Elastic full waveform inversion with probabilistic petrophysical clustering

Full waveform inversion aims to use all information provided by seismic data to deliver high-resolution models of subsurface parameters. However, multiparameter full waveform inversion suffers from an inherent trade-off between parameters and from ill-posedness due to the highly non-linear nature of full waveform inversion. Also, the models recovered using elastic full waveform inversion are subject to local minima if the initial models are far from the optimal solution. In addition, an objective function purely based on the misfit between recorded and modelled data may honour the seismic data, but disregard the geological context. Hence, the inverted models may be geologically inconsistent, and not represent feasible lithological units. We propose that all the aforementioned difficulties can be alleviated by explicitly incorporating petrophysical information into the inversion through a penalty function based on multiple probability density functions, where each probability density function represents a different lithology with distinct properties. We treat lithological units as clusters and use unsupervised K-means clustering to separate the petrophysical information into different units of distinct lithologies that are not easily distinguishable. Through several synthetic examples, we demonstrate that the proposed framework leads full waveform inversion to elastic models that are superior to models obtained either without incorporating petrophysical information, or with a probabilistic penalty function based on a single probability density function.     

Simultaneous joint migration inversion for high-resolution imaging/inversion of time-lapse seismic datasets

The current time-lapse practice is to exactly repeat well-sampled acquisition geometries to mitigate acquisition effects on the time-lapse differences. In order to relax the rigid requirements on acquisition effects, we propose simultaneous joint migration inversion as an effective time-lapse tool for reservoir monitoring, which combines a joint time-lapse data processing strategy with the joint migration inversion method. Joint migration inversion is a full-wavefield inversion method that explains the measured reflection data using a parameterization in terms of reflectivity and propagation velocity. Both the inversion process inside the imaging/inversion scheme and the extra illumination provided by including multiples in joint migration inversion makes the obtained velocity and reflectivity operator largely independent of the utilized acquisition geometry and, thereby, relaxes the strong requirement of non-repeatability during the monitoring. Because simultaneous joint migration inversion inverts for all datasets simultaneously and utilizes various constraints on the estimated reflectivities and velocity, the obtained time-lapse differences have much higher accuracy compared to inverting each dataset separately. It allows the baseline and monitor parameters to communicate with each other dynamically during inversion via a user-defined spatial weighting operator. In order to get more localized time-lapse velocity differences, we further extend the regular simultaneous joint migration inversion to a robust high-resolution simultaneous joint migration inversion process using the time-lapse reflectivity difference as an extra constraint for the velocity estimation during inversion. This constraint makes a link between the reflectivity- and the velocity difference by exploiting the relationship between them. We demonstrate the feasibility of the proposed method with a highly realistic synthetic model based on the Grane field offshore Norway and a time-lapse field dataset from the Troll Field.     

地震实时波形数据汇聚系统设计与实现

随着国家地震烈度速报与预警工程和地震信息化工程不断建设,地震台站数量激增,观测数据量呈指数型增长。为实现海量地震台站实时观测数据汇聚到国家中心,缩减数据汇集流程,本文开展了基于分布式技术（Hadoop技术框架）地震实时波形观测数据汇聚系统的研究,实现地震观测数据的云端采集,并以网络中断重连机制和实时流数据双链路接入机制保证数据的可靠传输,实现地震观测数据汇集的统一调度和高效管理。     

鹤岗地震台阵地震波形特征分析

为了提高我国微小地震监测能力,"一带一路"项目拟在黑龙江鹤岗地区建设地震台阵。在2019年8月4日至9月4日,鹤岗地震台阵监测到部分近震及远震,与黑龙江省鹤岗地震台观测数据进行对比分析,得到以下结果：与鹤岗地震台相比,鹤岗地震台阵对近震及区域性地震的监测能力较高,对远震和极远震的监测范围较大,记录完整的地震个数更多,整体背景噪声更小,地震波形质量更高,定位更精确,提高了区域地震监测能力。     

