CN115639605A - Automatic high-resolution fault identification method and device based on deep learning - Google Patents
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Abstract
本发明提供一种基于深度学习的高分辨率断层的自动识别方法和装置,包括:获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。所提出的方法相较于常规方法可获得更干净清晰的断层概率图,预测的断层位置也更加准确。
The present invention provides a method and device for automatic identification of high-resolution faults based on deep learning, including: acquiring a seismic image to be identified; inputting the seismic image to be identified into a quality improvement model, and outputting an enhanced seismic image; wherein, the The quality improvement model is obtained after training a first training set and a first validation set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training set and the first validation set Generated by random parameter simulation; input the lifted seismic image into a fault identification model, and output a fault probability map; wherein, the fault identification model is a second training set constructed based on a plurality of sample seismic images and corresponding fault probability map labels obtained after training with the second validation set. Compared with the conventional method, the proposed method can obtain a cleaner and clearer fault probability map, and the predicted fault position is also more accurate.
Description
技术领域technical field
本发明涉及地球物理勘探技术领域,尤其涉及一种基于深度学习的高分辨率断层的自动识别方法和装置。The invention relates to the technical field of geophysical exploration, in particular to an automatic recognition method and device for high-resolution faults based on deep learning.
背景技术Background technique
地震图像是一种重要的地球物理资料,它可以提供连续的,大范围的地下地质信息。有经验的地球物理工作者通过地震图像可以推断地层的沉积年代、分析沉积环境和构造运动以及辅助油气勘探。地下的断层是一种重要的地质构造,它控制着盆地的沉积、影响着地下流体的运移以及矿产的分布。通过地震图像识别地下断层是一种常用的手段,但是,由于地震勘探的规模大、数据复杂,人工解释断层是一项费力且高度主观的任务。因此,随着计算机技术的发展,计算机辅助自动断层识别越来越流行。Seismic image is an important geophysical data, which can provide continuous and large-scale subsurface geological information. Experienced geophysicists can infer the sedimentary age of the strata, analyze the sedimentary environment and tectonic movement, and assist oil and gas exploration through seismic images. The subsurface fault is an important geological structure, which controls the deposition of the basin, affects the migration of subsurface fluids and the distribution of minerals. Identifying subsurface faults from seismic images is a commonly used means, however, manual interpretation of faults is a laborious and highly subjective task due to the large scale and data complexity of seismic surveys. Therefore, with the development of computer technology, computer-aided automatic fault recognition is becoming more and more popular.
专利申请CN202010188216.0公开了一种基于地震数据优势频率的三属性融合断层识别方法,首先对全频带地震数据分频处理,选取优势频率体数据制作相干、倾角和方位角属性体,最后将三种属性应用HIS叠合显示得到融合体以进行断层解释。该方法通过分频和图像融合技术提高了断层的识别能力,但是需要耗费大量的精力来挑选优势频率体,后续的属性体计算需要大量的计算且参数的选取影响后续的解释精度。Patent application CN202010188216.0 discloses a three-attribute fusion fault identification method based on the dominant frequency of seismic data. Firstly, the seismic data of the whole frequency band is frequency-divided and processed, and the dominant frequency volume data is selected to make the coherence, dip and azimuth attribute volumes. Finally, the three Species attributes are displayed using HIS superimposition to obtain a fusion body for fault interpretation. This method improves the identification ability of faults through frequency division and image fusion technology, but it takes a lot of energy to select the dominant frequency volume, and the subsequent attribute volume calculation requires a lot of calculations, and the selection of parameters affects the subsequent interpretation accuracy.
专利申请CN201910854606.4公开了一种层位约束下基于地震解析道的相干增强断层识别方法,通过引入希尔伯特变换和直方图均衡化等手段改进传统的相干算法,具有较高的抗噪性,但碍于相干体本身的限制,结果容易产生多解且无法清晰刻画小断层。Patent application CN201910854606.4 discloses a coherent enhanced fault identification method based on seismic analysis traces under horizon constraints, which improves the traditional coherent algorithm by introducing Hilbert transform and histogram equalization, which has high noise resistance However, due to the limitations of the coherent body itself, the result is prone to multiple solutions and cannot clearly describe small faults.
专利申请CN201710186136.X公开了一种基于AdaBoost算法的地震相干体图像断层自动识别方法,实现了对地震相干体地震图像的自动识别,虽然提高了断层识别准确率,但是在挖掘特征信息时需要调整数据块的大小,增加了数据的空间占用率。Patent application CN201710186136.X discloses an automatic identification method for seismic coherent volume image faults based on AdaBoost algorithm, which realizes automatic identification of seismic coherent volume seismic images. Although the accuracy of fault identification is improved, it needs to be adjusted when mining feature information. The size of the data block increases the space occupancy of the data.
专利申请CN202110946552.1公开了一种基于深度学习语义分割的地震断层识别方法,借助深度卷积神经网络的强大拟合能力实现快速准确的断层识别,但是其识别精度受到地震数据信噪比的影响。Patent application CN202110946552.1 discloses a seismic fault identification method based on deep learning semantic segmentation, which uses the powerful fitting ability of deep convolutional neural network to realize fast and accurate fault identification, but its identification accuracy is affected by the signal-to-noise ratio of seismic data .
2016年黄诚等人采用体属性、沿层构造属性、吸收衰减属性等分析技术优选地震属性体,分阶段、分层次地对研究区的断层系统进行了有效预测,多方法的结合应用减少了断层识别过程中的多解性。2017年仲伟军等人综合利用相干体、方差体、构造曲率体以及多窗口倾角扫描体、构造导向滤波、相干能量梯度体、蚂蚁体追踪、边缘检测等技术手段,形成了以倾角导向技术为代表的断层识别技术流程,识别了不同级别、不同期次发育的断层。张瑞等人在2017年借助广义S变换与小波变换分析技术提出了分频蚂蚁追踪技术,该方法能够较好的压制噪声干扰,对断层的识别更加清晰、准确,可以识别出常规全频带数据难以识别的深层小断层。2017年李全和童利清通过地震属性优化组合对复杂断裂进行了识别,首先以基于导向的相干体属性控制断裂展布趋势,然后优选融合凸显局部细节的倾角体、方位角体和曲率体等地震属性体精细刻画三、四级断裂和微断裂的空间展布特征。同样在2017年,孙振宇等人以地震属性作为支持向量机的输入构建了断层识别模型,准确率达到98%,降低了人为主观因素的影响,缩短了解释周期。2020年刘万金和咸海龙将相干体和振幅、相干体与最大正曲率进行图像融合,充分利用各自的构造信息,增强了识别小断层的能力,而且提高了断层平面组合、平面展布特征及相互关系的可靠性。2022年侯俊韬等人将断层自动提取技术应用到基于导向滤波的分频相干体上,通过对噪音的压制得到了更加精细准确的断层刻画结果。丁昌伟等人在2022年提出利用信息价值对地震属性进行约简,结合改进的贝叶斯优化算法,优化XGBoost参数以进一步提高小断层地震解释的精度,该方法有效解决了小断层样本分布不均衡的问题,具有一定的抗干扰能力。刘乃豪等人在2022年引入深度学习中的边缘检测技术并根据地震数据和断层特点对网络结构进行优化,提出了使用地震断层解释的改进HED网络,其对断层预测的准确性更高,连续性更好。同样在2022年,张政等人将深度残差网络与迁移学习结合并应用于断层识别,在合成数据训练模型基础上,使用少量实际断层样本进行迁移学习,增强网络的泛化能力,提升了断层识别的性能。In 2016, Huang Cheng et al. used analysis techniques such as volume attributes, bedding-along structural attributes, and absorption-attenuation attributes to optimize seismic attribute bodies, and effectively predicted the fault system in the study area in stages and layers. The combined application of multiple methods reduced the Multisolution in fault identification process. In 2017, Zhong Weijun and others comprehensively used coherent volume, variance volume, structural curvature volume, multi-window dip scanning volume, structure-guided filtering, coherent energy gradient volume, ant body tracking, edge detection and other technical means to form a dip-guided technology. The technical process of fault identification represented by the company has identified faults developed at different levels and in different stages. In 2017, Zhang Rui and others proposed the frequency-division ant tracking technology with the help of generalized S-transform and wavelet transform analysis techniques. This method can better suppress noise interference, identify faults more clearly and accurately, and can identify conventional full-band data. Difficult to identify deep small faults. In 2017, Li Quan and Tong Liqing identified complex faults through the optimal combination of seismic attributes. First, they controlled the distribution trend of faults with coherent volume attributes based on guidance, and then optimized the fusion of dip volumes, azimuth volumes, and curvature volumes that highlight local details. The attribute body finely depicts the spatial distribution characteristics of third- and fourth-order fractures and micro-fractures. Also in 2017, Sun Zhenyu and others used seismic attributes as the input of support vector machine to build a fault identification model with an accuracy rate of 98%, which reduced the influence of human subjective factors and shortened the interpretation cycle. In 2020, Liu Wanjin and Xian Hailong fused images of coherent body and amplitude, coherent body and maximum positive curvature, made full use of their respective structural information, enhanced the ability to identify small faults, and improved fault plane combination, plane distribution characteristics and mutual relationship reliability. In 2022, Hou Juntao and others applied the automatic fault extraction technology to the frequency-division coherent body based on guided filtering, and obtained more precise and accurate fault description results by suppressing noise. In 2022, Ding Changwei and others proposed to use the information value to reduce the seismic attributes, combined with the improved Bayesian optimization algorithm, optimize the XGBoost parameters to further improve the accuracy of small fault seismic interpretation, this method effectively solves the uneven distribution of small fault samples It has a certain anti-interference ability. In 2022, Liu Naihao and others introduced the edge detection technology in deep learning and optimized the network structure according to the characteristics of seismic data and faults, and proposed an improved HED network using seismic fault interpretation, which has higher accuracy and continuity in fault prediction. better. Also in 2022, Zhang Zheng et al. combined the deep residual network with transfer learning and applied it to fault recognition. On the basis of the synthetic data training model, a small number of actual fault samples were used for transfer learning to enhance the generalization ability of the network and improve the Performance of fault identification.
