A new least-squares minimization approach to depth and shape determination from magnetic data

We have developed a least‐squares minimization approach to depth determination using numerical second horizontal derivative anomalies obtained from magnetic data with filters of successive window lengths (graticule spacings). The problem of depth determination from second‐derivative magnetic anomalies has been transformed into finding a solution to a non‐linear equation of the form, f(z) = 0. Formulae have been derived for a sphere, a horizontal cylinder, a dike and a geological contact. Procedures are also formulated to estimate the magnetic angle and the amplitude coefficient. We have also developed a simple method to define simultaneously the shape (shape factor) and the depth of a buried structure from magnetic data. The method is based on computing the variance of depths determined from all second‐derivative anomaly profiles using the above method. The variance is considered a criterion for determining the correct shape and depth of the buried structure. When the correct shape factor is used, the variance of depths is less than the variances computed using incorrect shape factors. The method is applied to synthetic data with and without random errors, complicated regionals, and interference from neighbouring magnetic rocks. Finally, the method is tested on a field example from India. In all the cases examined, the depth and the shape parameters are found to be in good agreement with the actual parameters.     

A new technique to estimate initial spill size using a modified fay-type spreading formula

Estimating oil spill size is important for a variety of economic, environmental and legal reasons. One attempt to determine oil spill size by visually assessing the extent of colour regimes in the spill and multiplying the areas of these regimes by thickness values leads to unsatisfactory results. Previous efforts to estimate oil spill size by inverting spreading using formulae like those of Blokker and Fay have also incurred difficulties related to environmental conditions which influence spill spread rate. Data obtained during a series of field experiments, conducted off the Saudi Arabian coastline during the fall of 1982, were used to devise a modification of Fay's spreading formula. The results agree significantly better with the observed areas of the oil spill than Fay's original formula. The modified area formula is then inverted to obtain a formula for initial volume spilled.     

Synthetic magnetic anomaly curves for a two-dimensional model and their use as a new interpretation technique

Summary The author proposes a new technique of magnetic interpretation in the case of a two-dimensional model, whereby six analytical expressions are deduced and computed forDH, DZ, PHB, PZB, AHB andAZB. These expressions denote the induced part of magnetic anomaly inH component, the induced part of anomaly inZ component, the permanent magnetization part ofH anomaly, the permanent magnetization part ofZ anomaly, the sum (DH+PHB), and the sum (DZ+PZB), respectively. The use of a series of these six curves taken together instead of the single profile curve, will improve the existing method of magnetic interpretation, reduce the uncertainties of the inverse problem, and provide a valuable tool for paleomagnetic studies of in situ older rocks commonly found in equatorial Africa. The resulting advantages outweigh the apparent increase in computation. This technique was applied to a field profile obtained across a subsurface dolerite vein, and the results agree with the theoretical predictions outlined.     

A new approach for separating mixed model parameters: application to simultaneous inversion of earthquake source parameters

A method for simultaneous determination of mixed model parameters, which have different physical dimensions or different responses to data, is presented. Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system, the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the trade-offs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history, source geometry, and hypocentral location from regional seismograms, although it is applicable to any geophysical inversion.     

A new approach for separating mixed model parameters:application to simultaneous inversion of earthquake source parameters

A method for simultaneous determination of mixed model parameters,which have different physical dimensions or different responses to data,is presented.Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system,the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the tradeoffs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history,source geometry,and hypocentral location from regional seismograms,although it is applicable to any geophysical inversion.     

Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian-based integrated approach

The traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian-based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non-snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis-Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods.     

A new approach for computing a flood vulnerability index using cluster analysis

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes.FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores.The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified.The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.     

A neurocomputing approach to predict monsoon rainfall in monthly scale using SST anomaly as a predictor

A relationship between summer monsoon rainfall and sea surface temperature anomalies was investigated with the aim of predicting the monthly scale rainfall during the summer monsoon period over a section (80°–90°E, 14°–24°N) of eastern India that depends heavily upon the rainfall during the summer monsoon months for its agricultural practices. The association between area-averaged rainfall of June over the study zone and global sea surface temperature (SST) anomalies for the period 1982–2008 was examined and the variability of rainfall in monthly scale was calculated. With a view to significant variability in the rainfall in the monthly scale, it was decided to implement the artificial neural network (ANN) for forecasting the monthly scale rainfall using the SST anomalies as a predictor. Finally, the potential of ANN in this prediction has been assessed.     

