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1.
A fast inversion technique for the interpretation of data from resistivity tomography surveys has been developed for operation on a microcomputer. This technique is based on the smoothness-constrained least-squares method and it produces a two-dimensional subsurface model from the apparent resistivity pseudosection. In the first iteration, a homogeneous earth model is used as the starting model for which the apparent resistivity partial derivative values can be calculated analytically. For subsequent iterations, a quasi-Newton method is used to estimate the partial derivatives which reduces the computer time and memory space required by about eight and twelve times, respectively, compared to the conventional least-squares method. Tests with a variety of computer models and data from field surveys show that this technique is insensitive to random noise and converges rapidly. This technique takes about one minute to invert a single data set on an 80486DX microcomputer. 相似文献
2.
利用支持向量分类(SVC)估算断层深度和特征选择(英文) 总被引：1，自引：0，他引：1
Mohammad Ehsan Hekmatian Vahid E. Ardestani Mohammad Ali Riahi Ayyub Memar Koucheh Bagh Jalal Amini 《应用地球物理》2013,10(1):88-96
地下断层深度的估算是重力解释难题之一,我们试利用支持向量分类(SVC)法进行计算。使用正演和非线性反演技术,通过相关误错使检测地下断层深度成为可能。但必要有一个深度初始猜测值,而且这猜测值通常不是由重力资料得。本文我们介绍以SVC作为利用重力数据估算断层深度的一种手段。在这项研究中,我们假设一种地下断层深度可归为一种类型,SVC作为一个分类算法。为了有效地利用此SVC算法,我们基于一个正确的特征选择算法去选择正确的深度特征。本次研究中我们建立了一套基于不同深度地下断层的合成重力剖面训练集,用以训练用于计算实际的地下断层深度的SVC代码。然后用其它合成重力剖面训练集测试我们训练的SVC代码,同时也用实际资料验证了我们的训练SVC代码。 相似文献
3.
A.T. Baokur 《Geophysical Prospecting》1994,42(2):141-149
A new definition of apparent resistivity for the presentation of magnetotelluric sounding data is proposed. The new definition is based on the frequency-normalized impedance function. Both the existing and proposed definitions of apparent resistivity are analysed theoretically and are compared using model curves computed for a 1D earth model. Apparent resistivity curves computed using the proposed definition are a better approximation to the true resistivity values of the subsurface layers. In addition, the layers are more noticeable on the apparent resistivity curves, which is an advantage, especially for the ascending and descending type of apparent resistivity curve. 相似文献
4.
Accurate forecasting of hydrological time‐series is a quite important issue for a wise and sustainable use of water resources. In this study, an adaptive neuro‐fuzzy inference system (ANFIS) approach is used to construct a time‐series forecasting system. In particular, the applicability of an ANFIS to the forecasting of the time‐series is investigated. To illustrate the applicability and capability of an ANFIS, the River Great Menderes, located in western Turkey, is chosen as a case study area. The advantage of this method is that it uses the input–output data sets. A total of 5844 daily data sets collected from 1985 to 2000 are used for the time‐series forecasting. Models having various input structures were constructed and the best structure was investigated. In addition, four various training/testing data sets were built by cross‐validation methods and the best data set was obtained. The performance of the ANFIS models in training and testing sets was compared with observations and also evaluated. In order to get an accurate and reliable comparison, the best‐fit model structure was also trained and tested by artificial neural networks and traditional time‐series analysis techniques and the results compared. The results indicate that the ANFIS can be applied successfully and provide high accuracy and reliability for time‐series modelling. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
5.
常规的可控源电磁法理论在计算视电阻率公式上,多半采用其电磁场的渐近特征,难以直接反映全区视电阻率的值,及直观地显现地下介质的地质构造.文中采用水平偶极子激发的电磁场,提出了电场的全区精确表达式,直接计算出大地电阻率.利用汉克尔数值滤波算法和逆样条插值算法对水平层状电磁场进行正演计算,并与计算的卡尼亚视电阻率的对比和野外试验结果表明：该方法的结果在远区等价卡尼亚电阻率,在近区和过渡带则明显地改善了卡尼亚电阻率的非波场区场畸变,从而能更好地接近基底的真电阻率,更形象地反映了地下介质的垂向电性变化. 相似文献
6.
Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported
in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect
seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features
as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289
signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are
almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of
Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event,
distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg
wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes
and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results
confirmed that the proposed ANFIS model has good potential for determining seismic events. 相似文献
7.
Mahmut Firat Mahmud Güngör 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(2):259-270
Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs.
In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting
system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with
various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS
models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS
models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR)
methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully
because it provides high accuracy and reliability for forecasting of monthly total sediment. 相似文献
8.