上述方法在一定程度上都为地震断层的自动识别做出了贡献,但是这些方法都缺乏对断层识别的精度分析,而且还缺少多地震数据质量较低时识别效果的分析,例如地震数据中包含大量的随机噪音、地震数据分辨率低。然而,野外采集的地震数据不可避免的会噪音随机噪音的干扰,以及地下介质对地震波高频成分的吸收作用所导致的分辨率低的情况,尤其是在深层、超深层勘探时。这些原因就导致了目前提出的断层识别的方法普适性较差。The above methods have contributed to the automatic identification of seismic faults to a certain extent, but these methods lack the accuracy analysis of fault identification, and also lack the analysis of the identification effect when the quality of multi-seismic data is low, for example, seismic data contains Lots of random noise, low resolution seismic data. However, the seismic data collected in the field will inevitably be disturbed by random noise and low resolution caused by the absorption of high-frequency components of seismic waves by the underground medium, especially in deep and ultra-deep exploration. These reasons lead to the poor universality of the current fault identification method.
因此,如何避免在较低地震数据质量时断层自动识别的精度较低以及可信度较低,仍然是本领域技术人员亟待解决的问题。Therefore, how to avoid the low accuracy and low reliability of automatic fault identification when the quality of seismic data is low is still a problem to be solved urgently by those skilled in the art.
发明内容Contents of the invention
本发明提供一种基于深度学习的高分辨率断层的自动识别方法,用以解决现有技术中在较低地震数据质量时断层自动识别的精度较低以及可信度较低的问题。The present invention provides an automatic identification method of high-resolution faults based on deep learning, which is used to solve the problems of low accuracy and low reliability of automatic identification of faults in the prior art when the quality of seismic data is low.
本发明提供一种基于深度学习的高分辨率断层的自动识别方法,包括:The present invention provides an automatic recognition method for high-resolution faults based on deep learning, including:
获取待识别地震图像;Obtain the seismic image to be identified;
将所述待识别地震图像输入质量提升模型,输出提升后地震图像;Input the seismic image to be identified into the quality improvement model, and output the enhanced seismic image;
其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;Wherein, the quality improvement model is obtained after training on a first training set and a first verification set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training set and the The first verification set is generated by random parameter simulation;
将所述提升后地震图像输入断层识别模型,输出断层概率图;Inputting the upgraded seismic image into a fault identification model, and outputting a fault probability map;
其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。Wherein, the fault recognition model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels.
根据本发明提供的一种基于深度学习的高分辨率断层的自动识别方法,还包括:According to the automatic identification method of a high-resolution fault based on deep learning provided by the present invention, it also includes:
将所述待识别地震图像输入所述断层识别模型,输出地震图像直接识别结果;input the seismic image to be identified into the fault identification model, and output the direct identification result of the seismic image;
比较所述断层概率图和所述地震图像直接识别结果。The fault probability map is compared with the seismic image direct identification result.
根据本发明提供的一种基于深度学习的高分辨率断层的自动识别方法,所述第一训练集和所述第一验证集通过随机参数模拟生成,具体包括:According to a method for automatic recognition of high-resolution faults based on deep learning provided by the present invention, the first training set and the first verification set are generated through random parameter simulation, specifically including:
按照预设规则生成地层反射率模型;Generate formation reflectance models according to preset rules;
通过随机参数控制生成不同类型的地层反射率模型;Generate different types of formation reflectance models through random parameter control;
将任一地层反射率模型与低频雷克子波卷积并添加随机噪音的地震数据进行两倍的下采样获得样本低质量地震图像;Convolve any stratum reflectivity model with low-frequency Reich wavelets and add random noise to the seismic data for twice down-sampling to obtain sample low-quality seismic images;
将所述任一地层反射率模型与高频雷克子波卷积获得对应的高质量地震图像标签。The corresponding high-quality seismic image labels are obtained by convolving any stratum reflectivity model with the high-frequency Rake wavelet.
根据本发明提供的一种基于深度学习的高分辨率断层的自动识别方法,所述质量提升模型在训练过程中的网络结构为生成对抗网络,所述生成对抗网络包含一个生成器和一个判别器,其中,所述生成器包含一个跳层连接和多个残差连接,所述判别器包含一个卷积层和七个卷积块。According to an automatic identification method of high-resolution faults based on deep learning provided by the present invention, the network structure of the quality improvement model in the training process is a generative confrontation network, and the generative confrontation network includes a generator and a discriminator , where the generator contains a skip connection and multiple residual connections, and the discriminator contains a convolutional layer and seven convolutional blocks.
根据本发明提供的一种基于深度学习的高分辨率断层的自动识别方法,所述质量提升模型在训练过程中,所述质量提升模型在验证集上最高峰值信噪比所对应的网络参数为最优模型参数,其中,所述峰值信噪比为生成对抗网络重构高质量地震图像和真实高质量地震图像之间的相似度。According to an automatic identification method of high-resolution faults based on deep learning provided by the present invention, during the training process of the quality improvement model, the network parameters corresponding to the highest peak signal-to-noise ratio of the quality improvement model on the verification set are: Optimal model parameters, wherein the peak signal-to-noise ratio is the similarity between the high-quality seismic image reconstructed by the generative adversarial network and the real high-quality seismic image.
根据本发明提供的一种基于深度学习的高分辨率断层的自动识别方法,所述断层识别模型的训练过程中使用的模型网络结构为U-net网络,所述U-net网络包括编码分支和解码分支,所述编码分支由四次卷积操作和下采样组成,所述解码分支由四个上采样和卷积操作组成。According to a method for automatic recognition of high-resolution faults based on deep learning provided by the present invention, the model network structure used in the training process of the fault recognition model is a U-net network, and the U-net network includes encoding branches and A decoding branch, the encoding branch consists of four convolution operations and downsampling, the decoding branch consists of four upsampling and convolution operations.
根据本发明提供的一种基于深度学习的高分辨率断层的自动识别方法,所述断层识别模型训练过程的损失函数中为表示断层的像素赋予的权重大于为表示非断层的像素赋予的权重。According to an automatic recognition method of high-resolution faults based on deep learning provided by the present invention, in the loss function of the fault recognition model training process, the weight assigned to pixels representing faults is greater than the weight assigned to pixels representing non-faults.
本发明还提供一种基于深度学习的高分辨率断层的自动识别装置,包括:The present invention also provides an automatic recognition device for high-resolution faults based on deep learning, including:
获取单元,用于获取待识别地震图像;an acquisition unit, configured to acquire a seismic image to be identified;
质量提升单元,用于将所述待识别地震图像输入质量提升模型,输出提升后地震图像;a quality improvement unit, configured to input the seismic image to be identified into a quality improvement model, and output the enhanced seismic image;
其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;Wherein, the quality improvement model is obtained after training on a first training set and a first verification set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training set and the The first verification set is generated by random parameter simulation;
断层识别单元,用于将所述提升后地震图像输入断层识别模型,输出断层概率图;a fault identification unit, configured to input the lifted seismic image into a fault identification model, and output a fault probability map;
其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。Wherein, the fault recognition model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels.
本发明还提供一种电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,所述处理器执行所述程序时实现如上述任一种所述的基于深度学习的高分辨率断层的自动识别方法的步骤。The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the depth-based Steps in learning the method for automatic identification of high-resolution tomograms.