A predictive model for well loss using fuzzy logic approach

Simple methods for calculating well losses are important for well design and optimization of groundwater source operation. Well losses arise from both laminar flow within the aquifer and turbulent flow within the well, and are often ignored in theoretical aquifer test analysis. The Jacob ( 1947 ) and Rorabaugh ( 1953 ) techniques for predicting well losses are widely used in the literature; however, inherent in these techniques are the assumptions of linearity, normality and homoscedascity. In the Rorabaugh technique, prior knowledge, or prediction of, the parameters A, C and n is required for calculation of well losses. Unfortunately, as of yet, no method for adequately obtaining these parameters without experimental data and linear regression exist. For these reasons, the Rorabaugh methodology has some practical and realistic limitations. In this paper, a fuzzy logic approach is employed in the calculation of well losses. An advantage of the fuzzy logic approach is that it does not make any assumptions about the form of the well loss functionality and does not require initial estimates for the calculation of well losses. Results show that the fuzzy model is a practical alternative to the Rorabaugh technique, producing lower errors (mean absolute error, mean square error and root mean square error) relative to observed data, for the case presented, comparatively to the Rorabaugh model. Copyright © 2010 John Wiley & Sons, Ltd.     

Virtual Pole from Magnetic Anomaly (VPMA): A procedure to estimate the age of a rock from its magnetic anomaly only

Virtual Pole from Magnetic Anomaly (VPMA) is a new multi-disciplinary methodology that estimates the age of a source rock from its magnetic anomaly, taken directly from available aeromagnetic data. The idea is to use those anomalies in which a strong remanent magnetic component is likely to occur. Once the total magnetization of the anomaly is computed through any of the currently available methods, the line that connects all virtual paleogeographic poles is related with the position, on a paleogeographic projection, of the appropriate age fragment of the APWP curve. We applied this procedure to five (5) well-known magnetic anomalies of the South American plate in SE Brazil, all of them associated to alkaline complexes of Mesozoic age. The apparent ages obtained from VPMA on three of the anomalies where the radiometric age of the source rock is known – Tapira, Araxá and Juquiá – were inside the error interval of the published ages. The VPMA apparent ages of the other two, where the age of the source rock is not known (Registro and Pariqueraçu magnetic anomalies) were geologically coherent. We expect that the application of the VPMA methodology as a reconnaissance geochronological tool may contribute to geological knowledge over continental areas, especially when the source rocks of the magnetic anomalies are unknown or buried below superficial sediments.     

A minimalistic approach for evapotranspiration estimation using the Prophet model

ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R² > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R² > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.     

A parameter allocation approach for flow simulation using the WetSpa-Python model

Hydrologic models are simplified representations of natural hydrologic systems. Since these models rely on assumptions and simplifications to capture some aspects of hydrological processes, calibration of parameters is unavoidable. However, utilizing the philosophy of a recent modelling framework proposed by Bahremand (2016), we show how calibration of most model parameters can be avoided by allocating or presetting these parameters utilizing knowledge gained from sensitivity analyses, field observations and a priori specifications as a part of a parameter allocation procedure. This paper details the simulation of daily river flow of the Shemshak-Roudak watershed performed using the Python version of the WetSpa model. The WetSpa-Python model is a distributed model of hydrological processes applied at the watershed scale. The model was applied to the Shemshak-Roudak watershed of Iran with parameter allocation. Model calibration involved only two parameters. Straightforward methods were proposed for allocating model parameters, including three baseflow-related parameters and the determination of maximum active groundwater storage using a mass curve technique. Also, the Budyko curve was used to constrain a correction factor for potential evapotranspiration. The WetSpa-Python model was extended to include the influence of snowmelt. A failure to include snow in the hydrological processes of the WetSpa-Python model creates a significant discrepancy between the observed and simulated hydrographs during the spring. The results of daily simulations for 12 years (2002–2014) are in good agreement with observations of discharge (Kling-Gupta Efficiency = 0.84). These results demonstrate that it is feasible to simulate hydrographs with limited calibration given a knowledge of hydrological processes and an understanding of relationships between catchment characteristics and model parameters.     