2019年吉林宁江MS5.1地震全矩张量反演及其发震机理初探

采用吉林、黑龙江、辽宁和内蒙古地震台网记录的地震波形数据,利用ISOLA近震全波形反演方法对2019年5月18日吉林宁江M_S5.1地震进行全矩张量反演。结果表明,该地震的最佳断层面解节面Ⅰ走向304°/倾角81°/滑动角26°,节面Ⅱ走向210°/倾角65°/滑动角170°;最佳矩心深度6km,矩震级M_W5.0。根据宁江M_S5.1地震序列展布形态,推断节面Ⅱ可能为优势发震断层面,即本次地震的主控断裂为扶余-肇东断裂,和与其正交的第二松花江断裂共同控制着余震展布方向。全矩张量解在Husdon震源类型图上的投影显示本次地震具有明显的非双力偶成分,是1次体积增加的张性破裂。根据区域地质构造特征和震源区接收函数、电磁测深和地下热结构等地球物理研究结果,综合分析认为在西太平洋板块作用形成俯冲带的同时,也相应地产生了热物质上涌,这些地球物理过程可能会改变莫霍面形态,使其向上突起并作用于活动断层,从而形成此次吉林宁江M_S5.1地震。     

识别地震与爆炸的一种时间域新方法

利用地震观测波形直达纵横波能量的差异,在时间域创建标志地震事件震源性质的新参量a,计算182个台站记录的24个地震事件的a值,并用以分析2017年9月3日敏感地区发生的地震事件性质。结果表明,判据a具有良好的震源性质依附效应,从而为地震与爆炸的识别引进了一种新方法。     

非均匀流-固边界耦合介质多参数全波形反演方法

海洋勘探环境可以抽象为下伏固体与上覆流体相互耦合的介质,本文针对流-固边界耦合介质提出了一种高效、稳定的多参数（速度和密度）全波形反演方法.本文采用弹性波一阶位移-应力方程作为过渡层耦合声波压力方程与弹性波位移方程来模拟耦合环境,相比于传统的交错网格建模方法或者构建连续性条件,本文提出的方法在正演精度和稳定性上凸显出很大优势,极大降低了计算内存.反演策略对多参数全波形反演至关重要,由于不同参数之间的相互耦合使得密度在多参数全波形反演中较难获得,因此本文将非均匀流-固边界耦合介质多参数全波形反演分为两个步骤完成：第一步利用变密度声波方程结合推导出的密度梯度算子进行纵波速度和密度的双参数反演;第二步根据链式法则求取横波速度的梯度,结合第一步的反演结果使用流-固边界耦合方程反演横波速度.最后通过与声波动方程数值模拟结果对比证明正演算法的准确性;上覆流体的Marmousi-2模型的数值试验测试说明反演方法的有效性和适应性.     

CryoSat-2SARIn模式二级产品在内陆水域的偏差分析

对比青藏高原及天山地区所有湖泊内CryoSat-2基线C版本二级产品(SIR_SIN_L2I)和ICESat测高结果发现,它们之间存在一个多达几十米的系统性偏差,这种偏差限制了多源测高数据的联合研究,必须加以消除。从一级数据出发,利用经典的重跟踪方法重新获得湖面高程,能很好地消除这个偏差。研究发现,当前的二级产品在生成过程中使用了错误的窗口延迟参数,从而导致该系统性偏差。因此,CryoSat-2的SARIn模式二级产品不能直接用于多源数据的联合分析,需要对一级数据重跟踪或直接在二级数据上加一个偏差改正。
     

Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Abstract

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ～8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ～10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads.     

Inversion of teleseismic waves at Shidao Seismographic Station

1 IntroductionThe temporary Shidao seismographic station,the farthest one from China s Mainland (except Tai-wan Province) supported by a national fundamentalresearch project for the study of the evolution ofcontinental margin, is located at Shidao island(…