本发明还提供一种非暂态计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现如上述任一种所述的基于深度学习的高分辨率断层的自动识别方法的步骤。The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the automatic identification of high-resolution faults based on deep learning as described in any one of the above is realized. method steps.
本发明提供的基于深度学习的高分辨率断层的自动识别方法和装置,通过获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。实现了获得更干净清晰的断层概率图,预测的断层位置更加准确。The method and device for automatic identification of high-resolution faults based on deep learning provided by the present invention obtain seismic images to be identified; input the seismic images to be identified into the quality improvement model, and output the enhanced seismic images; wherein, the quality improvement The model is obtained after training a first training set and a first validation set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training set and the first validation set are obtained by random Parameter simulation generation; input the upgraded seismic image into a fault identification model, and output a fault probability map; wherein, the fault identification model is based on a second training set and a second training set constructed based on a plurality of sample seismic images and corresponding fault probability map labels The second validation set is obtained after training. A cleaner and clearer fault probability map is achieved, and the predicted fault position is more accurate.
附图说明Description of drawings
为了更清楚地说明本发明或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍,显而易见地,下面描述中的附图是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the present invention or the technical solutions in the prior art, the accompanying drawings that need to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are the present invention. For some embodiments of the invention, those skilled in the art can also obtain other drawings based on these drawings without creative effort.
图1为本发明提供的基于深度学习的高分辨率断层的自动识别方法的流程示意图;Fig. 1 is a schematic flow chart of the automatic identification method of high-resolution faults based on deep learning provided by the present invention;
图2为本发明提供的基于深度学习的高分辨率断层的自动识别装置的结构示意图;Fig. 2 is a schematic structural diagram of an automatic identification device for high-resolution faults based on deep learning provided by the present invention;
图3为本发明提供的地震数据质量提升的神经网络结构图;Fig. 3 is the neural network structural diagram that the seismic data quality promotion that the present invention provides;
图4为本发明提供的地震数据质量提升神经网络的训练过程示意图;Fig. 4 is a schematic diagram of the training process of the seismic data quality improvement neural network provided by the present invention;
图5为本发明提供的地震断层识别神经网络结构图;Fig. 5 is the neural network structural diagram of earthquake fault identification provided by the present invention;
图6为本发明提供的断层识别神经网络的训练过程示意图;Fig. 6 is a schematic diagram of the training process of the fault recognition neural network provided by the present invention;
图7为本发明提供的高分辨率断层智能识别端到端的工作流示意图;Fig. 7 is a schematic diagram of the end-to-end workflow of high-resolution fault intelligent identification provided by the present invention;
图8为本发明提供的在野外真实地震数据上的应用以及与传统实现流程的对比示意图;Figure 8 is a schematic diagram of the application of the present invention on real seismic data in the field and the comparison with the traditional implementation process;
图9是本发明提供的电子设备的结构示意图。FIG. 9 is a schematic structural diagram of an electronic device provided by the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚,下面将结合本发明中的附图,对本发明中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
由于现有技术中普遍存在在较低地震数据质量时断层自动识别的精度较低以及可信度较低的的问题。下面结合图1-图9描述本发明的基于深度学习的高分辨率断层的自动识别方法。图1为本发明提供的基于深度学习的高分辨率断层的自动识别方法的流程示意图,如图1所示,该方法包括:Because the problems of low precision and low reliability of automatic fault identification generally exist in the prior art when the quality of seismic data is low. The method for automatic recognition of high-resolution tomograms based on deep learning of the present invention will be described below with reference to FIGS. 1-9 . Fig. 1 is a schematic flow chart of the automatic identification method of high-resolution faults based on deep learning provided by the present invention. As shown in Fig. 1, the method includes:
步骤110,获取待识别地震图像。
具体地,获取待识别地震图像,通常获得的地震数据图像由于野外采集环境限制的原因,图像质量不高,此处图像质量指的是分辨率以及图像信噪比,即野外采集的地震数据图像分辨率较低而且图像信噪比也较低,噪声比较大。Specifically, the seismic image to be identified is obtained. Usually, the image quality of the seismic data image obtained is not high due to the limitation of the field acquisition environment. Here, the image quality refers to the resolution and image signal-to-noise ratio, that is, the seismic data image collected in the field The resolution is low and the signal-to-noise ratio of the image is also low, and the noise is relatively large.
步骤120,将所述待识别地震图像输入质量提升模型,输出提升后地震图像;
其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成。Wherein, the quality improvement model is obtained after training on a first training set and a first verification set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training set and the The first validation set is generated by simulation with random parameters.
具体地,将低质量的待识别地震图像首先输入质量提升模型进行质量提升,输出的提升后地震图像相对于质量提升前的图像有更高的分辨率和更大的信噪比,而质量提升模型是基于大量的样本低质量地震图像和对应的高质量地震图像标签进行训练得到的,训练过程中训练数据分为训练集和验证集,在训练集完成训练后,使用验证集进行验证以挑选出网络参数最优的模型,此处需要对低质量地震图像和高质量地震图像进行说明,此处的低质量和高质量分别指图像分辨率低于第一阈值和图像分辨率高于第二阈值的图像,以及图像信噪比低于第三阈值和高于第四阈值的图像,而第一阈值、第二阈值、第三阈值和第四阈值是基于应用场景设定的。而且,本发明实施例中的第一训练集和第一验证集都是通过随机参数模拟生成的,不需要人工标注,节约人力成本。Specifically, the low-quality seismic image to be identified is first input into the quality improvement model for quality improvement, and the output seismic image after upgrading has higher resolution and larger signal-to-noise ratio than the image before quality improvement, while the quality improvement The model is trained based on a large number of sample low-quality seismic images and corresponding high-quality seismic image labels. During the training process, the training data is divided into a training set and a verification set. After the training set is completed, the verification set is used for verification to select The model with the optimal network parameters is obtained. Here, low-quality seismic images and high-quality seismic images need to be explained. The low-quality and high-quality seismic images here mean that the image resolution is lower than the first threshold and the image resolution is higher than the second threshold. The images with the threshold, and the images with the image signal-to-noise ratio lower than the third threshold and higher than the fourth threshold, and the first threshold, the second threshold, the third threshold and the fourth threshold are set based on the application scenario. Moreover, both the first training set and the first verification set in the embodiment of the present invention are generated through random parameter simulation, which does not require manual labeling and saves labor costs.
步骤130,将所述提升后地震图像输入断层识别模型,输出断层概率图;
其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。Wherein, the fault recognition model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels.
具体地,待识别地震图像完成质量提升后,就输入至断层识别模型,模型输出对应的断层概率图,而断层识别模型是基于大量的样本地震图像和对应的断层概率图标签进行训练得到的,训练过程中训练数据和质量提升模型一样也分为训练集和验证集,在训练集完成训练后,使用验证集进行验证以挑选出网络参数最优的模型。Specifically, after the quality improvement of the seismic image to be recognized is completed, it is input to the fault recognition model, and the model outputs the corresponding fault probability map, and the fault recognition model is trained based on a large number of sample seismic images and the corresponding fault probability map labels. During the training process, the training data is also divided into a training set and a verification set like the quality improvement model. After the training set is trained, the verification set is used for verification to select the model with the optimal network parameters.
本发明提供的方法,通过获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。实现了获得更干净清晰的断层概率图,预测的断层位置更加准确。In the method provided by the present invention, by acquiring the seismic image to be identified; inputting the seismic image to be identified into a quality improvement model, and outputting the enhanced seismic image; wherein, the quality improvement model is based on a plurality of sample low-quality seismic images and corresponding The first training set and the first verification set constructed by high-quality seismic image labels are obtained after training, and the first training set and the first verification set are generated by random parameter simulation; the improved seismic images are input into the fault The identification model outputs a fault probability map; wherein, the fault identification model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels. A cleaner and clearer fault probability map is achieved, and the predicted fault position is more accurate.
基于上述实施例,该方法中,还包括:Based on the foregoing embodiments, the method also includes:
将所述待识别地震图像输入所述断层识别模型,输出地震图像直接识别结果;input the seismic image to be identified into the fault identification model, and output the direct identification result of the seismic image;
比较所述断层概率图和所述地震图像直接识别结果。The fault probability map is compared with the seismic image direct identification result.
具体地,在完成待识别地震图像进过质量提升模型和断层识别模型的两层输入输出之后,将得到的断层概率图和将待识别地震图像直接输入断层识别模型输出的地震图像直接识别结果进行比较,实验结果可以发现经过质量提升模型得到的断层概率图在断层显示处更清晰,断层显示效果更好。Specifically, after completing the input and output of the seismic image to be identified through the two layers of the quality improvement model and the fault identification model, the obtained fault probability map and the direct identification result of the seismic image output by directly inputting the seismic image to be identified into the fault identification model are carried out. Compared with the experimental results, it can be found that the fault probability map obtained by the quality improvement model is clearer at the fault display, and the fault display effect is better.
