Structural Setting of the South-West Cameroon Using Satellite Potential Field Derived from SGG-UGM-2 Gravity Data

This study aims to improve knowledge of the structure of southwest Cameroon based on the analysis and interpretation of gravity data derived from the SGG-UGM-2 model. A residual anomaly map was first calculated from the Bouguer anomaly map, which is strongly affected by a regional gradient. The residual anomaly map generated provides information on the variation in subsurface density, but does not provide sufficient information, hence the interest in using filtering with the aim of highlighting the structures affecting the area of south-west Cameroon. Three interpretation methods were used: vertical gradient, horizontal gradient coupled with upward continuation and Euler deconvolution. The application of these treatments enabled us to map a large number of gravimetric lineaments materializing density discontinuities. These lineaments are organized along main preferential directions: NW-SE, NNE-SSW, ENE-WSW and secondary directions: NNW-SSE, NE-SW, NS and E-W. Euler solutions indicate depths of up to 7337 m. Thanks to the results of this research, significant information has been acquired, contributing to a deeper understanding of the structural composition of the study area. The resulting structural map vividly illustrates the major tectonic events that shaped the geological framework of the study area. It also serves as a guide for prospecting subsurface resources (water and hydrocarbons). .

A new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative for potential field data

Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.

Application of Image Enhancement Techniques to Potential Field Data

In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.

Combination of structure tensor and tilt angle in the edge detection of potential field data

Edge detection is a commonly requested task in the interpretation of potential field data. Different methods have different results for varied depths and shapes of geological bodies. In this paper,we propose using the combination of structure tensor and tilt angle to detect the edges of the sources,which can display the edges of shallow and deep bodies simultaneously. Through tests on synthetic potential field data,it is obvious that the proposed edge detection methods can display the sources edges more clearly and precisely,compared with other commonly used methods. The application on real potential field data shows similar result,obtaining the edges of layers and faults clearly. In addition,another advantage of the new method is its insensitivity to noise.

Nonlinear Inversion of Potential-Field Data Using an Improved Genetic Algorithm

The genetic algorithm is useful for solving an inversion of complex nonlinear geophysical equations. The multi-point search of the genetic algorithm makes it easier to find a globally optimal solution and avoid falling into a local extremum. The search efficiency of the genetic algorithm is a key to producing successful solutions in a huge multi-parameter model space. The encoding mechanism of the genetic algorithm affects the searching processes in the evolution. Not all genetic operations perform perfectly in a search under either a binary or decimal encoding system. As such, a standard genetic algorithm (SGA) is sometimes unable to resolve an optimization problem such as a simple geophysical inversion. With the binary encoding system the operation of the crossover may produce more new individuals. The decimal encoding system, on the other hand, makes the mutation generate more new genes. This paper discusses approaches of exploiting the search potentials of genetic operations with different encoding systems and presents a hybrid-encoding mechanism for the genetic algorithm. This is referred to as the hybrid-encoding genetic algorithm (HEGA). The method is based on the routine in which the mutation operation is executed in decimal code and other operations in binary code. HEGA guarantees the birth of better genes by mutation processing with a high probability, so that it is beneficial for resolving the inversions of complicated problems. Synthetic and real-world examples demonstrate the advantages of using HEGA in the inversion of potential-field data.

Advanced derivative ratio filters for edge detection of potential field data

Edge location is an important information of the source,and can be obtained by the potential field data. Most edge detection methods of potential field data are the functions of horizontal and vertical derivatives.The authors provide a new strategy to establish edge detection filters that can improve the resolution to identify small bodies,which use the ratio functions of different-order derivatives to recognize the edges of the sources.The new filter is named as advanced derivative ratio( ADR) filter and balanced outputs can be produced for different forms of ADR filters. The ADR filters are tested on synthetic data and real potential field data. The advantage of the ADR filters is that they can detect the edges of the causative sources more precisely and clearly,and the model testing results show that the resolution of ADR filters is higher than other existing filters. The ADR filters were applied to real data,with more subtle details obtained.