The temporal variation in a soil moisture profile can be studied using resistivity sounding data acquired at different times. The layered earth model based estimation of soil moisture from apparent resistivity data is a two-step non-linear inversion. Firstly, the apparent resistivity data are inverted to derive the layer resistivity variations and thicknesses and, secondly, the moisture content is estimated from these layer resistivity variations using a calibration equation. The soil moisture–resistivity problem was studied using the one-dimensional formulation of resistivity problem. A generalized geoelectric earth model was considered to simulate the soil moisture distribution and its temporal variation in the unsaturated zone. An algorithm (RESMOS) for the interpretation of the apparent resistivity data in terms of soil moisture variations through this two-step inversion process is reported. 相似文献
9.
Victor Infante Luis A. Gallardo Juan C. Montalvo-Arrieta Ignacio Navarro de León 《Journal of Applied Geophysics》2010,70(2):93-102
In this paper, evidence is presented that the combination of geospectral images and geophysical signatures (resistivity–velocity cross-plots) is a good tool to provide a natural visualization of the distribution and variations of lithological features in a test site. This was confirmed by the correlation between the electrical resistivity and seismic velocity values obtained after cross-gradient joint inversion at two profiles and geotechnical information provided by shallow boreholes in a site located in the Earth Sciences School grounds in Linares, Northeastern Mexico. The results obtained from this study show how the cross-gradient joint inversion facilitates the analysis of hydrological estimates and assists in lithological classification of subsurface materials. 相似文献
10.
Comparative analysis of fuzzy inference systems for water consumption time series prediction 总被引：1，自引：0，他引：1
Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series. 相似文献
11.
Adaptive Neuro-Fuzzy Inference System for drought forecasting 总被引：3，自引：2，他引：1
Ulker Guner Bacanli Mahmut Firat Fatih Dikbas 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(8):1143-1154
Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability
of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized
Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey
are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For
all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided
into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12 months were trained
and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated.
Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate
that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting. 相似文献
12.
Zacharie Sirabahenda André St-Hilaire Simon C. Courtenay Ashley Alberto Michael R. Van Den Heuvel 《水文科学杂志》2017,62(13):2209-2221
A data-driven model based on an adaptive neuro-fuzzy inference system (ANFIS) was tested for the estimation of suspended sediment concentrations within watersheds influenced by agriculture. ANFIS models were developed using different combinations of inputs such as precipitation, streamflow, surface runoff and the watershed vulnerability index. A multi-watershed ANFIS model was also developed combining the datasets from all studied watersheds. The best results were obtained from a combination of precipitation, streamflow and watershed vulnerability index as input variables. Nash-Sutcliffe coefficients were improved for the multi-watershed ANFIS compared to watershed-specific ANFIS models. The introduction of the erosion vulnerability index significantly improved the ability of the ANFIS model to estimate suspended sediment concentrations within the watersheds. Furthermore, the inclusion of this index opens the possibility of using the ANFIS model to investigate the impact of land-use changes on sediment delivery. 相似文献
13.
The purpose of geophysical electrical surveys is to determine the subsurface resistivity distribution by making measurements on the ground surface. From these measurements, the true resistivity of the subsurface can be estimated. The ground resistivity is related to various geological parameters, such as the mineral and fluid content, porosity and degree of water saturation in the rock. Electrical resistivity surveys have been used for many decades in hydrogeological, mining and geotechnical investigations. More recently, they have been used for environmental surveys. To obtain a more accurate subsurface model than is possible with a simple 1-D model, a more complex model must be used. In a 2-D model, the resistivity values are allowed to vary in one horizontal direction (usually referred to as the x direction) but are assumed to be constant in the other horizontal (the y) direction. A more realistic model would be a fully 3-D model where the resistivity values are allowed to change in all three directions. In this research, a simulation of the cone penetration test and 2D imaging resistivity are used as tools to simulate the distribution of hydrocarbons in soil. 相似文献
14.
Bernhard Siemon Esben Auken Anders Vest Christiansen 《Journal of Applied Geophysics》2009,67(3):259-268
Helicopter-borne frequency-domain electromagnetic (HEM) surveys are used for fast high-resolution, three-dimensional resistivity mapping. Standard interpretation tools are often based on layered earth inversion procedures which, in general, explain the HEM data sufficiently. As a HEM system is moved while measuring, noise on the data is a common problem. Generally, noisy data will be smoothed prior to inversion using appropriate low-pass filters and consequently information may be lost.For the first time the laterally constrained inversion (LCI) technique has been applied to HEM data combined with the automatic generation of dynamic starting models. The latter is important because it takes the penetration depth of the electromagnetic fields, which can heavily vary in survey areas with different geological settings, into account. The LCI technique, which has been applied to diverse airborne and ground geophysical data sets, has proven to be able to improve the HEM inversion results of layered earth structures. Although single-site 1-D inversion is generally faster and — in case of strong lateral resistivity variations — more flexible, LCI produces resistivity — depth sections which are nearly identical to those derived from noise-free data.The LCI results are compared with standard single-site Marquardt–Levenberg inversion procedures on the basis of synthetic data as well as field data. The model chosen for the generation of synthetic data represents a layered earth structure having an inhomogeneous top layer in order to study the influence of shallow resistivity variations on the resolution of deep horizontal conductors in one-dimensional inversion results. The field data example comprises a wide resistivity range in a sedimentary as well as hard-rock environment.If a sufficient resistivity contrast between air and subsurface exists, the LCI technique is also very useful in correcting for incorrect system altitude measurements by using the altitude as a constrained inversion parameter. 相似文献
15.