基于上述实施例,该方法中,所述第一训练集和所述第一验证集通过随机参数模拟生成,具体包括:Based on the above embodiments, in this method, the first training set and the first verification set are generated through random parameter simulation, specifically including:
按照预设规则生成地层反射率模型;Generate formation reflectance models according to preset rules;
通过随机参数控制生成不同类型的地层反射率模型;Generate different types of formation reflectance models through random parameter control;
将任一地层反射率模型与低频雷克子波卷积并添加随机噪音的地震数据进行两倍的下采样获得样本低质量地震图像;Convolve any stratum reflectivity model with low-frequency Reich wavelets and add random noise to the seismic data for twice down-sampling to obtain sample low-quality seismic images;
将所述任一地层反射率模型与高频雷克子波卷积获得对应的高质量地震图像标签。The corresponding high-quality seismic image labels are obtained by convolving any stratum reflectivity model with the high-frequency Rake wavelet.
具体地,通过随机参数模拟来生成训练数据。首先,随机生成一个具有水平层的反射率模型。然后,向水平层模型中添加随机垂直扰动来模拟地层倾角。接下来,再向模型中随机添加高斯扰动以模拟褶皱构造。紧接着,通过向模型中添加体积向量场扰动来模拟真实中的断层分布。至此,可以通过随机参数控制生成不同类型的地层反射率模型。最后,将反射率模型与低频雷克子波卷积并添加随机噪音的地震数据进行两倍的下采样获得低质量的样本地震图像,同时将该反射率模型与高频雷克子波卷积获得对应的高质量地震图像标签。Specifically, training data is generated by random parameter simulation. First, a reflectance model with horizontal layers is randomly generated. Then, random vertical perturbations are added to the horizontal layer model to simulate formation dip. Next, Gaussian perturbations were randomly added to the model to simulate the fold structure. Next, the real fault distribution is simulated by adding volumetric vector field perturbations to the model. So far, different types of formation reflectance models can be generated through stochastic parameter control. Finally, the reflectivity model is convolved with the low-frequency Reich wavelet and the seismic data with random noise added is twice down-sampled to obtain a low-quality sample seismic image, and the reflectivity model is convolved with the high-frequency Reich wavelet to obtain a corresponding High-quality seismic image tags.
基于上述实施例,该方法中,所述质量提升模型在训练过程中的网络结构为生成对抗网络,所述生成对抗网络包含一个生成器和一个判别器,其中,所述生成器包含一个跳层连接和多个残差连接,所述判别器包含一个卷积层和七个卷积块。Based on the above embodiments, in this method, the network structure of the quality improvement model during training is a generative adversarial network, and the generative adversarial network includes a generator and a discriminator, wherein the generator includes a skip layer connection and multiple residual connections, the discriminator consists of one convolutional layer and seven convolutional blocks.
具体地,神经网络的搭建,基于当前流行的生成对抗网络来搭建地震数据质量提升的网络。该网络结构包含一个生成器和一个判别器。生成器的网络结构包含一个跳层连接和多个残差连接。首先,通过一个卷积层和参数化ReLU(PReLU)激活函数提取低质量地震图像的浅层特征。然后,五个具有残差连接的卷积块被用来提取深层特征。随后,通过跳层连接对浅层和深层特征进行拼接融合。亚像素卷积层被用来特征图进行两倍的上采样,以获得与高质量地震图像相同的大小的输出图像。最后,使用一层卷积层和Tanh激活函数来预测高质量的地震图像。与生成器相比,判别器的网络结构更简单,因为它只需要确定输入图像为真实高质量地震图像的概率。判别器网络包含一个卷积层和七个卷积块。每个卷积块由一个卷积层、一个批处理规范化层(BN)和一个Leaky ReLU激活函数组成。最后,进行两个全连接层(Dense)和一个Sigmoid激活函数来预测输入图像是真正的高质量地震图像的概率。通过生成器和判别的对抗学习有助于生成器生成更逼真的高质量地震图像。Specifically, the construction of the neural network is based on the current popular generative adversarial network to build a network for improving the quality of seismic data. The network structure consists of a generator and a discriminator. The network structure of the generator consists of a skip connection and multiple residual connections. First, shallow features of low-quality seismic images are extracted through a convolutional layer and a parameterized ReLU (PReLU) activation function. Then, five convolutional blocks with residual connections are used to extract deep features. Subsequently, the shallow and deep features are spliced and fused through layer-skip connections. Sub-pixel convolutional layers are used to upsample the feature maps by a factor of two to obtain an output image of the same size as a high-quality seismic image. Finally, a convolutional layer and Tanh activation function are used to predict high-quality seismic images. Compared with the generator, the discriminator has a simpler network structure because it only needs to determine the probability that the input image is a real high-quality seismic image. The discriminator network consists of one convolutional layer and seven convolutional blocks. Each convolutional block consists of a convolutional layer, a batch normalization layer (BN) and a Leaky ReLU activation function. Finally, two fully connected layers (Dense) and a Sigmoid activation function are performed to predict the probability that the input image is a real high-quality seismic image. Adversarial learning via generator and discriminant helps the generator generate more realistic high-quality seismic images.
基于上述实施例,该方法中,所述质量提升模型在训练过程中,所述质量提升模型在验证集上最高峰值信噪比所对应的网络参数为最优模型参数,其中,所述峰值信噪比为生成对抗网络重构高质量地震图像和真实高质量地震图像之间的相似度。Based on the above embodiments, in this method, during the training process of the quality improvement model, the network parameter corresponding to the highest peak signal-to-noise ratio of the quality improvement model on the verification set is the optimal model parameter, wherein the peak signal-to-noise ratio The noise ratio is the similarity between the reconstructed high-quality seismic image by the generative adversarial network and the real high-quality seismic image.
具体地,通过交替优化生成器和判别器,生成器为了欺骗判别器从而生成更贴近真实的高质量地震图像,而判别器通过优化来提升鉴别生成器生成的图像和真实高质量图像的能力。使用Adam优化器来更新生成器和判别器网络参数。峰值信噪比用来表征重构图像与原始真实高质量地震图像的相似度,通过查看模型在训练集和验证集上随着迭代次数的增加峰值信噪比的变化来确定最优模型,模型在验证集上最高峰值信噪比所对应的网络参数被保存下来。Specifically, by alternately optimizing the generator and the discriminator, the generator generates high-quality seismic images that are closer to reality in order to deceive the discriminator, while the discriminator is optimized to improve the ability to distinguish between the image generated by the generator and the real high-quality image. Use the Adam optimizer to update the generator and discriminator network parameters. The peak signal-to-noise ratio is used to characterize the similarity between the reconstructed image and the original real high-quality seismic image. The optimal model is determined by looking at the peak signal-to-noise ratio of the model on the training set and validation set as the number of iterations increases. The model The network parameters corresponding to the highest PSNR on the validation set are saved.
基于上述实施例,该方法中,所述断层识别模型的训练过程中使用的模型网络结构为U-net网络,所述U-net网络包括编码分支和解码分支,所述编码分支由四次卷积操作和下采样组成,所述解码分支由四个上采样和卷积操作组成。Based on the above-mentioned embodiment, in this method, the model network structure used in the training process of the fault recognition model is a U-net network, and the U-net network includes an encoding branch and a decoding branch, and the encoding branch is composed of a quadratic volume The decoding branch consists of four upsampling and convolution operations.
具体地,将断层识别看成是图像语义分割任务,待识别图像中的每个采样点可以分为断层和非断层两种类型。使用语义分割中常用的U-net作为断层识别网络,U-net分为编码分支和解码分支,编码分支由四次卷积操作和下采样组成,解码分支由四个上采样和卷积操作组成。跳层连接将解码分支的上采样后的特征图与编码分支卷积后的特征图进行融合。最后,一个卷积核大小为1×1的卷积层和Sigmoid激活函数被用来输出预测的断层概率图,其大小与输入待识别图像相同。Specifically, fault recognition is regarded as an image semantic segmentation task, and each sampling point in the image to be recognized can be divided into two types: fault and non-fault. U-net, which is commonly used in semantic segmentation, is used as the fault recognition network. U-net is divided into an encoding branch and a decoding branch. The encoding branch consists of four convolution operations and downsampling, and the decoding branch consists of four upsampling and convolution operations. . Layer-skip connections fuse the upsampled feature maps of the decoding branch with the convolved feature maps of the encoding branch. Finally, a convolutional layer with a kernel size of 1×1 and a Sigmoid activation function are used to output the predicted tomographic probability map, which is the same size as the input image to be recognized.