High-Precision Structural Map of Southeast Cameroon Using Phase Filters on Potential Gravimetric Data

The south-east of Cameroon encompasses a wide variety of geological structures among which we can cite the Congo Craton (CC), the Sanaga Fault (SF), the Yaoundé Domain, the Panafrican belt, the Protozoic series and the Dja complex. The presence of all these structures justifies the great tectonic activity to which this area was subject from the rupture of Pangea to the creation of the different plates that exist today. In this work, we will bring out a high-resolution structural map of the study area by applying the qualitative analysis of the phase filters on 200,900 points of gravimetric data obtained from the combination of the XGM2016 and ETOPO1 models. Then, with these same data, we will bring out another structural map with the maxima method called Multi-Scale Horizontal Derivative of Vertical Derivative (MSHDVD) which will be compared to the first in order to show the limits of the MSHDVD method. To do this, we will first use the extension method to highlight the map of residual anomalies, then a combination of derivative, gradient and phase filters to highlight the geological structures responsible for fracturing in this area. Phase filters have the advantage that they make it possible to highlight all the geological edges responsible for the fracturing without taking into account the depth, while the MSHDVD method highlights the existing geological contacts (edges) at depths well defined by the examiner. The structural map obtained with the MSHDVD method shows that the major structural direction in this zone is W-E while that obtained from the interpretation of the phase filters is more precise and shows that the major structural direction in this area would be N-S and this result would be in perfect agreement with the tectonics of East Cameroon.

Analyzing gravity anomaly variations before the 2016 Ms6.4 earthquake in Menyuan,Qinghai with an interpolation/cutting potential field separation technique

We evaluated 2011-2015 mobile relative gravity data from the Hexi monitoring network that covers the epicenter of the 2016 Menyuan Ms6.4 earthquake, Qinghai Province, China and examined the spatiotemporal characteristics of the gravity field at the focal depth. In addition, we assessed the regional gravity field and its variation the half-year before the earthquake. We use first different interpolation algorithms to build a grid for the gravity data and then introduce potential field interpolation-cutting separation techniques and adaptive noise filtering. The results suggest that the gravity filed at the focal depth of 11.12 km separated from the total gravity field at about -400-150 ×10^-8 m/s^2 in the second half of 2015, which is larger than that in the same period in 2011 to 2014 （±30×10^-8 m/s^2）. Moreover, at the same time, the gravity field changed fast from September 2014 to May 2015 and May 2015 to September 2015, reflecting to some extent material migration deep in the crust before the Menyuan earthquake.

Abnormality Degree Detection Method Using Negative Potential Field Group Detectors

Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the setting thresholds. Using these monitoring methods may cause serious false positive or false negative results. In order to precisely monitor the state of equipment, the problem of abnormality degree detection without fault sample is studied with a new detection method called negative potential field group detectors（NPFG-detectors）. This method achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods. In the process of Iris data set simulation, the new algorithm obtains the successful results in abnormal detection. The detection rates for 3 types of Iris data set respectively reach 100%, 91.6%, and 95.24% with 50% training samples. The problem of Bearing abnormality degree detection via an abnormality degree curve is successfully solved.

CONSERVATIVE ESTIMATING FUNCTION IN THE NONLINEAR REGRESSION MODEL WITH AGGREGATED DATA

The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.

