CN107784276A - Microseismic event recognition methods and device - Google Patents
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Abstract
本发明提供了一种微震事件识别方法和装置,所述方法包括:S1,基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;S2,组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;S3,基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。通过预设卷积神经网络提取待识别事件各通道的波形特征,再将各通道的波形特征组合为一个整体即组合波形特征,输入预设支持向量机模型实现对待识别事件的分类,最终实现对事件中微震事件的识别。实现了微震事件的自动识别,不依赖操作人员的知识水平和经验,准确度高,不受应用场景的影响,泛化能力强。
The present invention provides a microseismic event identification method and device, the method comprising: S1, based on the waveform images of each channel of the event to be identified, using a preset convolutional neural network model to extract the waveform features of each channel of the event to be identified; S2, combining the waveform features of all channels of the event to be identified to obtain the combined waveform feature of the event to be identified; S3, based on the combined waveform feature of the event to be identified, using a preset support vector machine model to analyze the event to be identified sort. The waveform features of each channel of the event to be identified are extracted through the preset convolutional neural network, and then the waveform features of each channel are combined into a whole, that is, the combined waveform feature, and the preset support vector machine model is input to realize the classification of the event to be identified, and finally realize the Identification of microseismic events in events. The automatic identification of microseismic events is realized without relying on the knowledge level and experience of operators, with high accuracy, not affected by application scenarios, and strong generalization ability.
Description
技术领域technical field
本发明实施例涉及地球物理探测技术领域,更具体地,涉及一种微震事件识别方法和装置。Embodiments of the present invention relate to the technical field of geophysical detection, and more specifically, to a method and device for identifying microseismic events.
背景技术Background technique
微震是指震级小于三级的地震,这一类地震人们一般不能感觉到,只能使用特定仪器进行监测。微震会给地下的介质产生相应的激励,这样的激烈可能改变地下介质的力学状态。在矿山安全监测领域,微震作为矿山动力灾害的前兆,对微震进行实时监测,可有效预测和预防动力灾害的发生。另外在页岩气、煤层气等非常规油气勘探开发过程中,基于微震的裂缝监测技术已成为国内外压裂裂纹监测最准确的技术之一。在微震监测过程中,监测仪器接收到的信号除了由微震事件产生的微震信号,往往还包括爆破事件、噪声时间等产生的信号,因此,在微震监测过程中,从众多事件中识别出微震事件是监测的基础。Microearthquakes refer to earthquakes with a magnitude less than three. People generally cannot feel such earthquakes and can only be monitored with specific instruments. Microseisms will generate corresponding excitations to the underground medium, and such intensity may change the mechanical state of the underground medium. In the field of mine safety monitoring, microseisms are the precursor of mine dynamic disasters, and real-time monitoring of microseisms can effectively predict and prevent the occurrence of dynamic disasters. In addition, during the exploration and development of unconventional oil and gas such as shale gas and coalbed methane, microseismic-based fracture monitoring technology has become one of the most accurate technologies for fracturing crack monitoring at home and abroad. In the process of microseismic monitoring, in addition to the microseismic signals generated by microseismic events, the signals received by monitoring instruments often include signals generated by blasting events, noise time, etc. Therefore, in the process of microseismic monitoring, microseismic events can be identified from many events is the basis for monitoring.
微震事件具有单事件多通道的特点,具体地,在微震发生时,通常会引起多个传感器的触发,这些传感器所对应的通道将会采集并存储相应的波形数据。这样由一个微震事件引起的,多个通道触发采集得到的波形,称之为单事件多通道波形。目前,微震事件主要是通过信息技术人员进行识别,微地震监测仪器接收多个通道传送的大量信号,技术人员通过一定的信号处理手段提取这些信号的波形特征,再根据这些波形特征,利用理论知识和实践经验判断接收到的信号对应的事件是否为微震事件。A microseismic event has the characteristics of single event and multiple channels. Specifically, when a microseismic event occurs, multiple sensors are usually triggered, and the channels corresponding to these sensors will collect and store corresponding waveform data. Such a waveform caused by a microseismic event and triggered by multiple channels is called a single-event multi-channel waveform. At present, microseismic events are mainly identified by information technology personnel. Microseismic monitoring instruments receive a large number of signals transmitted by multiple channels, and technicians extract the waveform characteristics of these signals through certain signal processing methods. Based on practical experience, it is judged whether the event corresponding to the received signal is a microseismic event.