基于上述实施例,该方法中,所述断层识别模型训练过程的损失函数中为表示断层的像素赋予的权重大于为表示非断层的像素赋予的权重。Based on the above embodiment, in the method, in the loss function of the fault recognition model training process, the weight assigned to pixels representing faults is greater than the weight assigned to pixels representing non-faults.
具体地,由于正样本(断层像素)远少于负样本(非断层像素),断层语义分割存在强烈的样本不平衡。为了解决这个问题,使用focal损失,给表示断层的像素更高的权重,如下所示:Specifically, there is a strong sample imbalance in fault semantic segmentation since positive samples (fault pixels) are much less than negative samples (non-fault pixels). To solve this problem, a focal loss is used to give higher weights to pixels representing faults, as follows:
其中,N表示断层概率图中的像素数,yi表示真实的二进制标签(0代表非断层,1代表断层),pi表示断层识别网络的预测概率。用来调整正负样本的损失值权重,用来控制样本学习难度的权重。根据我们的实验经验,在本实施例中,设定α=0.9,γ=2。where N represents the number of pixels in the fault probability map, yi represents the true binary label (0 for non-fault, 1 for fault), and pi represents the predicted probability of the fault identification network. It is used to adjust the weight of loss value of positive and negative samples, and is used to control the weight of sample learning difficulty. According to our experimental experience, in this embodiment, α=0.9 and γ=2 are set.
下面对本发明提供的基于深度学习的高分辨率断层的自动识别装置进行描述,下文描述的基于深度学习的高分辨率断层的自动识别装置与上文描述的基于深度学习的高分辨率断层的自动识别方法可相互对应参照。The following describes the automatic identification device for high-resolution tomograms based on deep learning provided by the present invention, the automatic identification device for high-resolution tomograms based on deep learning described below is the same as the automatic identification device for high-resolution tomograms based on deep learning The identification methods can be referred to in correspondence with each other.
图2为本发明提供的基于深度学习的高分辨率断层的自动识别装置的结构示意图,如图2所示,该装置包括获取单元210、质量提升单元220和断层识别单元230,其中,Fig. 2 is a schematic structural diagram of an automatic identification device for high-resolution faults based on deep learning provided by the present invention. As shown in Fig. 2, the device includes an
所述获取单元210,用于获取待识别地震图像;The acquiring
所述质量提升单元220,用于将所述待识别地震图像输入质量提升模型,输出提升后地震图像;The
其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;Wherein, the quality improvement model is obtained after training on a first training set and a first verification set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training set and the The first verification set is generated by random parameter simulation;
所述断层识别单元230,用于将所述提升后地震图像输入断层识别模型,输出断层概率图;The
其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。Wherein, the fault recognition model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels.
本发明提供的装置,通过获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。实现了获得更干净清晰的断层概率图,预测的断层位置更加准确。The device provided by the present invention obtains the seismic image to be identified; inputs the seismic image to be identified into a quality improvement model, and outputs the enhanced seismic image; wherein, the quality improvement model is based on a plurality of samples of low-quality seismic images and corresponding The first training set and the first verification set constructed by high-quality seismic image labels are obtained after training, and the first training set and the first verification set are generated by random parameter simulation; the improved seismic images are input into the fault The identification model outputs a fault probability map; wherein, the fault identification model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels. A cleaner and clearer fault probability map is achieved, and the predicted fault position is more accurate.
基于上述实施例,该装置中,还包括对比单元,用于:Based on the foregoing embodiments, the device also includes a comparison unit for:
将所述待识别地震图像输入所述断层识别模型,输出地震图像直接识别结果;input the seismic image to be identified into the fault identification model, and output the direct identification result of the seismic image;
比较所述断层概率图和所述地震图像直接识别结果。The fault probability map is compared with the seismic image direct identification result.
基于上述实施例,该装置中,所述第一训练集和所述第一验证集通过随机参数模拟生成,具体包括:Based on the above embodiment, in this device, the first training set and the first verification set are generated through random parameter simulation, specifically including:
按照预设规则生成地层反射率模型;Generate formation reflectance models according to preset rules;
通过随机参数控制生成不同类型的地层反射率模型;Generate different types of formation reflectance models through random parameter control;
将任一地层反射率模型与低频雷克子波卷积并添加随机噪音的地震数据进行两倍的下采样获得样本低质量地震图像;Convolve any stratum reflectivity model with low-frequency Reich wavelets and add random noise to the seismic data for twice down-sampling to obtain sample low-quality seismic images;
将所述任一地层反射率模型与高频雷克子波卷积获得对应的高质量地震图像标签。The corresponding high-quality seismic image labels are obtained by convolving any stratum reflectivity model with the high-frequency Rake wavelet.
基于上述实施例,该装置中,所述质量提升模型在训练过程中的网络结构为生成对抗网络,所述生成对抗网络包含一个生成器和一个判别器,其中,所述生成器包含一个跳层连接和多个残差连接,所述判别器包含一个卷积层和七个卷积块。Based on the above embodiment, in this device, the network structure of the quality improvement model during training is a generative adversarial network, the generative adversarial network includes a generator and a discriminator, wherein the generator includes a skip layer connection and multiple residual connections, the discriminator consists of one convolutional layer and seven convolutional blocks.
基于上述实施例,该装置中,所述质量提升模型在训练过程中,所述质量提升模型在验证集上最高峰值信噪比所对应的网络参数为最优模型参数,其中,所述峰值信噪比为生成对抗网络重构高质量地震图像和真实高质量地震图像之间的相似度。Based on the above embodiment, in this device, during the training process of the quality improvement model, the network parameter corresponding to the highest peak signal-to-noise ratio of the quality improvement model on the verification set is the optimal model parameter, wherein the peak signal-to-noise ratio The noise ratio is the similarity between the reconstructed high-quality seismic image by the generative adversarial network and the real high-quality seismic image.
基于上述实施例,该装置中,所述断层识别模型的训练过程中使用的模型网络结构为U-net网络,所述U-net网络包括编码分支和解码分支,所述编码分支由四次卷积操作和下采样组成,所述解码分支由四个上采样和卷积操作组成。Based on the above-mentioned embodiment, in this device, the model network structure used in the training process of the fault recognition model is a U-net network, and the U-net network includes an encoding branch and a decoding branch, and the encoding branch consists of a quadratic volume The decoding branch consists of four upsampling and convolution operations.
基于上述实施例,该装置中,所述断层识别模型训练过程的损失函数中为表示断层的像素赋予的权重大于为表示非断层的像素赋予的权重。Based on the above-mentioned embodiment, in the device, in the loss function of the fault recognition model training process, the weights assigned to pixels representing faults are greater than the weights assigned to pixels representing non-faults.
基于上述实施例,本发明提供了一种基于合成地震数据利用生成对抗网络提升地震图像分辨率和降噪的方法,包括以下步骤:Based on the above embodiments, the present invention provides a method for improving the resolution and noise reduction of seismic images based on synthetic seismic data using a generative countermeasure network, including the following steps:
S1.构建训练数据集、搭建地震数据质量提升神经网络、训练和验证神经网络;S1. Construct training data set, build seismic data quality improvement neural network, train and verify neural network;
S2.构建训练数据集、搭建断层识别神经网络、训练和验证神经网络;S2. Construct a training data set, build a fault recognition neural network, train and verify the neural network;
S3.基于地震数据质量提升神经网络和断层识别神经网络建立高分辨率断层自动识别端到端的工作流;S3. Establish an end-to-end workflow for automatic identification of high-resolution faults based on the seismic data quality improvement neural network and fault identification neural network;
S4.将野外真实地震数据直接送入断层识别网络进行断层识别和经过本文提出的方法进行断层识别的结果进行对比,验证本文提出的方法的有效性。S4. The real seismic data in the field are directly sent to the fault identification network for fault identification and the results of fault identification by the method proposed in this paper are compared to verify the effectiveness of the method proposed in this paper.