中国南海海洋牧场的波浪能资源评估

利用欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts)近10年的ERA5再分析海浪场数据,为中国南海周边4个省(自治区)的国家级海洋牧场选取最适合开发波浪能资源的电站位置,重点研究各站点的波浪能开发潜力以及与波浪能装置布放方向有关的指标。研究表明:广东省惠州小星山以及福建省莆田市南日岛两个国家级海洋牧场所选站点的波浪能开发潜力最高,获得高经济收益的潜力也最强;此外,后者所选站点的波浪能传播方向最集中,最有利于装置吸收波浪能资源。

基于改进人工势场法的机织机器人避障路径规划

针对机织机器人自动化作业时的避障问题,提出基于改进人工势场法的三维避障路径规划算法。利用改进人工势场法中斥力势场函数,引入修正系数,在机织机器人陷入局部极小值点时增加虚拟障碍物,破坏其在虚拟力下的平衡状态,解决了人工势场法无法到达目标位置和局部极小值点的问题。通过体素化网格方法和快速凸包算法处理障碍物点云数据,重建实际障碍物模型,提高了碰撞检测效率。仿真结果表明,以点云数据重建障碍物模型并采用改进人工势场算法规划出的避障路径使机织机器人成功到达目标位置,末端位置精度平均提高37%,并避免陷入局部极小值点。

位场数据解释的Theta-Depth法

Theta图是利用位场(重磁)数据识别边界的常用方法,其表达式为重磁异常水平变化与垂直变化的比值函数.该方法计算浅源地质体边界的效果较好,而由于深源位场数据在换算过程中会产生趋同效应,在深源地质体识别应用中计算结果不准确,为此,本文提出Theta-Depth法并进行地质体埋深的计算.首先给出直接利用Theta图像进行场源体深度估算的方法,然后推导出基于Theta导数的线性方程来自动估算场源位置参数,本文方法可有效地利用Theta图像的特征为约束条件来提高反演结果的精度.理论模型试验证明本文提出的Theta-Depth法能有效地计算出场源体位置和深度.将该方法应用于满都拉地区实测磁数据的解释,帮助圈定了矿脉的分布.

基于深度学习模型的煤矿安全隐患数据主题挖掘

为了提高煤矿安全风险排查能力和监督能力,提出1种基于双向长短期记忆网络(BiLSTM)、条件随机场(CRF)和隐含狄利克雷分布(LDA)的模型。训练BiLSTM-CRF模型分词,采用困惑度-主题方差(perplexity-var)计算LDA模型最优主题数,构建BiLSTM-CRF-LDA模型挖掘内蒙古某煤矿安全隐患数据。研究结果表明:困惑度-主题方差指标能更准确地确定主题数;BiLSTM-CRF模型分词结果比jieba库更准确;BiLSTM-CRF-LDA模型能准确地挖掘出煤矿安全隐患类型、安全隐患空间分布和安全责任划分。研究结果可为煤矿安全风险排查与监督提供参考。

动态杂散电流干扰下管地电位的波动特征

在地铁、高铁和高压直流(HVDC)输电系统接地极双极运行引起的动态杂散电流干扰下,现场监测得到196组管地电位数据。在时域上分析了不同干扰下管地电位的动态波动特征,然后利用信号学上连续小波变换(CWT)的方法在频域上研究了不同干扰下管地电位的动态波动特性,统计了不同干扰下管地电位的周期分布范围,并对比了其周期分布的差异。结果表明:三种干扰源都对管道造成动态直流干扰,其中地铁和高铁干扰呈现白天干扰严重、夜间平稳的周期性变化,而HVDC干扰则与高压直流输电系统接地极入地电流的大小与方向有关,没有明显的昼夜差别;高铁交流干扰同时伴随着显著的直流干扰;地铁直流干扰的周期占比随周期增大呈现先增大后减小的趋势,因高铁线路不同,管道干扰的周期分布也有所不同,但主要分布在大于500s的范围,HVDC干扰引起的管地直流电位波动周期分布较为均匀。

基于数据场的改进LOF算法

LOF(Local Outlier Factor)是一种经典基于密度的局部离群点检测算法,为提高算法的精确度,以便更精准挖掘出局部离群点,在LOF算法的基础上,提出了一种基于数据场的改进LOF离群点检测算法。通过对数据集每一维的属性值应用数据场理论,计算势值,进而引入平均势差的概念,针对每一维度中大于平均势差的任意两点在计算距离时加入一个权值,从而提高离群点检测的精确度,实验结果表明该算法是可行的,并且拥有更高的精确度。

