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.     

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.     

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.     

Artificial neural network coupled with wavelet transform for estimating snow water equivalent using passive microwave data

Snow Water Equivalent (SWE) is an important parameter in hydrologic engineering involving the streamflow forecasting of high-elevation watersheds. In this paper, the application of classic Artificial Neural Network model (ANN) and a hybrid model combining the wavelet and ANN (WANN) is investigated in estimating the value of SWE in a mountainous basin. In addition, k-fold cross validation method is used in order to achieve a more reliable and robust model. In this regard, microwave images acquired from Spectral Sensor Microwave Imager (SSM/I) are used to estimate the SWE of Tehran sub-basins during 1992–2008 period. Also for obtaining measured SWE within the corresponding Equal-Area Scalable Earth-Grid (EASE-Grid) cell of SSM/I image, approach of Cell-SWE extraction using height–SWE relations is applied in order to reach more precise estimations. The obtained results reveal that the wavelet-ANN model significantly increases the accuracy of estimations, mainly because of using multi-scale time series as the ANN inputs. The Nash–Sutcliffe Index (NSE) for ANN and WANN models are respectively 0.09 and 0.44 which shows a firm improvement of 0.35 in NSE parameter when WANN is applied. Similar trend is observed in other parameters including RMSE where the value is 0.3 for ANN and 0.07 for WANN.     

Automatic lithofacies segmentation using the wavelet transform modulus maxima lines combined with the detrended fluctuation analysis

The main goal of this paper is to design and develop a new technique and software tool that help automatic lithofacies segmentation from well logs data. Lithofacies is a crucial problem in reservoir characterization and our study intends to prove that techniques like wavelet transform modulus maxima lines (WTMM) and detrended fluctuation analysis (DFA) approaches allow geological lithology segmentation from well logs data. On the one hand, WTMM prove to be useful for delimitation of each layer. We based on its sensitivity on the presence of more than one texture. On the other hand, DFA is used to enhance the estimation of the roughness coefficient of each facies. We have used them jointly to segment the lithofacies of boreholes located in the Algerian Sahara. Obtained results are encouraging to publish this method because the principal benefit is economic.     

基于小波变换的地震信号分时分频相关去噪

介绍了Donoho的小波域阈值去噪处理方法,提出了对小波变换尺度上小波系数进行分时分频相关处理去噪后,再重构小波系数的方法,以去除大部分随机噪声。然后,再对重构后地震剖面进行小波域阈值去噪处理。结果表明,使用上述新方法可以有效改善地震剖面处理效果,提高信噪比。     

基于自组织映射神经网络的水库洪水过程分类

针对传统洪水分类方法中洪水特征提取时存在信息损失和主观性强的问题,本文基于洪水全过程构建自组织映射神经网络(Self-Organizing Map, SOM),综合考虑代表性和拓扑性等评价指标以优选网络规模,实现洪水全过程的拓扑逻辑关系挖掘及分类。以三峡水库洪水过程为研究对象,研究结果表明：(1) 2×3维SOM覆盖率达到56.7%,与3×3维SOM相比,仅有约2%的覆盖率差距,具有良好代表性;2×3维SOM输出层仅有1处翻转,拓扑结构比3×3维SOM更优,更适合三峡水库洪水过程分类。(2) 2×3维SOM将洪水过程划分为6类,其神经元拓扑结构可有效刻画各分类的差异与联系,说明SOM可基于可视化拓扑逻辑关系实现高维洪水数据的可靠客观分类。(3)与传统方法的历史典型洪水分类结果相比,SOM能提供可靠且丰富的分类信息。     

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.     