但是,采用手工提取信号波形特征进而识别微震事件的方法,工作量大,依赖操作人员的知识水平和经验,且根据人工提取的波形特征,识别的准确度低,一般只适用于特定的应用场景,泛化能力差。However, the method of manually extracting signal waveform features to identify microseismic events requires a large workload and depends on the knowledge and experience of the operator, and according to the manually extracted waveform features, the recognition accuracy is low, and it is generally only suitable for specific application scenarios , poor generalization ability.
发明内容Contents of the invention
针对现有技术中微震事件识别技术存在的工作量大,识别的准确度低,以及泛化能力差的问题。本发明实施例提供了一种克服上述问题或者至少部分地解决上述问题的微震事件识别方法和装置。Aiming at the problems of heavy workload, low recognition accuracy and poor generalization ability in the microseismic event recognition technology in the prior art. Embodiments of the present invention provide a microseismic event identification method and device for overcoming the above problems or at least partially solving the above problems.
一方面本发明实施例提供了一种微震事件识别方法,所述方法包括:On the one hand, the embodiment of the present invention provides a method for identifying microseismic events, the method comprising:
S1,基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;S1, based on the waveform image of each channel of the event to be identified, using a preset convolutional neural network model to extract the waveform features of each channel of the event to be identified;
S2,组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;S2. Combining the waveform features of all channels of the event to be identified to obtain the combined waveform feature of the event to be identified;
S3,基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。S3. Based on the combined waveform features of the event to be identified, use a preset support vector machine model to classify the event to be identified.
其中,在步骤S1之前还包括:Wherein, before step S1 also includes:
将所述待识别事件各通道的波形信号转化为图像形式,并经过预处理得到所述波形图像。The waveform signal of each channel of the event to be identified is converted into an image form, and the waveform image is obtained through preprocessing.
其中,步骤S1具体包括:Wherein, step S1 specifically includes:
将所述待识别事件各通道的波形图像分别随机裁剪为若干个小图块,利用所述预设卷积神经网络模型分别提取每个小图块的波形特征;The waveform images of each channel of the event to be identified are respectively randomly cut into several small blocks, and the waveform features of each small block are respectively extracted by using the preset convolutional neural network model;
求所述待识别事件各通道对应的所述若干个小图块的波形特征的均值得到所述待识别事件各通道的波形特征。Calculate the mean value of the waveform features of the several small tiles corresponding to each channel of the event to be identified to obtain the waveform feature of each channel of the event to be identified.
其中,步骤S2具体包括:Wherein, step S2 specifically includes:
将所述待识别事件每个通道的波形特征转换为1×M维度,其中M为大于零的整数;Converting the waveform feature of each channel of the event to be identified into a 1×M dimension, where M is an integer greater than zero;
将所述待识别事件所有通道的波形特征进行组合得到N×M维度的组合波形特征,其中N为所述待识别事件的通道数。Combine the waveform features of all channels of the event to be identified to obtain a combined waveform feature of N×M dimensions, where N is the number of channels of the event to be identified.
其中,所述预设卷积神经网络模型通过以下步骤获得:Wherein, the preset convolutional neural network model is obtained through the following steps:
构建卷积神经网络,其中,所述卷积神经网络的输入为所述待识别事件的波形图像,全连接层后连接softmax层;Construct a convolutional neural network, wherein the input of the convolutional neural network is the waveform image of the event to be identified, and the softmax layer is connected after the fully connected layer;
利用第一训练数据集对所述卷积神经网络进行训练得到训练好的卷积神经网络;其中,所述第一训练数据集包括所述待识别事件的波形图像以及对应的事件类别。Using the first training data set to train the convolutional neural network to obtain a trained convolutional neural network; wherein the first training data set includes the waveform image of the event to be identified and the corresponding event category.