Approximation to Cutoffs of Higher Modes of Rayleigh Waves for a Layered Earth Model 总被引：2，自引：0，他引：2
A cutoff defines the long-period termination of a Rayleigh-wave higher mode and, therefore is a key characteristic of higher
mode energy relationship to several material properties of the subsurface. Cutoffs have been used to estimate the shear-wave
velocity of an underlying half space of a layered earth model. In this study, we describe a method that replaces the multilayer
earth model with a single surface layer overlying the half-space model, accomplished by harmonic averaging of velocities and
arithmetic averaging of densities. Using numerical comparisons with theoretical models validates the single-layer approximation.
Accuracy of this single-layer approximation is best defined by values of the calculated error in the frequency and phase velocity
estimate at a cutoff. Our proposed method is intuitively explained using ray theory. Numerical results indicate that a cutoffs
frequency is controlled by the averaged elastic properties within the passing depth of Rayleigh waves and the shear-wave velocity
of the underlying half space. 相似文献
16.
It is proposed that the Straightforward Inversion Scheme (SIS) developed by the authors for 1D inversion of resistivity sounding and magneto-telluric sounding data can also be used in similar fashion for time-domain induced polarization sounding data. The necessary formulations based on dynamic dipole theory are presented. It is shown that by using induced polarization potential, measured at the instant when steady state current is switched off, an equation can be developed for apparent ‘chargeability–resistivity’ which is similar to the one for apparent resistivity. The two data sets of apparent resistivity and apparent chargeability–resistivity can be inverted in a combined manner, using SIS for a common uniform thickness layer earth model to estimate the respective subsurface distributions of resistivity and chargeability–resistivity. The quotient of the two profiles will give the sought after chargeability profile. A brief outline of SIS is provided for completeness. Three theoretical models are included to confirm the efficacy of SIS software by inverting only the synthetic resistivity sounding data. Then one synthetic data set based on a geological model and three field data sets (combination of resistivity and IP soundings) from diverse geological and geographical regions are included as validation of the proposal. It is hoped that the proposed scheme would complement the resistivity interpretation with special reference to shaly sand formations. 相似文献
17.
18.
Upendra K. Singh 《Journal of Applied Geophysics》2011,75(4):687-698
The analysis of well logging data plays key role in the exploration and development of hydrocarbon reservoirs. Various well log parameters such as porosity, gamma ray, density, transit time and resistivity, help in classification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular geological stratigraphy formation are function of its composition, physical properties that help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify the kind of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the kinds of stratigraphy from well logs over Prydz bay basin, East Antarctica using fuzzy inference system. A model is built based on few data sets of known stratigraphy and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. Initially the fuzzy based algorithm is trained, validated and tested on well log data and finally identifies the formation lithology of a hydrocarbon reservoir system of study area. The effectiveness of this technique is demonstrated by the analysis of the results for actual lithologs and coring data of ODP Leg 188. The fuzzy results show that the training performance equals to 82.95% while the prediction ability is 87.69%. The fuzzy results are very encouraging and the model is able to decipher even thin layer seams and other strata from geophysical logs. The result provides the significant sand formation of depth range 316.0- 341.0 m, where core recovery is incomplete. 相似文献
19.
We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is successful not only in enhancing the subsurface information but also as a survey design tool to identify the appropriate combination of the geophysical tools and show whether application of an individual method for further investigation of a specific site is beneficial. 相似文献
20.
Forecasting of space–time groundwater level is important for sparsely monitored regions. Time series analysis using soft computing tools is powerful in temporal data analysis. Classical geostatistical methods provide the best estimates of spatial data. In the present work a hybrid framework for space–time groundwater level forecasting is proposed by combining a soft computing tool and a geostatistical model. Three time series forecasting models: artificial neural network, least square support vector machine and genetic programming (GP), are individually combined with the geostatistical ordinary kriging model. The experimental variogram thus obtained fits a linear combination of a nugget effect model and a power model. The efficacy of the space–time models was decided on both visual interpretation (spatial maps) and calculated error statistics. It was found that the GP–kriging space–time model gave the most satisfactory results in terms of average absolute relative error, root mean square error, normalized mean bias error and normalized root mean square error. 相似文献