具体的,S1中,可分为三个阶段。第一阶段是训练数据集的建立,使用的是监督学习的深度学习方法,为了建立低质量地震数据到高质量地震数据的映射关系,需要大量的低质量地震数据以及它们对应的标签(高质量地震数据)用于训练,然而,在现实中收集这样的数据是十分困难的。因此,通过随机参数模拟来生成训练数据。首先,随机生成一个具有水平层的反射率模型。然后,向水平层模型中添加随机垂直扰动来模拟地层倾角。接下来,再向模型中随机添加高斯扰动以模拟褶皱构造。紧接着,通过向模型中添加体积向量场扰动来模拟真实中的断层分布。至此,可以通过随机参数控制生成不同类型的地层反射率模型。最后,将反射率模型与低频雷克子波卷积并添加随机噪音的地震数据进行两倍的下采样获得低质量的地震数据,同时将该反射率模型与高频雷克子波卷积获得对应的高质量地震数据。通过不同的随机参数组合,可以生成大量且多样的地震数据对。将低质量的地震数据作为地震数据质量提升神经网络的输入,高质量地震数据作为标签。按照8:2的比例对训练集和验证集进行划分,这就完成了训练数据的准备。第二阶段是神经网络的搭建,图3为本发明提供的地震数据质量提升的神经网络结构图,基于当前流行的生成对抗网络来搭建地震数据质量提升的网络(如图3所示)。该网络结构包含一个生成器和一个判别器。如图3(a)所示,生成器的网络结构包含一个跳层连接和多个残差连接。首先,通过一个卷积层和参数化ReLU(PReLU)激活函数提取低质量地震图像的浅层特征。然后,五个具有残差连接的卷积块(如图3(c)所示)被用来提取深层特征。随后,通过跳层连接对浅层和深层特征进行拼接融合。亚像素卷积层被用来特征图进行两倍的上采样,以获得与高质量地震图像相同的大小的输出图像。最后,使用一层卷积层和Tanh激活函数来预测高质量的地震图像。如图3(b)所示,与生成器相比,判别器的网络结构更简单,因为它只需要确定输入图像为真实高质量地震图像的概率。判别器网络包含一个卷积层和七个卷积块(如图3(d))。每个卷积块由一个卷积层、一个批处理规范化层(BN)和一个Leaky ReLU激活函数组成。最后,进行两个全连接层(Dense)和一个Sigmoid激活函数来预测输入图像是真正的高质量地震图像的概率。通过生成器和判别的对抗学习有助于生成器生成更逼真的高质量地震图像。第三阶段是神经网络的训练和验证,生成对抗网络的训练分为生成器的训练和判别器的训练。定义生成器的损失函数为均方误差损失加上感知损失以及对抗损失的和。Specifically, in S1, it can be divided into three stages. The first stage is the establishment of the training data set, which uses the deep learning method of supervised learning. In order to establish the mapping relationship between low-quality seismic data and high-quality seismic data, a large amount of low-quality seismic data and their corresponding labels (high-quality seismic data) are required. Earthquake data) are used for training, however, it is very difficult to collect such data in reality. Therefore, the training data is generated by random parameter simulation. First, a reflectance model with horizontal layers is randomly generated. Then, random vertical perturbations are added to the horizontal layer model to simulate formation dip. Next, Gaussian perturbations were randomly added to the model to simulate the fold structure. Next, the real fault distribution is simulated by adding volumetric vector field perturbations to the model. So far, different types of formation reflectance models can be generated through stochastic parameter control. Finally, the reflectivity model is convolved with the low-frequency Reich wavelet and the seismic data with random noise added is twice down-sampled to obtain low-quality seismic data, and the reflectivity model is convolved with the high-frequency Reich wavelet to obtain the corresponding High-quality seismic data. Through different combinations of random parameters, a large number and variety of seismic data pairs can be generated. The low-quality seismic data is used as the input of the seismic data quality improvement neural network, and the high-quality seismic data is used as the label. The training set and the verification set are divided according to the ratio of 8:2, which completes the preparation of the training data. The second stage is the construction of the neural network. FIG. 3 is a structural diagram of the neural network for improving the quality of seismic data provided by the present invention. The network for improving the quality of seismic data is built based on the currently popular generative adversarial network (as shown in FIG. 3 ). The network structure consists of a generator and a discriminator. As shown in Fig. 3(a), the network structure of the generator consists of a skip connection and multiple residual connections. First, shallow features of low-quality seismic images are extracted through a convolutional layer and a parameterized ReLU (PReLU) activation function. Then, five convolutional blocks with residual connections (as shown in Fig. 3(c)) are used to extract deep features. Subsequently, the shallow and deep features are spliced and fused by layer-skip connections. Sub-pixel convolutional layers are used to upsample the feature maps by a factor of two to obtain an output image of the same size as a high-quality seismic image. Finally, a convolutional layer and Tanh activation function are used to predict high-quality seismic images. As shown in Fig. 3(b), compared with the generator, the network structure of the discriminator is simpler because it only needs to determine the probability that the input image is a real high-quality seismic image. The discriminator network consists of one convolutional layer and seven convolutional blocks (Fig. 3(d)). Each convolutional block consists of a convolutional layer, a batch normalization layer (BN) and a Leaky ReLU activation function. Finally, two fully connected layers (Dense) and a Sigmoid activation function are performed to predict the probability that the input image is a real high-quality seismic image. Adversarial learning via generator and discriminant helps the generator generate more realistic high-quality seismic images. The third stage is the training and verification of the neural network. The training of the generative confrontation network is divided into the training of the generator and the training of the discriminator. The loss function of the definition generator is the sum of the mean square error loss plus the perceptual loss and the adversarial loss.
对生成器的损失定义如下:The loss for the generator is defined as follows:
lG=lMSE+0.6×lVGG+0.1×ladv l G =l MSE +0.6×l VGG +0.1×l adv
式中,lMSE表示像素级的MSE损失,lVGG表示VGG损失,,ladv表示对抗性损失。where lMSE denotes the pixel-level MSE loss, lVGG denotes the VGG loss, and ladv denotes the adversarial loss.
lMSE计算生成的图像和其对应的真实图像逐个像素的均方误差,计算公式如下:lMSE calculates the pixel-by-pixel mean square error between the generated image and its corresponding real image, and the calculation formula is as follows:
式中,W和H表示低分辨率噪声地震图像ILR的维度,t表示从ILR到IHR的分辨率提升的倍数。In the formula, W and H represent the dimensions of low-resolution noisy seismic image ILR, and t represents the multiple of resolution improvement from ILR to IHR.
VGG网络已被证明具有强大的特征提取能力。通过计算生成的图像与真实的图像在VGG网络的特征提取空间的差异来提升生产图像的质量。基于预训练的16层VGG网络(VGG16)定义VGG损失函数。计算由生成器生成的高分辨率无噪声地震图像和其对应的真实图像IHR在VGG16网络内第i层最大池化层之前的第j次卷积(激活函数后)得到的特征图之间的欧氏距离。该欧式距离被用作VGG损失来衡量重建图像与真实图像在VGG网络特征表示空间中的相似度,计算公式如下:VGG networks have been proven to have strong feature extraction capabilities. The quality of the production image is improved by calculating the difference between the generated image and the real image in the feature extraction space of the VGG network. The VGG loss function is defined based on a pre-trained 16-layer VGG network (VGG16). Calculate the relationship between the high-resolution noise-free seismic image generated by the generator and the feature map obtained by the jth convolution (after the activation function) of the corresponding real image IHR before the i-th maximum pooling layer in the VGG16 network Euclidean distance. The Euclidean distance is used as the VGG loss to measure the similarity between the reconstructed image and the real image in the VGG network feature representation space, and the calculation formula is as follows:
式中,φi,j表示在第i个最大值池化层之前提取第j个卷积层(激活函数后)的特征图的操作,Wi,j和Hi,j表示VGG网络每个特征提取层的特征图的尺寸。In the formula, φ i,j represents the operation of extracting the feature map of the j-th convolutional layer (after the activation function) before the i-th maximum pooling layer, W i,j and H i,j represent each The dimension of the feature map of the feature extraction layer.
为了欺骗判别器,生成器尽可能地学习真实的高分辨率无噪声地震图像的分布。因此,根据判别器将重构的地震图像识别为真实高分辨率无噪声图像的概率来定义ladv:To fool the discriminator, the generator learns the distribution of true high-resolution noise-free seismic images as much as possible. Therefore, ladv is defined in terms of the probability that the discriminator recognizes the reconstructed seismic image as a true high-resolution noise-free image:
式中,N代表低分辨率有噪声样本的数量,表示判别器将由生成器重建的地震图像视为真正的高分辨率无噪声地震图像的概率。where N represents the number of low-resolution noisy samples, and represents the probability that the discriminator regards the seismic image reconstructed by the generator as a true high-resolution noise-free seismic image.
判别器的损失只有对抗损失。通过交替优化生成器和判别器,生成器为了欺骗判别器从而生成更贴近真实的高质量地震数据,而判别器通过优化来提升鉴别生成器生成的图像和真实高质量图像的能力。使用Adam优化器来更新生成器和判别器网络参数。峰值信噪比用来表征重构图像与原始图像的相似度,图4为本发明提供的地震数据质量提升神经网络的训练过程示意图,如图4所示,通过查看模型在训练集和验证集上随着迭代次数的增加峰值信噪比的变化来确定最优模型,模型在验证集上最高峰值信噪比所对应的网络参数被保存下来用于模型后续的预测任务。The loss of the discriminator is only the adversarial loss. By alternately optimizing the generator and the discriminator, the generator is designed to deceive the discriminator to generate more realistic high-quality seismic data, while the discriminator is optimized to improve the ability to distinguish between the image generated by the generator and the real high-quality image. Use the Adam optimizer to update the generator and discriminator network parameters. The peak signal-to-noise ratio is used to characterize the similarity between the reconstructed image and the original image. Fig. 4 is a schematic diagram of the training process of the seismic data quality improvement neural network provided by the present invention. As the number of iterations increases, the peak signal-to-noise ratio changes to determine the optimal model, and the network parameters corresponding to the highest peak signal-to-noise ratio of the model on the verification set are saved for subsequent prediction tasks of the model.
