Sonification of geophysical data through time–frequency analysis: theory and applications

In this paper, we introduce a new method of geophysical data interpretation based on simultaneous analysis of images and sounds. The final objective is to expand the interpretation workflow through multimodal (visual–audio) perception of the same information. We show how seismic data can be effectively converted into standard formats commonly used in digital music. This conversion of geophysical data into the musical domain can be done by applying appropriate time–frequency transforms. Using real data, we demonstrate that the Stockwell transform provides a very accurate and reliable conversion. Once converted into musical files, geophysical datasets can be played and interpreted by using modern computer music tools, such as sequencers. This approach is complementary and not substitutive of interpretation methods based on imaging. It can be applied not only to seismic data but also to well logs and any type of geophysical time/depth series. To show the practical implications of our integrated visual–audio method of interpretation, we discuss an application to a real seismic dataset in correspondence of an important hydrocarbon discovery.     

地震映像数据的时频分析方法及应用

本文研究地震映像数据的时频解释方法.采用短时傅立叶变换方法获得地震映像记录频谱的时间与空间分布,根据介质对地震波频谱影响的基本规律,通过分析已知地质断面地震映像记录频谱的时间与空间分布,研究了综合利用频率域和时间域信息进行地震映像数据解释的效果.时频分析方法提取了地震波的频谱中关于地层岩性、构造方面的信息,为地震映像数据的处理和解释提供了更多的参考信息.实例证明,利用时频分析解释地震映像数据,有助于了解覆盖层下岩性变化、薄层的分布范围、探测隐伏土洞、确定混凝土构件中缺陷位置,提高地震映像数据的解释精度和准确性.     

Identification and classification of multiple reflections with self-organizing maps

Artificial neural networks can be used effectively to identify and classify multiple events in a seismic data set. We use a specialized neural network, a self-organizing map (SOM), that tries to establish rules for the characterization of the physical problem. Selected seismic data attributes from CMP gathers are used as input patterns, such that the SOM arranges the data to form clusters in an abstract space. We show with synthetic and real data how the SOM can identify and classify primaries and multiples, and how it can classify the various types of multiple corresponding to a certain generating mechanism in the subsurface.     

Characterization of ground motions using recurrence plots

The current analysis of earthquakes is typically based on linear mathematical models that may fail to describe and forecast particular behaviors, because in many cases the data complexity may induce a highly non linear behavior. In this paper the implementation of an alternative method for seismic time series analysis is presented. The RPs (Recurrence Plots) enables recognition and treatment of measured accelerations. An RP obtained from seismic data allows a more efficient interpretation of the ground motions and this explanation contributes to characterize materials and responses. The nonlinear attributes from RPs analysis can be used as filters to reveal patterns or be combined to predict a seismic property. Automated seismic data characterization, based on nonlinear seismic attributes, could rewrite the rules of earthquake phenomena interpretation. The objective of this work is to establish a new methodology for practical application of nonlinear dynamics in seismic pattern/attributes recognition, an evolving and challenging engineering field.     

重力与地震资料的模拟退火约束联合反演

联合反演是综合地球物理研究的重要定量解释手段.本文在总结和分析重力与地震资料联合反演的研究现状基础上,利用改进的全局寻优的快速模拟退火算法,实现了重力和地震资料的约束同步联合反演.针对性地设计了密度和速度界面不完全一致的模型,理论模型的试验说明了方法的效果和适用性.结合最近完成的广东徐闻地区实际资料的处理和解释,表明该方法可准确确定复杂构造物性界面的密度和速度结构,在该地区的油气勘探中发挥了作用.在先验信息约束下,该联合反演方法要明显优于单独的重力反演.     

基于改进型BP网络的空间电场分类研究

空间电场信号异常识别是研究地震引起电离层扰动的重要内容。 将空间超低频电场电位数据看作随机数字信号, 以均值、 均方差、 偏度和峰度等四个指标进行描述, 采用“5·12”汶川大地震前空间超低频电场电位数据作为原始数据, 训练改进型BP神经网络, 建立了空间电场信号异常分类识别模型, 并以SOM神经网络进行验证。 计算结果显示, 空间超低频电场电位异常信号主要集中在5°~25°N, 88°~120°E之间的区域, 汶川大地震影响范围内的电离层扰动, 可能是汶川地震发生前引起的, 这与前人研究一致, 说明采用改进型BP神经网络异常分类识别模型研究地震引起的电离层扰动是可行的。     

Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self‐organizing map

During typhoons or storms, accurate forecasts of hourly streamflow are necessary for flood warning and mitigation. However, hourly streamflow is difficult to forecast because of the complex physical process and the high variability in time. Furthermore, under the global warming scenario, events with extreme streamflow may occur that leads to more difficulties in forecasting streamflows. Hence, to obtain more accurate hourly streamflow forecasts, an improved streamflow forecasting model is proposed in this paper. The computational kernel of the proposed model is developed on the basis of support vector machine (SVM). Additionally, self‐organizing map (SOM) is used to analyse observed data to extract data with specific properties, which are capable of providing valuable information for streamflow forecasting. After reprocessing, these extracted data and the observed data are used to construct the SVM‐based model. An application is conducted to clearly demonstrate the advantage of the proposed model. The comparison between the proposed model and the conventional SVM model, which is constructed without SOM, is performed. The results indicate that the proposed model is better performed than the conventional SVM model. Moreover, as regards the extreme events, the result shows that the proposed model reduces the forecasting error, especially the error of peak streamflow. It is confirmed that because of the use of data extracted by SOM, the improved forecasting performance is obtained. The proposed model, which can produce accurate forecasts, is expected to be useful to support flood warning systems. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ⁰) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ⁰ and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.     

A hybrid neural network model for typhoon-rainfall forecasting

A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach.     

地质雷达精细处理与解释

地质雷达探测技术已在基础建设工程中得到广泛应用。在研究地质雷达高精度特性时,不能脱离弱异常的精细处理解释。本文探讨了:(1)地质雷达噪声源;(2)雷达资料的精细处理和解释,重点讨论在处理解释中如何达到“精细”的过程。     

AUTOMATIC INTERPRETATION OF GRAVITY GRADIOMETRIC DATA IN TWO DIMENSIONS: VERTICAL GRADIENT1

The magnetic and gravity field produced by a given homogeneous source are related through Poisson's equation. Starting from this consideration, it is shown that some 2D interpretation tools, widely applied in the analysis of aeromagnetic data, can also be used for the interpretation of gravity gradiometric data (vertical gradient). This paper deals specifically with the Werner deconvolution, analytic signal and Euler's equation methods. After a short outline of the mathematical development, synthesized examples have been used to discuss the efficiency and limits of these interpretation methods. These tools could be applied directly to airborne gravity gradiometric data as well as ground gravity surveys after transformation of the Bouguer anomalies into vertical gradient anomalies. An example is given of the application of the Werner deconvolution and Euler's equation methods to a microgravity survey.     

基于SOM和PSO的非监督地震相分析技术

地震相分析技术是储层预测的一种重要方法,可以用来描述有利沉积相带的分布规律.传统的地震相聚类分析方法对大数据的处理运算速度较慢,且容易陷入局部极小值,造成聚类分析的结构不准确.本文提出基于自组织神经网络(SOM)和粒子群优化方法(PSO)相结合的地震相分析技术,利用自组织神经网络能够保持原始地震数据的拓扑结构特性的特点,将大量冗余样本压缩为小样本数据,再通过粒子群的全局寻优能力改善K均值聚类的效果.理论模型和实际应用表明该方法能既有效实现数据压缩,又能提供较为准确的全局解,在地震相预测中兼顾计算效率和计算精度.     

Self-Organizing Maps for identification of zeolitic diagenesis patterns in closed hydrologic systems on the Earth and its implications for Mars

We have conducted a survey of zeolite occurrences in saline-alkaline paleolake deposits on Earth to identify the most prominent zeolite alteration patterns and to characterize the most common authigenic minerals and their paragenetic relationships. We collected the bulk mineral assemblages (from previous and our studies) as identified by X-ray diffraction from zeolitic tuff beds and associated sedimentary beds from thirteen paleolake deposits from the USA, Mexico, Greece, and Tanzania. We applied the Kohonen Self-Organizing Maps (SOM) to look for interesting patterns in the tuff bed mineral assemblages without prescribing any specific interpretation, and for information reduction and classification. Decision Tree (DT) method was applied to characterize these clusters. We were able to define clear class boundaries between fresh glass, non-analcime zeolites, analcime, and K feldspar. The non-analcime zeolites were further grouped into several classes based on mineral type. We also discuss the potential implications for Mars, showing that the mineral assemblages of diagenetic facies identified by SOM and DT can be used to test or validate the orbital, in situ, or modeling results, while the trained SOM provides a robust generalized ability to classify the new mineral assemblage data into the most common diagenetic facies identified in saline-alkaline paleoenvironments that contain zeolites. The study concludes that generalizing the complex geochemical behaviors using unsupervised statistical learning methods can help to identify the most prominent geochemical behaviors.     

