Optimization of Flue Gas Composition to Maximize Base Gas Replacement During UGS Process

Natural Resources Research - Base gas replacement by a cheap gas is one of the approved methods to reduce the cost of investment in underground gas storage process. Maximizing the amount of...     

Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.     

Laboratory Study of Topographic Effects on the Near-surface Tornado Flow Field

To study topographic effects on the near-surface tornado flow field, the Iowa State University tornado simulator was used to simulate a translating tornado passing over three different two-dimensional topographies: a ridge, an escarpment and a valley. The effect of the translation speed on maximum horizontal wind speeds is observed for translation speeds of 0.15 and 0.50 \(\hbox {m}\,\hbox {s}^{-1}\), with the lower value resulting in a larger maximum horizontal wind speed. The tornado translation over the three topographies with respect to flat terrain is assessed for changes in: (a) the maximum horizontal wind speeds in terms of the flow-amplification factor; (b) the maximum aerodynamic drag in terms of the tornado speed-up ratio; (c) the maximum duration of exposure at any location to high wind speeds of a specific range in terms of the exposure amplification factor. Results show that both the maximum wind amplification factor of 14%, as well as the maximum speed-up ratio of 14%, occur on the ridge. For all topographies, the increase in aerodynamic drag is observed to be maximized for low-rise buildings, which illustrates the importance of the vertical profiles of the horizontal wind speed near the ground. The maximum exposure amplification factors, estimated for the range of wind speeds corresponding to the EF2 (50–60 \(\hbox {m}\,\hbox {s}^{-1}\)) and EF3 (61–75 \(\hbox {m}\,\hbox {s}^{-1})\) scales, are 86 and 110% for the ridge, 4 and 60% for the escarpment and ? 6 and 47% for the valley, respectively.     

Spatial correlation for horizontal and vertical components of acceleration from northern Iran seismic events

The evaluation of seismic risk of spatially distributed systems requires the spatial correlation model for ground motion intensity measures. This study investigates the spatial correlation of four earthquakes recorded in northern Iran. The intra-event spatial correlation for both horizontal and vertical components of spectral acceleration at eight periods in the range of 0.0–3.0 s is estimated using geostatistical tools. An exponential form is chosen to fit experimental semivariograms, and the correlation ranges of spectral accelerations as a function of period are derived. The results show similar trend of correlation ranges for both components. It should be mentioned that the ranges for the vertical component, in general, are higher than those observed for the horizontal one. For both components, the correlation ranges as a function of period are divided into three segments. The first and the third one are increasing while the second one is decreasing with increasing period.     

A general weighted total Kalman filter algorithm with numerical evaluation

An applicable algorithm for Total Kalman Filter (TKF) approach is proposed. Meanwhile, we extend it to the case in which we can consider arbitrary weight matrixes for the observation vector, the random design matrix and possible correlation between them. Also the updated dispersion matrix of the predicted unknown is given. This approach makes use of condition equations and straightforward variance propagation rules. It is applicable to data fusion within a dynamic errors-in-variables (DEIV) model, which usually appears in the determination of the position and attitude of mobile sensors. Then, we apply for the first time the TKF algorithm and its extended version named WTKF to a DEIV model and compare the results. The results show the efficiency of the proposed WTKF algorithm. In particular in the case of large weights, WTKF shows approximately 25% improvement in contrast to TKF approach.     

Strong ground motion from the November 12, 2017, <Emphasis Type="Bold">M</Emphasis> 7.3 Kermanshah earthquake in western Iran

In this paper, we analyzed the strong ground motion from the November 12, 2017, Kermanshah earthquake in western Iran with moment magnitude (M) of 7.3. Nonlinear and linear amplification of ground motion amplitudes were observed at stations with soft soil condition at hypocentral distances below and above 100 km, respectively. Observation of large ground motion amplitudes dominated with long-period pulses on the strike-normal component of the velocity time series suggests a right-lateral component of movement and propagation of rupture towards southeast. Comparison of the horizontal peak ground acceleration (PGA) from the M 7.3 earthquake with global PGA values showed a similar decay in ground motion amplitudes, although it seems that PGA from the M 7.3 Kermanshah earthquake is higher than global values for NEHRP site class B. We also found that the bracketed duration (D_b) was higher in the velocity domain than in the acceleration domain for the same modified Mercalli intensity (MMI) threshold. For example, D_b reached ~?30 s at the maximum PGA while it was ~?50 s at the maximum peak ground velocity above the threshold of MMI?=?5. Although the standard design spectrum from Iranian Code of Practice for Seismic Resistant Design of Buildings (standard No. 2800) seems to include appropriate values for the design of structures with fundamental period of 1 s and higher, it is underestimated for near-field ground motions at lower periods.     

A bias-free geodetic boundary value problem approach to height datum unification

A geodetic boundary value problem (GBVP) approach has been formulated which can be used for solving the problem of height datum unification. The developed technique is applied to a test area in Southwest Finland with approximate size of 1.5° × 3° and the bias of the corresponding local height datum (local geoid) with respect to the geoid is computed. For this purpose the bias-free potential difference and gravity difference observations of the test area are used and the offset (bias) of the height datum, i.e., Finnish Height Datum 2000 (N2000) fixed to Normaal Amsterdams Peil (NAP) as origin point, with respect to the geoid is computed. The results of this computation show that potential of the origin point of N2000, i.e., NAP, is (62636857.68 ± 0.5) (m²/s²) and as such is (0.191 ± 0.003) (m) under the geoid defined by W ₀ = 62636855.8 (m²/s²). As the validity test of our methodology, the test area is divided into two parts and the corresponding potential difference and gravity difference observations are introduced into our GBVP separately and the bias of height datums of the two parts are computed with respect to the geoid. Obtaining approximately the same bias values for the height datums of the two parts being part of one height datum with one origin point proves the validity of our approach. Besides, the latter test shows the capability of our methodology for patch-wise application.     

Potential of legacy 2D seismic data for deep targeting and structural imaging at the Neves–Corvo massive sulphide-bearing deposit,Portugal

Seismic methods are becoming an established choice for deep mineral exploration after being extensively tested and employed for the past two decades. To investigate whether the early European mineral-exploration datasets had potential for seismic imaging that was overlooked, we recovered a low-fold legacy seismic dataset from the Neves–Corvo mine site in the Iberian Pyrite Belt in southern Portugal. This dataset comprises six 4–6 km long profiles acquired in 1996 for deep targeting. Using today's industry-scale processing algorithms, the world-class, ca. 150 Mt, Lombador massive sulphide and other smaller deposits were better imaged. Additionally, we also reveal a number of shallow but steeply dipping reflections that were absent in the original processing results. This study highlights that legacy seismic data are valuable and should be revisited regularly to take advantage of new processing algorithms and the experiences gained from processing such data in hard-rock environments elsewhere. Remembering that an initial processing job in hard rock should always aim to first obtain an overall image of the subsurface and make reflections visible, and then subsequent goals of the workflow could be set to, for example understanding relative amplitude ratios. The imaging of the known mineralization implies that this survey could likely have been among one of the pioneer studies in the world that demonstrated the capability of directly imaging massive sulphide deposits using the seismic method.     

Estimation of groundwater storage from seismic data using deep learning

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic–elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.