基于Hyperion高光谱数据和随机森林方法的岩性分类与分析

探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。     

Multiscale analysis of noisy 3D GPR data using the directional continuous wavelet transform

Here, we present a new technique of noise effect attenuation in the 3D ground-penetrating radar (GPR) data analysis using the 2D directional continuous wavelet transform (DCWT). The proposed technique is based on the application of a Gaussian low pass filter to the modulus of the 2D DCWT for low scales. After application of the low pass filter, maxima of the CWT are mapped for all range of scales. Application to a noisy GPR data shows that the proposed idea can improve GPR data analysis by the continuous wavelet transform.     

利用ASTER数据反演陆面温度的研究进展

目前利用ASTER数据反演陆面温度的算法可划分为3类：ASTERTES（Temperature／Emissivity Separation）算法，多通道算法，辐射传输算法。对上述3类方法进行了分析比较，提出了今后工作中要：实现遥感的综合反演、地表发射率的精确获取以及反演实用价值更大的组分温度的努力方向。     

Aeromagnetic data analysis using the 2D directional continuous wavelet transform (DCWT)

In this paper, we have analyzed the geomagnetic anomaly field reduced to the pole by the 2D directional continuous wavelet transform (DCWT). The objective is to identify geological contacts. The proposed idea has been applied firstly at a synthetic model, the obtained results showed the robustness of the proposed technique, after that the synthetic data has been noised; analysis shows that contacts identified by DCWT are affected by noise. To resolve this problem, we have proposed an algorithm to reduce the noise effect in the contacts model. Application on the real geomagnetic data of In Ouzzal area located in the West of Hoggar (Algeria) shows clearly the robustness of the proposed method. Comparison with the analytic signal solutions exhibits a difference between the two models of contacts. Our method gives results that are accurate with the structural geological map.     

Improving resolution of gravity data with wavelet analysis and spectral method

Gravity data are the results of gravity force field interaction from all the underground sources. The objects of detection are always submerged in the background field, and thus one of the crucial problems for gravity data interpretation is how to improve the resolution of observed information. The wavelet transform operator has recently been introduced into the domain fields both as a filter and as a powerful source analysis tool. This paper studied the effects of improving resolution of gravity data with wavelet analysis and spectral method, and revealed the geometric characteristics of density heterogeneities described by simple shaped sources. First, the basic theory of the multiscale wavelet analysis and its lifting scheme and spectral method were introduced. With the experimental study on forward simulation of anomalies given by the superposition of six objects and measured data in Songliao plain, Northeast China, the shape, size and depth of the buried objects were estimated in the study. Also, the results were compared with those obtained by conventional techniques, which demonstrated that this method greatly improves the resolution of gravity anomalies. Translated from Progress in Geophysics, 2007, 22(1): 112–120 [译自: 地球物理学进展]     

小波分析在地震资料去噪中的应用

小波变换方法已广泛应用于信号处理领域。应用多尺度小波分析方法来消除地震观测信号中的噪声是一种行之有效的方法。这里从小波变换的基本原理出发,详细介绍了地震信号的阈值去噪原理,并根据模拟信号和实测地震信号的频谱分析,讨论了如何选择小波基及去噪过程中的阈值取值问题。从小波分解理论知道,利用多尺度分解方式对地震资料进行分析处理,相当于对实测地震资料进行不同尺度的细化分析,由于对不同地区、不同资料的精度要求不同,我们只要使用不同的尺度进行小波变换处理,就可以得到去除原信号的细部巨变(噪声干扰)特征的信号。同时,我们对小波变换处理后重构的地震信号与原信号进行了对比分析,误差结果分析表明该方法切实可行。我们还利用MATLAB语言及其小波工具箱,实现了对地震资料的去噪处理。     

小波分析在TEM资料处理中的应用

针对瞬变电磁信号频带宽、动态范围大、晚期信号弱的特点，常规的滤波方法难以取得好的效果，提出应用多尺度小波分析进行瞬变电磁信号的去噪处理。实测资料的数据处理结果表明，小波分析能有效地区分有用信号与干扰噪声，是一种瞬变电磁信号去噪的可行实用的方法。并详细阐述了瞬变电磁信号的特点及小波去噪的原理。     

机载雷达和辐射计数据反演植被覆盖区土壤水分的初步研究

用土壤水分试验(SMEX02)中获取的机载雷达和辐射计(PALS)数据进行反演土壤水分的研究。首先利用PALS数据中雷达和辐射计对土壤水分最敏感的波段,进行主被动结合的多元线性回归反演土壤水分。结果表明,这种方法并不能有效地提高反演精度;然后,改进了一个新提出的适用于大尺度星载C波段辐射计数据的土壤水分获取方法,提高其运算效率,并将该算法应用到PALS数据,一方面验证了该方法在机载高分辨率数据和非C波段下的适用性,另一方面对该方法进行了定量化评价。     

小波分析方法在航磁资料数据处理中的应用

航磁异常包含了地下各磁性体的叠加效应,为了更好地进行异常分离,尝试利用小波多尺度分析方法进行航磁异常的分离,其能够将异常分解到多个不同尺度上,来反映不同深度场源体产生的异常,具有较好的分离效果,值得进一步推广使用。     

Multiscale analysis of 3D GPR data using the 2D directional continuous wavelet transform (DCWT)

The main goal of this paper is to extract land topographical information by analyzing the 3D ground-penetrating radar (GPR) data by the 2D directional continuous wavelet transform method. The first objective is to identify direction of obstacles, and the second is to characterize each one with a special roughness coefficient. Application on real data shows that the proposed tool can enhance the GPR data interpretation.     