自然伽马测井曲线的分析与反褶积校正

本文探讨系统响应函数对自然伽马测井曲线所造成的影响及反褶积校正的方法,从而使受围岩影响而降低了幅值的薄层曲线恢复原来的异常,改善了厚层曲线在界面处的陡度,以利于分层定厚、地层对比和进一步作各种定量的数字解释。     

VLF SURVEY OF THE WEATHERED LAYER IN SOUTHERN INDIA*

The electrical properties of the weathered layer were investigated by means of Geonics VLF-EM 16/16R equipment in two areas of the Andhra Pradesh State. The resistivity and thickness of the weathered layer were found to be variable even over a small survey area. In the area underlain by Precambrian granite-gneiss, most of the recorded VLF-EM anomalies were caused by variations in the resistivity of the weathered layer. Changes in thickness were well reflected in the VLF-EMR curves. The second area was underlain by Cretaceous basalts and dolerites. Quantitative interpretation of the VLF-EMR data with a simple one-dimensional model yielded considerable detail about the weathered layer. For the granitic area, a prior estimate of at least one resistivity parameter of the ground is required. If this is not already available, a limited amount of direct-current resistivity surveying can provide the required information. A study of the EMR data from the basaltic area revealed the presence of a thin, highly conductive layer between the weathered layer and the bedrock. The parameters of this layer were found to be variable, making it necessary to use a set of diagrams for quantitative interpretation. Due to the presence of this highly conductive layer, the EMR data contain little information on the bedrock resistivity. Our field studies suggest that the VLF-EMR method can be used as a fast and inexpensive tool for mapping of the weathered layer in tropical regions with hard rock geology. Such mapping is of considerable importance because the weathered layer is an important source of groundwater.     

On the interpretation of paleomagnetic data on moraines

Some methods of the interpretation and application of paleomagnetic data for the stratigraphic separation and correlation of Neopleistocene moraines, as well as the factors affecting the degree of ordering of magnetic moments of ferromagnetic particles in a moraine, are considered. A refined interpretation of the well-known Ryabushkin-Pevzner model of moraine magnetization is proposed. It is shown that the way of using an occasional coincidence of the direction of long axes of fragments with the direction of magnetization vectors, which is stated in some publications, has no grounds for its application for the stratigraphic separation of moraines related to the Dnieper and Moscow glaciations. However, it cannot be ruled out that this phenomenon may be used for the investigation of processes of glacial lithogenesis.     

波前重建法折射成像及应用研究

根据实际工作需要对传统的地震折射资料解释方法的适用范围进行了讨论,指出了传统折射资料解释方法所存在的问题。采用重建波前的方法进行折射成像,通过改进震源函数,并在反演过程中使用有限差分技术解程函方程,进行波场外推,从根本上解决了传统折射资料解释方法存在的问题,计算精度高,速度快。通过理论模型和实际资料的对比计算和验证,效果良好。     

活动断裂调查中的高分辨率遥感技术应用方法研究

本文系统分析了高分辨率遥感在活动断裂调查中应用的技术现状、工作流程,梳理了各类遥感数据的要求、适用条件和处理方法,总结了活动断裂的遥感解译方法、解译要素和测量参数,并通过实例解析了一些典型的断错地貌,给出了相应的遥感特征. 基于资源三号卫星的立体像对和影像,判读了大青山活动断裂的几何特征和活动特性. 结果表明: 人工改造较大的地区宜收集早期遥感影像,利用不同波段间地物光谱的差异来增强隐伏活动断裂的信息,使用空间增强方法来识别断层陡坎等线性构造;雷达数据多极化分解是检测隐伏构造信息的有效方法;由宏观信息向局部信息追踪是活动断裂解译的有效途径;将遥感影像与数字高程模型(DEM)联合可进行活动断层参数的高精度测量. 本文结果可为活动断裂大比例尺、定量调查提供参考.      

三维密度界面的正反演研究和应用

重力位场的界面反演是位场处理解释中的重要问题.本文将基于快速傅里叶变换的频率域界面反演方法Parker-Oldenburg公式推广到物性可随深度变化的三维情况,得出了密度可以横向、纵向任意变化的重力界面正反演公式.该方法在计算时可以合理地选取地面下某一深度作为基准面以减小界面起伏,使迭代易于收敛.理论模型试验表明该方法反演精度高,收敛速度快,在密度界面反演中具有广泛的实用价值.最后利用该方法反演华北地区莫霍面的深度,反演结果得到了地震测深数据的验证.