需要说明的是,在本发明中,随机给定的高地震质量的地震数据的峰值频率范围在30-55hz,低质量地震数据的峰值频率范围在10-30HZ。同时给定低质量地震数据中随机噪音的信噪比范围为3-13。对于训练数据的划分比例,按照80%用于训练,20%用于验证的准则进行划分的。在其他实施例中,可以使用其他参数进行数据的模拟以及训练数据集的划分。It should be noted that, in the present invention, the randomly given high-quality seismic data has a peak frequency in the range of 30-55 Hz, and the low-quality seismic data has a peak frequency in the range of 10-30 Hz. Also given the signal-to-noise ratio of random noise in low-quality seismic data ranges from 3 to 13. For the division ratio of training data, it is divided according to the criterion of 80% for training and 20% for verification. In other embodiments, other parameters may be used for data simulation and training data set division.
具体的,S2中,也可分为三个阶段。第一阶段是训练数据集的建立,使用(Wu etal.,2020)公开的数据集按照8:2的比例准备训练数据集和验证数据集。接下来是第二阶段断层识别神经网络的搭建,将断层识别看成是图像语义分割任务,地震图像中的每个采样点可以分为断层和非断层两种类型。图5为本发明提供的地震断层识别神经网络结构图,使用语义分割中常用的U-net作为断层识别网络,其网络结构如图5所示,U-net分为编码分支和解码分支,编码分支由四次卷积操作和下采样组成,解码分支由四个上采样和卷积操作组成。跳层连接将解码分支的上采样后的特征图与编码分支卷积后的特征图进行融合。最后,一个卷积核大小为1×1的卷积层和Sigmoid激活函数被用来输出预测的断层概率图,其大小与输入地震图像相同。第三阶段是断层识别网络的训练和验证,由于正样本(断层像素)远少于负样本(非断层像素),断层语义分割存在强烈的样本不平衡。为了解决这个问题,使用focal损失,给表示断层的像素更高的权重,定义断层识别网络的损失函数Lfault如下所示:Specifically, in S2, it can also be divided into three stages. The first stage is the establishment of the training data set, using the public data set (Wu et al., 2020) to prepare the training data set and the verification data set in a ratio of 8:2. Next is the construction of the fault recognition neural network in the second stage. Fault recognition is regarded as an image semantic segmentation task. Each sampling point in the seismic image can be divided into two types: fault and non-fault. Fig. 5 is the structural diagram of the neural network structure for earthquake fault recognition provided by the present invention, using U-net commonly used in semantic segmentation as the fault recognition network, its network structure as shown in Fig. 5, U-net is divided into encoding branch and decoding branch, encoding The branch consists of four convolution operations and downsampling, and the decoding branch consists of four upsampling and convolution operations. Layer-skip connections fuse the upsampled feature maps of the decoding branch with the convolved feature maps of the encoding branch. Finally, a convolutional layer with a kernel size of 1 × 1 and a sigmoid activation function are used to output the predicted fault probability map, which is the same size as the input seismic image. The third stage is the training and verification of the fault recognition network. Since the positive samples (fault pixels) are far less than the negative samples (non-fault pixels), there is a strong sample imbalance in fault semantic segmentation. In order to solve this problem, the focal loss is used to give higher weight to the pixels representing the fault, and the loss function L fault of the fault recognition network is defined as follows:
其中,N表示断层概率图中的像素数,yi表示真实的二进制标签(0代表非断层,1代表断层),pi表示断层识别网络的预测概率。用来调整正负样本的损失值权重,用来控制样本学习难度的权重。根据的实验经验,在本发明中,设定α=0.9,γ=2。where N represents the number of pixels in the fault probability map, yi represents the true binary label (0 for non-fault, 1 for fault), and pi represents the predicted probability of the fault identification network. It is used to adjust the weight of loss value of positive and negative samples, and is used to control the weight of sample learning difficulty. According to experimental experience, in the present invention, α=0.9 and γ=2 are set.
在训练时,对输入的地震数据进行了归一化处理以消除数据分布差异的影响,并执行了图像翻转和旋转的数据增广操作以增强模型的鲁棒性。Adam优化器用来更新网络的参数。图6为本发明提供的断层识别神经网络的训练过程示意图,断层识别网络的训练过程如图6所示,将模型在验证集上最小损失对应的网络参数被保存下来用于模型后续的断层预测任务。在其他实施例中,可以使用其他的优化器、训练参数、损失函数和数据增强方式。During training, the input seismic data were normalized to eliminate the influence of data distribution differences, and data augmentation operations of image flipping and rotation were performed to enhance the robustness of the model. The Adam optimizer is used to update the parameters of the network. Figure 6 is a schematic diagram of the training process of the fault recognition neural network provided by the present invention. The training process of the fault recognition network is shown in Figure 6, and the network parameters corresponding to the minimum loss of the model on the verification set are saved for subsequent fault prediction of the model Task. In other embodiments, other optimizers, training parameters, loss functions and data augmentation methods may be used.
具体的,S3中,构建了一个野外地震数据高分辨率断层识别端到端的工作流。图7为本发明提供的高分辨率断层智能识别端到端的工作流示意图,该工作流由S1和S2中训练好的神经网络串联而成,首先原始的地震图像(如图7(a)所示)被送入S1中训练好的数据质量提升网络进行分辨率的提升和随机噪音的抑制,得到高质量的地震数据(如图7(b)所示),然后将经过数据质量提升的数据送入S2中的断层识别网络进行断层自动识别,获得高分辨率的断层识别结果(如图7(c)所示)。该工作流有效地整合了地震数据质量提升和断层识别这两个任务,通过提出的方法,可以直接从原始的低频充满随机噪声的地震图像中获得高精度的断层识别结果。Specifically, in S3, an end-to-end workflow for high-resolution fault identification of field seismic data is constructed. Fig. 7 is a schematic diagram of the end-to-end workflow of high-resolution fault intelligent identification provided by the present invention. The workflow is formed by the neural network trained in S1 and S2 in series. First, the original seismic image (as shown in Fig. 7(a) shown) is sent to the trained data quality improvement network in S1 for resolution improvement and random noise suppression to obtain high-quality seismic data (as shown in Figure 7(b)), and then the improved data quality It is sent to the fault recognition network in S2 for automatic fault recognition, and high-resolution fault recognition results are obtained (as shown in Fig. 7(c)). This workflow effectively integrates the two tasks of seismic data quality improvement and fault identification. Through the proposed method, high-precision fault identification results can be obtained directly from the original low-frequency seismic images full of random noise.