Structural boundaries delimitation from geomagnetic data using the continuous wavelet transform. Application to Hoggar (Algeria)

The main goal of the proposed work is to delineate structural boundaries in a very complex geology environment using the spatial and statistical properties of the potential field data. The analysis is performed using magnetic anomaly of the total field data over In Ouzzal, an Archaean north–south elongated block belonging to the Hoggar (Algeria). This region is geologically and geophysically very poorly known except some localized areas. The intrinsic properties of high-frequency signals and the related causative sources are explored, thanks to two-dimensional continuous wavelet transform. The obtained results, represented by spatial distribution of the maxima of the modulus of the wavelet transform at each scale, clearly show that the major magnetic singularities of the field may be related to geological features. Comparison with the Euler’s deconvolution solutions exhibits a very good correlation. Even though where geological structures are known, our method shows better resolution and accuracy. The proposed multiscale method proves to be more powerful, easy to use, and versatile where classical methods of potential field interpretation fail or are very constraining. However, work is still ongoing to try to better and fully characterize the causative sources of the potential fields.     

Geomorphometric analysis based on discrete wavelet transform

This work presents a geomorphometric approach for outlining anomalies of the topographic surface that may be related to geological structures or to geomorphological phenomena. It is based on 2D discrete wavelet transform of digital elevation models. This transform is used to extract singularities of a series of data. This is specifically applied to a digital elevation model, in order to get its detail coefficients and to have evidence about their variations and values. In particular, this approach can be helpful for the delineation and identification of landforms singularities, like landslides and geological structures. The potential and effectiveness of this approach is shown by an application to a case study about a large deep-seated landslide, located at the central-south front of the Apennine in South Italy.     

Seismic random noise attenuation using artificial neural network and wavelet packet analysis

In this paper, we present a method for attenuating background random noise and enhancing resolution of seismic data, which takes advantage of semi-automatic training of feed forward back propagation (FFBP) artificial neural network (ANN) in a multiscale domain obtained from wavelet packet analysis (WPA). The images of approximations and details of the input seismic sections are calculated and utilized to train neural network to model coherent events by an automatic algorithm. After the modeling of coherent events, the remainder data are assumed to be related to background random noise. The proposed method is applied on both synthetic and real seismic data. The results are compared with that of the adaptive Wiener filter (AWF) in synthetic shot gather and real common midpoint gather and also with that of band-pass filtering on real common offset gather. The comparison indicates substantially higher efficiency of the proposed method in attenuating random noise and enhancing seismic signals.     

基于小波变换与神经网络的石羊河流域夏季地温预测模型研究

地温变化在气候反馈效应中起着重要作用, 理解地温及其与影响因素之间的时空关系对预测全球温度变化至关重要。利用1998 - 2017年石羊河流域的逐日常规气象观测资料, 采用小波分析结合BP（Back Propagation）神经网络构建了石羊河流域夏季地温预报模型, 结果表明： 日平均地温预测效果在不同站点均为最佳, 其中预测值和观测值的相关系数均大于0.87, 3 ℃以内的预测概率均大于84%。其中, 民勤地区地温预测效果最好, 预测值和观测值的相关系数达到0.91, 3 ℃以内的预测概率达到86%。日最高地温的预测值与观测值的相关系数高于0.8, 但误差平方和、 标准差较大。永昌地区日最高地温的模拟效果最好, 3 ℃以内的预测概率达到83%。日最低地温的预测与观测值的平均相关系数高于0.66, 3 ℃以内的预报概率高于83%, 但预测值略低。其中, 武威地区日最低地温的预测效果最好, 预测值与观测值的相关系数为0.72, 3 ℃以内的预测概率达到94%。研究成果可为有效弥补干旱、 半干旱区地温观测资料缺失和探讨其与局地气候的关系提供一些参考。     

Utilization of self-organizing map and fuzzy clustering for site characterization using piezocone data

In this paper soft computing techniques, self-organizing maps and fuzzy clustering techniques have been proposed to isolate different layers in stratified soil based on available cone penetration test results. The results have been compared with that obtained from cone classification chart, hierarchical and K-mean clustering techniques. It was observed that variation in result with self-organizing map (SOM) and fuzzy clustering for isolating soil layers is marginal. These techniques are found to be efficient compared to hierarchical clustering technique. The results of K-mean clustering show that the identified soil strata are similar to that obtained from cone classification chart, SOM and fuzzy clustering.     