The improvement of the Morlet wavelet for multi-period analysis of climate data

The multi-level dynamics of an atmosphere system exhibits temporal structures in different types of climate data. This article addresses two issues in multi-period analysis of climate data. Firstly, the advantages of the modified Morlet wavelet transform (MMWT) for analyzing multi-period structure of time series over Morlet wavelet transform (MWT) are emphasized. Secondly, the multi-period issues of temperature data are studied with MMWT through four steps: the four dominant periods of 60 year temperature data are determined with the wavelet variance; by analyzing the real part of MMWT, the warm and cold stages of the temperature data at different scales are determined, and the time intervals of the warm and cold interchange are singled out; the amplitude of each periodic component is quantitatively characterized by the amplitude of wavelet coefficients; the most intensive oscillation time intervals are computed by the squared modulus of the MMWT (MMPS).     

小波分析在急倾斜煤层开采沉陷监测数据分析中的应用

本文针对急倾斜煤层开采沉陷地面监测工作的实际,针对监测数据分析中噪声太大,较难发现和提取微小沉降变形信息的现状,提出采用小波分析处理变形监测数据的方法,建立了开采沉陷变形模型。以某急倾斜煤层开采沉陷的监测实例,采用matlab小波分析实现了监测数据的处理,对开采沉陷区的发展趋势进行预测,为沉陷区地质灾害的评估和预测及沉陷区土地的规划利用奠定了坚实的基础。     

土壤水分同化系统的敏感性试验研究

利用1998年7月6日至8月9日青藏高原GAME-Tibet试验区MS3608站点的4cm、20cm和100cm的土壤水分观测数据同化SiB2模型输出的表层、根区和深层土壤水分,探讨了一个基于集合卡尔曼滤波和简单生物圈模型的单点土壤水分同化方案。分析和评价了集合大小、同化周期、模型误差、背景场误差以及观测误差对同化系统性能的影响。结果表明:①增加集合数目可以减小土壤水分同化系统的误差,但同时又降低了运行效率;②对于集合卡尔曼滤波,初始场的估计是否准确对同化系统性能影响不大;③模型误差和观测误差的准确估计可以提高土壤水分的估计精度;④利用数据同化的方法对土壤水分的估计有显著提高。     

Fractal analysis revisited by the continuous wavelet transform of AVO seismic data

The main goal of this paper is to establish reservoirs media heterogeneities by the wavelet transform modulus maxima lines. First, we gathered amplitude versus offsets (AVO) amplitudes at the top of the reservoirs, then we calculated the 2D wavelet transform after we calculated its maxima, and we estimated the Hölder exponent at each maxima. Variation of the Hölder exponent can give more information about lithology and fluid nature at any point. We applied the proposed idea at a 2D synthetic AVO intercept model, obtained results showed that the wavelet transform modulus maxima lines can be used as a seismic image processing tool. We suggest application of the proposed idea on real AVO seismic data and its attributes. It can give more ideas about reservoirs model.     

Random seismic noise attenuation data using the discrete and the continuous wavelet transforms

In this paper, we propose a technique of random noise attenuation from seismic data using discrete and continuous wavelet transforms. Firstly, the discrete wavelet transform (DWT) is applied to denoise seismic data using the threshold method. After, we calculate the continuous wavelet transform of the denoised seismic seismogram, the final denoised seismic seismogram is the continuous wavelet transform coefficients at the lower scale. Application to a synthetic seismogram shows the robustness of the proposed tool for random noise attenuation. Application to real vertical seismic profile recorded in Algeria clearly shows the efficiency of the proposed tool for random noise attenuation.     

Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network

The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Hölder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Hölder exponents of the data set 1 and the Hölder exponents of the radioactive elements concentrations; data set 5: the estimated Hölder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Hölder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data.     

小波分析在测井数据融合处理中的应用

受各种干扰和人为因素影响,单的地球物理测井信号曲线存在多解性和局限性。本文综合利用多条测井信号所反映的地层信息,应用小波分析方法,采用多尺度分析理论重构小波变换系数模极值,从而将多条测井曲线信号融合成条参数曲线。通过对融合后的参数曲线解译,增强了原始测井曲线中所包含的共有信息,削弱了单条测井信号中存在的偶然误差,使测井数据的可信度大为提高。阐述了数据融合处理的原理与具体方法,并结合工程实际分析了应用实效。该方法有助于更好地分析和解决实际工程问题。     

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can remotely measure the reflectance properties of the environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene. An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm. A pipeline for unsupervised mapping of spectra on a mine face is proposed which draws from several recent advances in the hyperspectral machine learning literature. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data. The pipeline is evaluated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale. The combined system is shown to produce a superior map to its constituent algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day.     

基于小波变换的阈值降噪方法在地震资料处理中的应用

这里主要讨论了小波变换阈值降噪方法的基本原理,并且将广泛使用的通用阈值方法与Birge-Massart阈值方法进行了比较,展现了Birge-Massart阈值选取方法的优越性。通过对模拟数据和实际地震资料的处理结果表明,基于小波变换的Birge-Massart阈值降噪方法对地震信号降噪具有很好的效果。