深部地质结构对浅层地质与成矿作用的制约——以中国东部大陆及海区为例

根据十余年来参加中国岩石圈三维结构研究和国际合作编图资料,将深部地质结构与浅层地质、成矿特征进行统一的综合分析研究,对中国东部及邻近海域地质构造取得一些新认识,提出一些新观点:(1)在中国大陆及邻近海域400 km深度内,划分出5层地质结构,其中第3、4层之间的变化制约了浅层地质与成矿作用;(2)中国大陆及海区受周边动力挤压,其中西部挤压力大于东部,导致西部软流圈物质经4条路径向东流动,使东部汇聚巨大巨厚的软流圈,形成独特的软流圈巨量热能(力)-动力主体动力区,构成了亚洲大陆第4个主体动力区;(3)太平洋板块俯冲力制约了北纬42°40'以北的完达山及俄罗斯远东地区,菲律宾海板块俯冲力制约了台湾和中国东南沿海地区,二者的动力之和构成了阻挡软流圈物质向东部洋区流动的"远程力效应";(4)软流圈巨量热能(力)-动力作用的主体动力区打造了中国东部及海区7项地质、地貌特征和三大类矿产资源效应。其中岩浆活动与化石燃料矿产(石油、天然气、煤)之间不是"水火不相容",而是岩浆活动为提升油-气成熟度和煤化作用及变质作用提供了重要的温度条件;(5)软流圈巨量热能(力)-动力作用打造了中国东部及海区一些独有的地质作用:①形成了新生代南海小洋盆,并向巴拉望岛俯冲。②产生了新生代陆缘裂谷带,形成了琼州海峡、台湾海峡,并向台湾岛轻度俯冲。③大面积的巨厚软流圈是一座"巨型岩浆山",成为东部及海区"地幔柱"的统一"根基"。地幔柱实际上是巨大"岩浆山"中的"岩浆山峰",今后会发现更多"地幔柱"(即"岩浆山峰")。④中国东部地震除吉林省珲春地区的深源地震(540 km)外,朝鲜半岛及其以西的中国东部和海区均为浅源与中深源地震(震源深度小于100 km),其主体动力都来自本区巨量热能(力)-动力源,太平洋板块-菲律宾海板块的联合俯冲力只起了"远程力效应"的深部侧向挤压辅助作用。⑤巨量热能(力)-动力作用形成了一系列盆-山耦合带,组成了中国东部及海区中-新生代"盆-山耦合群","盆"里有油气资源,"山"里有内生矿产。     

基于迁移学习的地球物理测井储层参数预测方法研究

随着大数据和机器学习的成熟和推广应用,人工神经网络在地球物理测井预测储层参数中得到重视.本文引入迁移学习进行测井储层参数预测,以孔隙度预测神经网络模型和孔隙度含水饱和度联合预测神经网络模型为基础模型,分别以渗透率及含水饱和度预测作为目标任务进行迁移学习,以提升储层参数预测效果和效率.文中详细阐述了基于迁移学习的测井储层参数预测方法,并使用64口井的测井数据进行储层参数预测效果分析.结果表明,使用迁移学习后,渗透率模型预测效果最高可以提升58.3%;含水饱和度模型预测效果最高可以提升近40%,且最大可以节省60%的计算资源;以孔隙度预测模型为基础模型时更适合使用参数冻结的训练方式,以孔隙度含水饱和度联合预测模型为基础模型时更适合使用参数微调的训练方式.

     

基于半监督学习的井震联合储层横向孔隙度预测方法

传统地震储层预测技术一般基于弹性参数反演和岩石物理建模的级联流程实现储层孔隙度预测, 其预测精度受到波动理论和岩石物理理论的近似假设、初始模型和二次反演累积误差等因素的影响.为缓解这些问题, 本文提出了一种基于双向门控递归单元神经网络的半监督学习井震联合孔隙度预测方法, 实现从地震数据直接预测储层横向孔隙度.通过少量的地震测井样本标签对和多目标函数约束建立智能化多尺度多信息融合孔隙度预测模型, 实现地震数据到孔隙度, 孔隙度再到生成地震数据的闭环映射.此外, 在网络模型每次迭代更新的过程中随机引入非井旁地震道参与网络训练, 非井旁地震道的波形匹配能在一定程度上保证井间孔隙度的预测精度.模型数据和实际数据测试结果表明, 本文提出的方法相比于有监督学习孔隙度预测方法能进一步提高储层孔隙度的预测准确性和横向连续性, 获得较为可靠的储层物性参数的空间分布.

