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Although horizon interpretation is a routine task for building reservoir models and accurately estimating hydrocarbon production volumes, it is a labour-intensive and protracted process. Hence, many scientists have worked to improve the horizon interpretation efficiency via auto-picking algorithms. Nevertheless, the implementation of a classic auto-tracking method becomes challenging when addressing reflections with weak and discontinuous signals, which are associated with complicated structures. As an alternative, we propose a workflow consisting of two steps: (1) the computation of strata histograms using transdimensional Markov-chain Monte Carlo and (2) horizon auto-tracking using waveform-based auto-tracking guided by those strata histograms. These strata histograms generate signals that are vertically sharper and more laterally continuous than original seismic signals; therefore, the proposed workflow supports the propagation of waveform-based auto-picking without terminating against complicated geological structures. We demonstrate the performance of the novel horizon auto-tracking workflow through seismic data acquired from the Gulf of Mexico, and the Markov-chain Monte Carlo inversion results are validated using log data. The auto-tracked results show that the proposed method can successfully expand horizon seed points even though the seismic signal continuity is relatively low around salt diapirs and large-scale faults. 相似文献
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综合最新布设的龙门山断裂带地震空段台阵(LmsSGA)与四川省地震局固定地震台网数据,对龙门山断裂带新近一年(2016年11月21日到2017年10月28日)的23479个地震事件开展双差定位工作,共获取包括汶川地震余震和芦山地震余震在内的6111个重定位地震事件.在此基础上,分别与汶川地震和芦山地震的早期余震空间分布特征进行比较.研究发现在汶川地震发生近十年后,其余震活动依旧活跃.汶川地震现今余震活动主要分布在10~25km的深度区间,震源深度呈现西南段较东北段偏深的特征.此外,汶川近年余震分布相比早期余震偏深,破裂带西南段的余震活动有向深部迁移的趋势.对于芦山地震,其近期余震活动较弱,余震主要分布在10~15km的深度区间,比早期余震的分布区间偏浅.龙门山断裂带最新余震活动分布特征表明,余震活动随着时间的推移有迁移的现象.考虑到距离主震事件已分别有5~10年的流逝时间,余震迁移现象可能由以流体扩散方式为主的准静态应力机制触发. 相似文献
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精确拾取微地震事件初至是微震定位的关键技术之一。根据STA/LTA和分形维数两种微地震初至拾取方法的原理,采用理论模型数据对两种初至拾取方法进行了测试,并选取不同信噪比的实际数据从初至拾取精度、算法效率两个方面进行了对比。结果表明,对于高信噪比微震事件,两种方法都能获得精度较高的初至,但对于低信噪比微震事件,分形维数与STA/LTA比较其拾取精度相对要高。鉴此,运用STA/LTA和分形维两种算法相结合的微震事件初至拾取方法,对实际数据进行了处理,实现了微震事件初至较为准确的自动拾取。 相似文献
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针对微震信号采集过程中存在大量不同频率的干扰信号,导致信号初至拾取难度大的问题,提出一种经验小波变换（EWT）结合分量阈值重构规则及奇异值分解（SVD）技术对微震信号进行降噪的方法。该方法利用EWT自适应分解和抗模态混叠的特点分解微震信号,得到各分量信号。对于高信噪比信号,选取相关系数大于0.3且方差贡献率大于15%的固有模态分量进行重构,具有较好的降噪效果;对于低信噪比信号,在高信噪比降噪方法的基础上利用SVD去除高频分量中的噪声,并与EWT分解出的低频有效分量进行重构。实验表明,对不同信噪比的构造信号与实际微震信号进行降噪,信噪比均得到明显改善;以修改的能量比法和分形维数法拾取实际微震降噪信号初至到时作为验证,拾取相对误差均小于1%,证明了本文降噪方法的有效性。 相似文献
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本文引入RNN的升级算法LSTM神经网络技术,建立了一套海量数据、高精度的自动拾取地震资料初至流程。相比于其他神经网络方法,LSTM神经网络能够提取数据的时序特征,加强学习初至前噪音时序特征,从而提高初至拾取的精度,为地震资料的初至拾取提供一套新的思路。首先设计样本制作过程并建立、训练模型,通过模拟资料验证方法的有效性,应用于胜利油田浅海与西部山地地震勘探资料的初至拾取,取得理想效果,证明LSTM神经网络初至拾取具有较高的精度与适用性。 相似文献
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针对网球场捡球工作量大的问题,开发了一款基于STM32的智能自动小型移动网球拾取机器人,规避了现存网球机器人结构庞大、灵活性差、效率低的问题.通过构建机器人三维模型,设计了一款造价低廉、高精密的小型移动网球拾取机器人.采用质心法获取目标物坐标信息,在机器视觉模块与双机械臂机构协同下,采取多行走舵机与万向轮协同实现机器人自动寻找目标、完成拾取、归集网球等动作.实验结果表明机器人能很好地完成以上功作,且拾取速度快,精确度达到98％. 相似文献
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Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems 
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下载免费PDF全文 Zefeng Li 《地震科学（英文版）》2021,34(6):531-540
Seismic data processing techniques, together with seismic instrumentation, determine our earthquake monitoring capability and the quality of resulting earthquake catalogs. This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing. Over the past two decades, seismologists have made considerable advancements in seismic data processing, partly thanks to the significant development of computational power, signal processing, and machine learning techniques. In particular, wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data. Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution. These techniques are becoming standard, leading to emerging intelligent software systems for next-generation earthquake monitoring. Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms (for both permanent and temporary deployments) and in emergency real-time monitoring of ongoing sequences (e.g., aftershock and induced seismicity sequences). We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks. 相似文献