Heterogeneities analysis using the generalized fractal dimensions and the continuous wavelet transform. Application to the KTB boreholes

The main goal of this paper is to identify heterogeneities from well logs data using the wavelet-based multifractal analysis. Firstly, the wavelet transform modulus maxima lines method is applied with a moving window of 128 samples to the raw well logs data. After that, the generalized fractal dimensions that correspond to the three first moments of the function of partition are estimated. Application to synthetic and real well logs data of the main and pilot Kontinentales Tiefbohrprogramm de Bundesreplik Deutschland wells shows that the information and the correlation dimensions can be used for heterogeneities analysis and lithofacies segmentation form well logs data.     

Attenuation of multiple waves using the continuous wavelet transform (CWT)

This paper describes how the continuous wavelet transform is used to filter multiple waveforms in both time and frequency domains. It is well suited to process the stationary signals, and it shows the signal in both time and frequency scales. This new approach was tested first on synthetic data and then on real data. The results obtained on both cases were good. The method consists of identifying the multiples on which we apply a normal move out using the multiple velocity law. The multiples will be aligned and the primary reflections will not be aligned. This operation allows locating the multiples in the time-scale domain. We compute the continuous wavelet transform (CWT for short) in order to focus on the patterns relative to seismic events. To filter the multiples, we define a zone with frequency and time bounds. These bounds are deduced from the projection of the seismic trace. Then an automatic mask is applied to the pattern to be isolated. Filtering in time–frequency domain is done by keeping only the wavelet coefficients that are outside the mask and assigning zero to the coefficients larger than a threshold amplitude inside the defined zone. The mask shape does not matter, which is not the case in classical filtering, where both the window size and shape play a key role. The mask is defined from three parameters: time, frequency, and the wavelet coefficients. To go back to the time domain, one has to compute the wavelet transform inverse of the trace. This procedure is repeated for all traces. To reset the traces to their initial positions, we apply the dynamic correction inverse with the same velocity law as the multiples. It turns out that the attenuation of multiples by the CWT works fine, in particular, the two identified multiples were quasi eliminated (Fig. 10).     

基于自组织神经网络的污染场地多监测指标相关性分析

为查明场地污染分布特征,需对场地土壤和地下水进行钻探取样,按规范的检测指标进行逐一测试。在初查和详查阶段将获得大量的土壤和地下水污染数据,数据样本数量大、监测指标多,数据结构复杂,如何从场地大数据中提取价值信息已成为研究热点。以某有机污染场地为例,基于自组织映射神经网络（SOM）和K均值算法开展大数据分析,深入探讨地下水和土壤中各污染指标间的相关性。结果表明:（1）基于自组织映射神经网络的大数据分析可快速挖掘复杂多维的污染场地监测数据,有效完成关键信息的提取;（2）地下水中污染检出指标存在显著的聚类特征,同一聚类中的污染指标具备相似的空间分布特征。对场地污染物检测采取先分类后分级的优化筛选策略,减少污染物检测指标数目,从而有效降低场地检测费用;（3）土壤和地下水中污染检出指标存在良好的空间相关性,这与该污染场地地下水渗流速度缓慢有关。土壤和地下水污染检出指标空间分布的相关性,有助于场地污染源的追溯。     

Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method

Acta Geotechnica - The random finite element method has been widely used to evaluate slope uncertainty and reliability. To determine the probability of failure, the safety factor sampling often...     

基于小波变换的测井多尺度分层研究

测井信号是岩性、地层微观结构、含流体性等各种信息的总和,测井数据是反映地质体地质信息的载体,地层变化主要体现在测井信号的突变点或突变区域中。以某测井的自然伽马曲线为例,提出应用零通小波、模极大值对测井曲线多尺度分析,在不同尺度上定量划分出不同级别的地层界面,分析结果与实际地层界面一致。小波分析为地层的定量划分提供了一种新的思路。     

Sub-seasonal extreme rainfall prediction in the Kelani River basin of Sri Lanka by using self-organizing map classification

Natural Hazards - The availability of several multi-model and ensemble sub-seasonal forecasts online has generated a growing interest in extreme rainfall prediction and early warning. Developing...     

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.