其中,所述预设支持向量机模型通过以下步骤获得:Wherein, the preset support vector machine model is obtained through the following steps:
构建支持向量机,所述支持向量机的输入量维度为N×M;Constructing a support vector machine, the input dimension of the support vector machine is N×M;
利用第二训练数据集对所述支持向量机进行训练,得到预设支持向量机模型;其中,所述第二数据集包括所述待识别事件的组合波形特征以及对应的事件的类别。The support vector machine is trained by using the second training data set to obtain a preset support vector machine model; wherein the second data set includes the combined waveform features of the event to be identified and the corresponding event category.
其中,所述预设支持向量机模型包括多个输入量维度N不同的支持向量机。Wherein, the preset support vector machine model includes a plurality of support vector machines with different input dimensions N.
另一方面本发明实施例提供了一种微震事件识别装置,所述装置包括:On the other hand, an embodiment of the present invention provides a device for identifying microseismic events, the device comprising:
特征提取模块,用于基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;The feature extraction module is used to extract the waveform features of each channel of the event to be identified based on the waveform image of each channel of the event to be identified using a preset convolutional neural network model;
特征组合模块,用于组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;A feature combination module, configured to combine the waveform features of all channels of the event to be identified to obtain the combined waveform feature of the event to be identified;
识别模块,用于基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。The identification module is configured to classify the event to be identified based on the combined waveform features of the event to be identified by using a preset support vector machine model.
第三方面本发明实施例提供了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,使所述计算机执行所述微震事件识别方法。In the third aspect, an embodiment of the present invention 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 When executed by a computer, the computer is made to execute the microseismic event identification method.
第四方面本发明实施例提供了一种非暂态计算机可读存储介质,所述非暂态计算机可读存储介质存储计算机指令,所述计算机指令使所述计算机执行所述微震事件识别方法。In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the microseismic event identification method.
本发明实施例提供的一种微震事件识别方法和装置,通过预设卷积神经网络提取待识别事件各通道的波形特征,再将各通道的波形特征组合为一个整体即组合波形特征,输入预设支持向量机模型实现对待识别事件的分类,最终实现对事件中微震事件的识别。实现了微震事件的自动识别,不依赖操作人员的知识水平和经验,准确度高,不受应用场景的影响,泛化能力强。The embodiment of the present invention provides a method and device for identifying microseismic events. The waveform features of each channel of the event to be identified are extracted through a preset convolutional neural network, and then the waveform features of each channel are combined into a whole, that is, the combined waveform feature. The support vector machine model is set to classify the events to be identified, and finally realize the identification of microseismic events in the event. The automatic identification of microseismic events is realized without relying on the knowledge level and experience of operators, with high accuracy, not affected by application scenarios, and strong generalization ability.
附图说明Description of drawings
图1为本发明实施例提供的一种微震事件识别方法的流程图;Fig. 1 is a flow chart of a method for identifying microseismic events provided by an embodiment of the present invention;
图2为本发明实施例中获取所述预设卷积神经网络模型和预设支持向量机模型的示意图;2 is a schematic diagram of obtaining the preset convolutional neural network model and preset support vector machine model in an embodiment of the present invention;
图3为本发明实施例提供的一种微震事件识别装置的结构框图。Fig. 3 is a structural block diagram of a device for identifying microseismic events provided by an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚地描述,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 embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are the Some, but not all, embodiments are invented. 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.
图1为本发明实施例提供的一种微震事件识别方法的流程图,如图1所示,所述方法包括:S1,基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;S2,组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;S3,基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。Fig. 1 is a flowchart of a microseismic event recognition method provided by an embodiment of the present invention. As shown in Fig. 1, the method includes: S1, based on the waveform images of each channel of the event to be recognized, using a preset convolutional neural network model Extracting the waveform features of each channel of the event to be identified; S2, combining the waveform features of all channels of the event to be identified to obtain the combined waveform feature of the event to be identified; S3, based on the combined waveform feature of the event to be identified, using The preset support vector machine model classifies the event to be recognized.
