mylonitic fabric (S₃). Texturally early titanite (pre‐S₂) shows diffusion‐dominated δ¹⁸O zoning that records cooling from peak Ottawan, granulite‐facies conditions. Numerical diffusion models in the program Fast Grain Boundary yield good fits to observed δ¹⁸O profiles for cooling rates of 50 ± 20 °C Ma^?1, which are considerably faster than the 1–5 °C Ma^?1 cooling rates previously inferred for the Adirondack Highlands from regional thermochronology. High cooling rates are consistent with an episode of rapid shearing and exhumation along the CCMZ during gravitational collapse of the Ottawan orogen at c. 1050 Ma. Texturally later titanite (syn‐S₂) has higher overall δ¹⁸O and shows a transition from diffusion‐dominated to recrystallization‐dominated δ¹⁸O zoning, indicating infiltration of elevated‐δ¹⁸O fluids along S₂ shear zones and continued shearing below the blocking temperature for oxygen (~≤500 °C for grain sizes at the study site). The texturally latest titanite (post‐S₃) has growth‐dominated δ¹⁸O zoning, consistent with porphyroblastic grain growth following cessation of shearing along the Harrisville segment of the CCMZ.     

Centennial scale benthic foraminiferal record of late Holocene oceanographic variability in Disko Bugt,West Greenland

A new centennial scale benthic foraminiferal record of late Holocene climate variability and oceanographic changes off West Greenland (Disko Bugt) highlights substantial subsurface water mass changes (e.g. temperature and salinity) of the West Greenland Current (WGC) over the past 3.6 ka BP. Benthic foraminifera reveal a long-term late Holocene cooling trend, which may be attributed to increased advection of cold, low-salinity water masses derived from the East Greenland Current (EGC). Cooling becomes most pronounced from c. 1.7 ka BP onwards. At this point the calcareous Atlantic benthic foraminiferal fauna decrease significantly and is replaced by an agglutinated Arctic fauna. Superimposed on this cooling trend, centennial scale variability in the WGC reveals a marked cold phase at c. 2.5 ka BP, which may correspond to the 2.7 ka BP cooling-event recorded in marine and terrestrial archives elsewhere in the North Atlantic region. A warm phase recognized at c. 1.8 ka BP is likely to correspond to the ‘Roman Warm Period’ and represents the warmest bottom water conditions. During the time period of the ‘Medieval Climate Anomaly’ we observe only a slight warming of the WGC. A progressively more dominant cold water contribution from the EGC on the WGC is documented by the prominent rise in abundance of agglutinated Arctic water species from 0.9 ka BP onwards. This cooling event culminates at c. 0.3 ka BP and represents the coldest episode of the ‘Little Ice Age’.Gradually increased influence of cold, low-salinity water masses derived from the EGC may be linked to enhanced advection of Polar and Arctic water by the EGC. These changes are possibly associated with a reported shift in the large-scale North Atlantic Oscillation atmospheric circulation pattern towards a more frequent negative North Atlantic Oscillation mode during the late Holocene.     

Phases greater than 90° in MT data: Analysis using dimensionality tools

An example of magnetotelluric data is discussed which has distinctive phase values out of quadrant, and for which the Z_xx element of the measured tensor is greater than the Z_xy element in both real and quadrature parts. Mohr circle plots and principal value decompositions of the real and quadrature parts of the tensor clarify its understanding. An analysis of rotational invariants of the data suggests a case of galvanic distortion of a 2D structure. Results from phase tensor analysis are included, and the example is seen to be a dramatic instance of phase tensor analysis reducing an apparently 3D example to 2D characteristics.The Mohr circles of this example do not capture the axes origin, although there are phases out of quadrant. Extra intrigue is thus added to the question, noted in Lilley, F.E.M., 1998. Magnetotelluric tensor decomposition: part I, theory for a basic procedure. Geophysics 63, 1885–1897 and arising independently in Caldwell, T.G., Bibby, H. M., Brown, C., 2004. The magnetotelluric phase tensor. Geophys. J. Int. 158, 457–469 and Bibby, H.M., Caldwell, T.G., Brown, C., 2005. Determinable and non-determinable parameters of galvanic distortion in magnetotellurics. Geophys. J. Int. 163, 915–930, whether the determinant values taken separately of the real and quadrature parts of a magnetotelluric tensor should both never be negative.For data whose Mohr circles do not capture the axes origin, simple conditions are derived regarding phase. These conditions govern whether or not it is formally possible for observed phases of Z_xy to exceed 90°, for any rotation of the observing axes.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.