利用重磁资料研究北极地区扬马延微陆块中南部断裂构造与油气远景

为了研究北极地区扬马延微陆块中南部的断裂构造与油气远景,系统收集并整理了已有的重力及磁力资料,基于变纬度化极技术计算磁力化极异常,利用位场边缘识别技术获得相应的位场边缘识别信息,结合已有的地质及地球物理研究成果,对重磁异常及位场边缘识别信息进行深入分析,确定扬马延微陆块中南部断裂构造格架,圈定油气勘探有利区。研究认为,扬马延微陆块中南部重力高异常与重力低异常相间分布的特征反映了该区隆起与坳陷分布的范围及展布特征。扬马延微陆块中南部主要发育有北东向、北西向、近东西向及近南北向4组断裂,这4组断裂对基底结构、性质、隆坳格架及新生界分布起着重要的控制作用。扬马延微陆块中南部圈定出Ⅰ类和Ⅱ类油气勘探有利区,这些勘探有利区最显著的特征是处于自由空间重力局部异常及垂向二阶导数异常高值带,其发育明显受断裂构造控制。Ⅰ类勘探有利区对应的重力异常幅值较高、异常变化梯度较陡;相对于Ⅰ类勘探有利区,Ⅱ类勘探有利区对应的重力异常幅值相对较小、异常变化梯度相对较为平缓。

航磁水平分量数据的均衡滤波位场边缘增强研究

航磁矢量数据包含异常的强弱与方向信息,能够有效减少反演的多解性,从而提高数据处理与解释的精度和可靠性.因此,我们提出了航磁水平分量数据的均衡滤波位场边缘增强技术,提高了场源边界识别的分辨率.组合模型试验证明此方法不仅能够有效均衡不同埋深的地质体的异常信息,而且能提高对于相近或叠加地质体的识别能力.将此方法应用于新疆启鑫地区实测航磁矢量数据处理,结果表明,此方法使研究区弱异常得到了有效的均衡与增强,获取了更加清晰的位场边缘细节信息,结合研究区地质概况推断出29条断裂构造线,为研究区构造演化的推断与解释提供了更加丰富的参考依据.因此,该方法在应用航磁矢量数据进行位场边缘研究方面具有重要意义,为航磁矢量数据处理与解释提供了新的技术支撑.

面向无人驾驶车辆的局部路径规划研究

针对无人车单车智能出现的因路侧设备损坏、障碍物遮挡导致的行驶安全问题,开展无人机与无人车协同的路径规划研究。首先,提出一种基于无人机与无人车协同的无人智能系统模型。其次,提出基YOLOv3算法和改进人工势场法相结合的无人车局部路径规划方案,验证YOLOv3算法对城市交通中常见人、车、交通标志等路况信息的检测具有良好的实时性和准确性。然后,针对传统人工势场法,通过引入无人车与目标点的相对距离和设定无人车距离来优化人工势场模型。最后,模拟障碍物位置信息,进行仿真实验。实验结果表明,改进后的人工势场法显著提高路径平滑度,有效地规划出车辆的行驶路径。

基于BERT-BiLSTM-CRF的电力集控安全隐患数据处理

为了提高电力集控系统安全隐患数据处理的效果,提出一种基于来自变换器的双向编码器表示-双向长短期记忆网络-条件随机场(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short Term Memory-Conditional Random Fields,BERT-BiLSTM-CRF)的电力集控安全隐患数据处理方法。构建电力集控隐患数据检测模型,应用改进长短时记忆网络(Long Short Term Memory,LSTM)来构建电力集控安全隐患数据修复网络,实现电力集控安全隐患数据处理。实验结果表明,采用所提方法能够更好地完成电力集控安全隐患数据检测与修复,应用效果较好。