卷积神经网络(Convolutional Neural Networks,CNN)是近年发展起来,并引起广泛重视的一种高效识别方法。一般地,CNN的基本结构包括两层,其一为特征提取层,每个神经元的输入与前一层的局部接受域相连,并提取该局部的特征。一旦该局部特征被提取后,它与其它特征间的位置关系也随之确定下来;其二是特征映射层,网络的每个计算层由多个特征映射组成,每个特征映射是一个平面,平面上所有神经元的权值相等。特征映射结构采用非线性激活函数作为卷积神经网络的激活函数,使得特征映射具有位移不变性,优选地,可采用Relu激活函数。此外,由于一个映射面上的神经元共享权值,因而减少了网络自由参数的个数。卷积神经网络中的每一个卷积层都紧跟着一个用来求局部平均与二次提取的计算层,这种特有的两次特征提取结构减小了特征分辨率。Convolutional Neural Networks (CNN) is an efficient recognition method that has been developed in recent years and has attracted widespread attention. Generally, the basic structure of CNN includes two layers, one is the feature extraction layer, the input of each neuron is connected to the local receptive field of the previous layer, and the local features are extracted. Once the local feature is extracted, the positional relationship between it and other features is also determined; the second is the feature map layer, each calculation layer of the network is composed of multiple feature maps, each feature map is a plane, All neurons on the plane have equal weights. The feature map structure uses a nonlinear activation function as the activation function of the convolutional neural network, so that the feature map has displacement invariance. Preferably, the Relu activation function can be used. In addition, since neurons on a mapping plane share weights, the number of free parameters of the network is reduced. Each convolutional layer in the convolutional neural network is followed by a calculation layer for local averaging and secondary extraction. This unique two-time feature extraction structure reduces the feature resolution.
CNN主要用来识别位移、缩放及其他形式扭曲不变性的二维图形。由于CNN的特征检测层通过训练数据进行学习,所以在使用CNN时,避免了显示的特征抽取,而隐式地从训练数据中进行学习;再者由于同一特征映射面上的神经元权值相同,所以网络可以并行学习,这也是卷积网络相对于神经元彼此相连网络的一大优势。卷积神经网络以其局部权值共享的特殊结构在语音识别和图像处理方面有着独特的优越性,其布局更接近于实际的生物神经网络,权值共享降低了网络的复杂性,特别是多维输入向量的图像可以直接输入网络这一特点避免了特征提取和分类过程中数据重建的复杂度。CNNs are primarily used to identify two-dimensional graphics that are invariant to displacement, scaling, and other forms of distortion. Since the feature detection layer of CNN learns through training data, when using CNN, it avoids the explicit feature extraction, and learns implicitly from the training data; moreover, because the weights of neurons on the same feature map are the same , so the network can learn in parallel, which is also a major advantage of the convolutional network over the network of neurons connected to each other. Convolutional neural network has unique advantages in speech recognition and image processing with its special structure of local weight sharing. Its layout is closer to the actual biological neural network. Weight sharing reduces the complexity of the network, especially multi-dimensional The feature that the image of the input vector can be directly input into the network avoids the complexity of data reconstruction in the process of feature extraction and classification.
但由于微震监测技术中的信号具有单事件多通道的特点,若采用卷积神经网络对微震事件进行识别,由于每个事件的通道数不同,需要构建并训练多个不同的卷积神经网络。卷积神经网络的训练耗时较长,无法满足快速识别微震事件的要求。However, since the signal in the microseismic monitoring technology has the characteristics of single event and multiple channels, if the convolutional neural network is used to identify microseismic events, since the number of channels for each event is different, it is necessary to construct and train multiple different convolutional neural networks. The training of convolutional neural network takes a long time, which cannot meet the requirements of quickly identifying microseismic events.