具体的,S4中,对比常规的方法,既直接将原始的地震图像送入断层识别网络进行断层识别的结果与提出的先对地震图像进行质量提升再进行断层识别的结果进行对比以验证提出的新方法的有效性和先进性。首先,将收集的到野外地震数据直接送入S2中训练好的断层识别网络进行断层识别。然后,采用S3中提出的工作流,对野外地震数据先进行数据质量提升再进行断层识别。最后,对比上述两种方式的断层识别结果。本发明中,图8为本发明提供的在野外真实地震数据上的应用以及与传统实现流程的对比示意图,先利用S1中训练有素的地震数据质量提升网络对原始地震图像(如图8(a)所示)进行质量提升。图8(b)是经过质量提升后的地震图像,与原始地震图像相比,随机噪音得到了明显的压制,并且一些高频的地质特征,如断层和薄层等,肉眼更容易观察。接着,将质量提升后的地震图像喂给训练好的断层识别网络,得到高分辨率的断层识别结果(如图8(d)所示)。将该结果与常规断层识别流程(将原始地震图像(如图8(a)所示)直接喂给断层识别网络)的结果(如图8(b)所示)进行对比,提出的高分辨率断层识别方法识别出的断层更加精细、干净,识别的噪音干扰较少。此外,一些相邻较近的断层,直接通过原始图像进行识别时,识别的结果都混到一起了,而经过提出的方法进行识别的结果这些断层都分离开了,且得到了很好的表征。更重要的是,由于原始地震图像的分辨率较低且包含很多随机噪音,一些小尺度的断层没有被识别出来,而经过地震数据质量提升后,这些小尺度的断层都被断层识别神经网络很好的检测出来了。进一步对比图8(d)和图8(b),的方法识别的断层相较于传统方法,断层线更加尖锐,这将提供更准确的断层位置信息,对于后续准确的地质建模工作有着巨大的帮助。这些都表明了提出的高分辨率断层识别新方法相较于现有的断层识别方法的优越性,尤其是对于复杂地质条件下低质量的地震数据。Specifically, in S4, compared with the conventional method, the result of directly sending the original seismic image into the fault identification network for fault identification is compared with the proposed result of first improving the quality of the seismic image and then performing fault identification to verify the proposed The effectiveness and advancement of the new method. First, the collected field seismic data are directly sent to the fault identification network trained in S2 for fault identification. Then, using the workflow proposed in S3, the data quality of the field seismic data is first improved and then the fault identification is performed. Finally, compare the fault identification results of the above two methods. In the present invention, Fig. 8 is a schematic diagram of the application on the real seismic data in the field provided by the present invention and the comparison with the traditional implementation process. First, the well-trained seismic data quality improvement network in S1 is used to improve the original seismic image (as shown in Fig. 8 ( a) Shown) for quality improvement. Figure 8(b) is the improved seismic image. Compared with the original seismic image, the random noise has been significantly suppressed, and some high-frequency geological features, such as faults and thin layers, are easier to observe with the naked eye. Then, the quality-improved seismic images are fed to the trained fault identification network to obtain high-resolution fault identification results (as shown in Fig. 8(d)). Comparing this result with the result of the conventional fault identification process (feeding the raw seismic image (as shown in Figure 8(a)) directly to the fault identification network) (as shown in Figure 8(b)), the proposed high-resolution The faults identified by the fault identification method are more refined and clean, and the identified noise interference is less. In addition, when some adjacent faults are directly recognized through the original image, the recognition results are mixed together, but the recognition results of the proposed method are separated and well-characterized . More importantly, some small-scale faults were not identified due to the low resolution of the original seismic image and the high random noise contained in it. However, after the quality of the seismic data was improved, these small-scale faults were detected by the neural network for fault identification. OK detected. Further comparing Fig. 8(d) and Fig. 8(b), the fault identified by the method is sharper than the traditional method, which will provide more accurate fault location information, which has great potential for subsequent accurate geological modeling. s help. These all show the superiority of the proposed new method for high-resolution fault identification compared to existing fault identification methods, especially for low-quality seismic data under complex geological conditions.
本发明基于深度学习技术提出了一个端到端的高分辨断层自动识别工作流程。该工作流程包含两个神经网络,一个用于地震图像的质量提升,另一个用于地震断层识别。首先,将原始地震图像输入训练有素的地震数据质量提升网络,获得高质量的地震图像。然后将其输入训练有素的断层识别网络,得到高分辨率的断层识别结果。由于实际中缺乏大量的有标签的地震数据用于神经网络的训练和验证,为了克服这个问题,通过随机参数模拟技术获得了大量的训练数据。然后,将提出的方法应用于野外真实地震数据,结果表明,该方法在没有任何人工标注数据的条件下,可以从原始地震图像中获得高精度的断层识别结果。并且将提出的方法与常规的断层识别方法进行对比,结果表明,提出的高分辨率断层识别方法识别出的断层更加精细、干净,识别的噪音干扰较少。此外,一些相邻较近的断层,直接通过原始图像进行识别时,识别的结果都混到一起了,而经过提出的方法进行识别的结果这些断层都分离开了,且得到了很好的表征。更重要的是,由于原始地震图像的分辨率较低且包含很多随机噪音,一些小尺度的断层没有被识别出来,而经过地震数据质量提升后,这些小尺度的断层都被断层识别神经网络很好的检测出来了。这意味着的方法会提供更准确的断层位置信息,对于后续准确的地质建模工作有着巨大的帮助。这些都表明了提出的高分辨率断层识别新方法相较于现有的断层识别方法的优越性,尤其是对于复杂地质条件下低质量的地震数据。The present invention proposes an end-to-end high-resolution fault automatic identification workflow based on deep learning technology. The workflow consists of two neural networks, one for seismic image quality enhancement and the other for seismic fault identification. First, the raw seismic images are fed into a well-trained seismic data quality improvement network to obtain high-quality seismic images. It is then fed into a well-trained fault recognition network to obtain high-resolution fault recognition results. Due to the lack of a large amount of labeled seismic data for the training and verification of the neural network, in order to overcome this problem, a large amount of training data is obtained through random parameter simulation technology. Then, the proposed method is applied to real seismic data in the field, and the results show that the method can obtain high-precision fault identification results from raw seismic images without any artificially labeled data. And the proposed method is compared with the conventional fault identification method. The results show that the proposed high-resolution fault identification method identifies faults that are more precise and clean, and the identified noise interference is less. In addition, when some adjacent faults are directly recognized through the original image, the recognition results are mixed together, but the recognition results of the proposed method are separated and well-characterized . More importantly, some small-scale faults were not identified due to the low resolution of the original seismic image and the high random noise contained in it. However, after the quality of the seismic data was improved, these small-scale faults were detected by the neural network for fault identification. OK detected. This means that the method will provide more accurate fault location information, which is of great help to the subsequent accurate geological modeling work. These all show the superiority of the proposed new method for high-resolution fault identification compared to existing fault identification methods, especially for low-quality seismic data under complex geological conditions.
图9示例了一种电子设备的实体结构示意图,如图9所示,该电子设备可以包括:处理器(processor)910、通信接口(Communications Interface)920、存储器(memory)930和通信总线940,其中,处理器910,通信接口920,存储器930通过通信总线940完成相互间的通信。处理器910可以调用存储器930中的逻辑指令,以执行基于深度学习的高分辨率断层的自动识别方法,该方法包括:获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。FIG. 9 illustrates a schematic diagram of the physical structure of an electronic device. As shown in FIG. 9, the electronic device may include: a processor (processor) 910, a communication interface (Communications Interface) 920, a memory (memory) 930, and a
此外,上述的存储器930中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。In addition, the above-mentioned logic instructions in the
另一方面,本发明还提供一种计算机程序产品,所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序,所述计算机程序包括程序指令,当所述程序指令被计算机执行时,计算机能够执行上述各方法所提供的基于深度学习的高分辨率断层的自动识别方法方法,该方法包括:获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。On the other hand, the present invention also provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer During execution, the computer can execute the deep learning-based automatic identification method of high-resolution faults provided by the above methods, the method includes: acquiring the seismic image to be identified; inputting the seismic image to be identified into the quality improvement model, and outputting the improved post-seismic image; wherein, the quality improvement model is obtained after training on a first training set and a first verification set constructed based on a plurality of sample low-quality seismic images and corresponding high-quality seismic image labels, and the first training The set and the first verification set are generated by random parameter simulation; the lifted seismic image is input into a fault identification model, and a fault probability map is output; wherein, the fault identification model is based on a plurality of sample seismic images and corresponding fault probability obtained after training on the second training set and the second verification set constructed with graph labels.
又一方面,本发明还提供一种非暂态计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现以执行上述各提供的基于深度学习的高分辨率断层的自动识别方法方法,该方法包括:获取待识别地震图像;将所述待识别地震图像输入质量提升模型,输出提升后地震图像;其中,所述质量提升模型是基于多个样本低质量地震图像和对应的高质量地震图像标签构建的第一训练集和第一验证集进行训练后得到的,所述第一训练集和所述第一验证集通过随机参数模拟生成;将所述提升后地震图像输入断层识别模型,输出断层概率图;其中,所述断层识别模型是基于多个样本地震图像和对应的断层概率图标签构建的第二训练集和第二验证集进行训练后得到的。In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to perform the above-mentioned high-resolution tomography based on deep learning. An automatic identification method, the method comprising: acquiring a seismic image to be identified; inputting the seismic image to be identified into a quality improvement model, and outputting the enhanced seismic image; wherein, the quality improvement model is based on a plurality of samples of low-quality seismic images and The first training set and the first verification set constructed by corresponding high-quality seismic image labels are obtained after training, and the first training set and the first verification set are generated by random parameter simulation; A fault identification model is input, and a fault probability map is output; wherein, the fault identification model is obtained after training on a second training set and a second verification set constructed based on a plurality of sample seismic images and corresponding fault probability map labels.
以上所描述的装置实施例仅仅是示意性的,其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下,即可以理解并实施。The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without any creative effort.
通过以上的实施方式的描述,本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现,当然也可以通过硬件。基于这样的理解,上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品可以存储在计算机可读存储介质中,如ROM/RAM、磁碟、光盘等,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。Through the above description of the implementations, those skilled in the art can clearly understand that each implementation can be implemented by means of software plus a necessary general hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.
最后应说明的是:以上实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.
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