     

融合先验物理信息的高精度智能可控源电磁反演算法

可控源电磁反演利用人工信号获取地下结构信息,为地质勘探和资源开发提供准确的数据支持。然而,传统电磁反演方法面临低分辨率的挑战,主要是由于简化处理和观测数据的限制,导致模型平滑、细节丢失,从而削弱了反演的准确性,影响了电磁勘探的效率。为了解决这一问题,本文提出将传统反演结果与响应数据同时作为深度网络的输入数据,为深度网络反演提供先验物理信息,并结合深度学习算法提升可控源电磁反演的计算效率。通过模型试验,对合成的电阻率模型分别进行了传统反演、智能反演以及融合先验物理信息的智能反演。结果表明,融合先验物理信息的智能反演能够更好地刻画异常体结构特征,有效提升反演效率,并且得到的电阻率参数更符合实际。最后,将该反演技术应用于金川铜镍矿床的可控源数据反演解释,取得了较为可靠的应用效果。     

基于SHAP值机器学习的江西暖季暴雨预报因子重要性分析

机器学习模型（Machine Learning,ML）的不可解释性给其在气象业务中的应用带来了挑战。模型解释和可视化是解决这一问题的有效途径。文中将SHAP值应用于天气预报ML模型解释,研究了江西省暖季暴雨模型的预报因子对预报结果的影响。分别选取2016—2020年、2021—2022年4—9月ECWMF（European Centre for Medium-Range Weather Forecasts）高分辨率数值模式物理量及国家站降水观测数据进行XGBoost 建模与模型解释。结果表明,全局重要性排名前4位依次是总降水（重要性42.70%）、850 hPa比湿（重要性11.17%）、925 hPa相对湿度（重要性10.44%）、500 hPa相对湿度（重要性 9.16%）。个例分析表明,命中个例中高重要性物理因子在暴雨区的 SHAP 值较大,漏报（空报）个例在漏报（空报）区域高重要性物理因子的SHAP值偏小（偏大）。SHAP值从全局和局部可定量给出ML模型有物理意义的解释,解释结果与天气学原理和业务经验较一致,有利于ML在气象业务中的深入应用。     

APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.     

Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

基于机器学习的地震动特征提取与模拟以2021年云南漾濞6.4级地震为例

随着地震动数据数量的增长和质量的提高,将基于数据驱动的机器学习方法应用到地震动模拟中有重要意义。以2021年5月21日云南漾濞M_S6.4地震为例,利用主成分析方法从前震及余震地震动记录中提取特征母波时程,将地震动三要素作为模拟误差约束,在求解母波的线性组合系数时使用多目标优化算法寻优,最终找到帕累托最优解作为模拟目标台站记录时的组合系数,得到模拟地震动时程。结果表明:主成分析法在对实际地震动记录进行特征提取后,得到的特征母波时程可以在一定程度上保留原始数据的主要信息;考虑幅值、频谱和持时这三要素的角度去控制模拟误差,可以使得模拟的地震动时程更加接近真实记录。提出的基于特征提取的地震动模拟方法可以为基于小震数据合成大震地震动提供参考。     

Self-perceived Roles in Life and Achieving Sustainability on Family Farms in North-eastern Australia

Rural production landscapes in Australia are experiencing a rapid rate of change as a result of, among other factors, climate change, biodiversity loss and changing societal values. Consequently, there is increasing pressure on producers to increase their sustainability. Understanding how producers perceive themselves in the context of this changing landscape is limited but important for the design of policy effective for achieving sustainability. This paper is based on a case study in the north-eastern Australian rangelands that included face-to-face interviews with 28 beef producers and a telephone survey with another 91 producers. The study investigated male and female beef producers' self-perceived roles in life through a lens of different farming discourses and the relationship between these roles and beliefs aligned with sustainability. Results revealed that although producers' self-percieved roles in life were being constructed through a mix of more or less ‘traditional’ discourses, tradition was still a strong influence. Producers who strongly identified with roles linked to ‘less traditional’ discourses were more likely than those who strongly identified with production-orientated roles to agree with beliefs that favoured nature conservation, learning and adapting to change. Increased opportunities for producers to participate in alternative discourses would appear important for fostering a self-identity that is open to learning, difference and change.