支持向量机支持向量机(Support Vector Machine,SVM)是Cortes和Vapnik于1995年首先提出的,它在解决小样本、非线性及高维模式识别中表现出许多特有的优势,并能够推广应用到函数拟合等其他机器学习问题中。支持向量机方法是建立在统计学习理论的VC维理论和结构风险最小原理基础上的,根据有限的样本信息在模型的复杂性(即对特定训练样本的学习精度,Accuracy)和学习能力(即无错误地识别任意样本的能力)之间寻求最佳折衷,以期获得最好的推广能力(或称泛化能力)。Support Vector Machine Support Vector Machine (Support Vector Machine, SVM) was first proposed by Cortes and Vapnik in 1995. It shows many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition, and can be extended to Function fitting and other machine learning problems. The support vector machine method is based on the VC dimension theory of statistical learning theory and the principle of structural risk minimization, according to the complexity of the model (that is, the learning accuracy for specific training samples, Accuracy) and learning ability (that is, The ability to identify any sample without error) seeks the best compromise in order to obtain the best generalization ability (or generalization ability).
支持向量机的构建和训练十分便捷,所以在对单事件多通道事件进行识别过程中,针对通道数的数的事件我们只需要构建和训练相应维度的支持向量机来进行分类即可。支持向量机很好的实现了对不同通道数的事件进行分类的同时,避免了使用卷积神经网络进行事件分类时构建和训练卷积神经网络耗时长的问题。The construction and training of support vector machines is very convenient, so in the process of identifying single-event and multi-channel events, we only need to build and train support vector machines of corresponding dimensions to classify events with the number of channels. The support vector machine realizes the classification of events with different channel numbers well, and at the same time avoids the time-consuming problem of constructing and training the convolutional neural network when using the convolutional neural network for event classification.
具体地,在微震监测中,一个事件中接收到多个通道的波形信号,对应于多个波形图像。利用所述预设卷积神经网络模型提取各通道的波形特征后,将各通道的波形特征进行组合得到所述待识别事件的组合波形特征,这样就将所述待识别事件各通道的波形特征组合成为一个整体作为所述预设向量机的输入,实现对所述待识别事件的分类,判断待识别事件是否为微震事件。Specifically, in microseismic monitoring, waveform signals of multiple channels are received in one event, corresponding to multiple waveform images. After using the preset convolutional neural network model to extract the waveform features of each channel, the waveform features of each channel are combined to obtain the combined waveform features of the event to be identified, so that the waveform features of each channel of the event to be identified Combined into a whole as the input of the preset vector machine, it realizes the classification of the event to be identified, and judges whether the event to be identified is a microseismic event.
本发明实施例提供的一种微震事件识别方法,通过预设卷积神经网络提取待识别事件各通道的波形特征,再将各通道的波形特征组合为一个整体即组合波形特征,输入预设支持向量机模型实现对待识别事件的分类,最终实现对事件中微震事件的识别。实现了微震事件的自动识别,不依赖操作人员的知识水平和经验,准确度高,不受应用场景的影响,泛化能力强。A microseismic event recognition method provided by an embodiment of the present invention extracts the waveform features of each channel of the event to be identified through a preset convolutional neural network, and then combines the waveform features of each channel into a whole, that is, the combined waveform feature, and inputs the preset support The vector machine model realizes the classification of the events to be identified, and finally realizes the identification of microseismic events in the event. The automatic identification of microseismic events is realized without relying on the knowledge level and experience of operators, with high accuracy, not affected by application scenarios, and strong generalization ability.
在上述实施例中,在步骤S1之前还包括:In the above embodiment, before step S1, it also includes:
将所述待识别事件各通道的波形信号转化为图像形式,并经过预处理得到所述波形图像。The waveform signal of each channel of the event to be identified is converted into an image form, and the waveform image is obtained through preprocessing.