A new unbiased plotting position formula for Gumbel distribution

The probability plots (graphical approach) are used to fit the probability distribution to given series, to identify the outliers and to assess goodness of fit. The graphical approach requires probability of exceedence or non exceedence of various events. This is obtained through the use of plotting position formula. In literature many plotting position formulae have been reported. All of the many existing formulae provide different results particularly at the tails of the distribution and hence there is need of unbiased plotting position formulae for different distributions. Expression for the largest expected order statistics is found in a simple form. Using exact plotting position from Gumbel order statistics a new unbiased plotting position formula has been developed for the Gumbel distribution. The developed formula better approximates the exact plotting positions as compared to other existing formulae.     

A new fluid factor and its application using a deep learning approach

Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore-filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale–sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction.     

Soil anomaly mapping using a caesium magnetometer: Limits in the low magnetic amplitude case

Caesium magnetometers are new tools for soil property mapping with a decimetric resolution [Mathé, V., Lévêque, F., 2003. High resolution magnetic survey for soil monitoring: detection of drainage and soil tillage effects. Earth and Planetary Science Letters 212 (1–2), 241–251]. However, when the magnetic anomalies are only a few nanoteslas (nT), the geologic and pedogenic signal must first be isolated from magnetic disturbances for this method to be useful. This paper investigates the instrumental artifacts and environmental disturbances to adapt the survey protocol to slightly magnetic soils.Among the possible instrumental sources of disturbances listed and quantified, the most significant are: 1) The battery effect upon sensors 2 m away (classic protocol, about ± 0.15 nT) while increasing this distance up to 10 m cancelled it; 2) The noise level of magnetometers and sensors, which, according to tests on two magnetometers and three sensors, rarely and randomly exceeds 0.1 nT, but seems to increase with the electronic component age.Among the environmental disturbances, temporal variations such as diurnal variation or fluctuations linked to the moving of metallic masses play a major role, although the pseudogradient or base-station methods have commonly cancelled them. The efficiency of the latter is strongly dependent on the source nature. However, the ground currents and electromagnetic fields propagating in soils cause more problems. As a first step to better understand such disturbance sources, uncommon magnetic signal variations supposedly due to electromagnetic wave conversions and likely linked to the railway traffic are presented.Based on previous results, an adapted protocol using one magnetometer and two caesium sensors (0.3 and 1.6 m above the surface) is proposed to increase the signal / noise ratio. At first, to maintain an accurate horizontal and vertical location of the sensors, the latter are affixed to a wooden handcart running on plastic rails. Rails adapt to micro-topography, thereby decreasing strongly the soil–sensors distance variations. Anomalies due to topography rarely exceed 0.1 nT. Finally, a method to remove diurnal variations from high-resolution magnetic maps is proposed. Parallel profiles performed successively are adjusted by a cross-profile. Assuming that the temporal variations during each profile are negligible (less than 0.05 nT), this technique, contrary to the pseudogradient, preserves both the decimetric and the metric anomalies (gain of more than 1 nT).     

扩孔螺栓连接型消能梁段的力学模型及参数研究

当前国家对建筑结构的抗震性能和震后功能恢复能力提出了更高要求。基于短剪切型消能梁段的受剪屈服特性和剪切扩孔型螺栓连接的受剪滑移性能,提出一种新型扩孔螺栓连接型消能梁段,可有效增大消能梁段的延性和耗能能力并同时减小消能梁段的损伤,使带扩孔螺栓连接型消能梁段的新型Y形偏心支撑结构更好地适应当前要求。采用有限元方法详细分析扩孔螺栓连接型消能梁段的滞回性能、破坏模式和耗能机理,由此得到其骨架曲线和力学模型,并阐述其力学模型的影响参数,为相应偏心支撑结构的设计和分析提供理论依据。