在上述实施例中,步骤S1具体包括:In the above embodiment, step S1 specifically includes:
将所述待识别事件各通道的波形图像分别随机裁剪为若干个小图块,利用所述预设卷积神经网络模型分别提取每个小图块的波形特征;The waveform images of each channel of the event to be identified are respectively randomly cut into several small blocks, and the waveform features of each small block are respectively extracted by using the preset convolutional neural network model;
求所述待识别事件各通道对应的所述若干个小图块的波形特征的均值得到所述待识别事件各通道的波形特征。Calculate the mean value of the waveform features of the several small tiles corresponding to each channel of the event to be identified to obtain the waveform feature of each channel of the event to be identified.
优选地,可以将各通道的波形图像随机裁剪为10个小图块。Preferably, the waveform image of each channel can be randomly cropped into 10 small tiles.
本发明实施例通过将各通道的波形图像随机裁剪后分别提取波形特征,再求取这些小图块的均值特征,可以使提取到的各通道的波形特征更加准确。In the embodiment of the present invention, the waveform features of each channel can be extracted more accurately by randomly cutting out the waveform images of each channel and extracting the waveform features respectively, and then calculating the mean value features of these small blocks.
在上述实施例中,步骤S2具体包括:In the above embodiment, step S2 specifically includes:
将所述待识别事件每个通道的波形特征转换为1×M维度,其中M为大于零的整数;Converting the waveform feature of each channel of the event to be identified into a 1×M dimension, where M is an integer greater than zero;
将所述待识别事件所有通道的波形特征进行组合得到N×M维度的组合波形特征,其中N为所述待识别事件的通道数。Combine the waveform features of all channels of the event to be identified to obtain a combined waveform feature of N×M dimensions, where N is the number of channels of the event to be identified.
在上述实施例中,所述预设卷积神经网络模型通过以下步骤获得:In the above embodiment, the preset convolutional neural network model is obtained through the following steps:
构建卷积神经网络,其中,所述卷积神经网络的输入为所述待识别事件的波形图像,全连接层后连接softmax层,输出为所述待识别事件的波形特征;Construct a convolutional neural network, wherein the input of the convolutional neural network is the waveform image of the event to be identified, the fully connected layer is connected to the softmax layer, and the output is the waveform feature of the event to be identified;
利用第一训练数据集对所述卷积神经网络进行训练得到训练好的卷积神经网络;其中,所述第一训练数据集包括所述待识别事件的波形图像以及对应的事件类别。Using the first training data set to train the convolutional neural network to obtain a trained convolutional neural network; wherein the first training data set includes the waveform image of the event to be identified and the corresponding event category.
其中,所述softmax层用于对输入卷积神经网络的波形图片对应的事件进行分类,在训练过程中需要通过分类结果来判断分类的准确性,以确定是否得出训练好的卷积神经网络。在得到训练好的卷积神经网络后,因为只需要利用训练好的卷积神经网络对波形图像进行特征提取,在识别微震事件时,直接在训练好的卷积神经网络相应的卷积层中提取待检测时间各通道的波形特征即可,而不需要利用训练好的神经网络中softmax层对待检测时间的分类结果。那么,预设卷积神经网络模型既利用了卷积神经网络提取波形图形波形特征的功能,也避免了利用softmax层进行分类时,只适用于特定通道数的事件的问题。Wherein, the softmax layer is used to classify the events corresponding to the waveform pictures input to the convolutional neural network. During the training process, it is necessary to judge the accuracy of the classification through the classification results to determine whether to obtain the trained convolutional neural network. . After getting the trained convolutional neural network, because it is only necessary to use the trained convolutional neural network to extract the features of the waveform image, when identifying microseismic events, directly in the corresponding convolutional layer of the trained convolutional neural network It is enough to extract the waveform features of each channel at the time to be detected, instead of using the classification results of the softmax layer of the trained neural network at the time to be detected. Then, the preset convolutional neural network model not only utilizes the function of the convolutional neural network to extract waveform features of waveform graphics, but also avoids the problem that it is only applicable to events with a specific number of channels when using the softmax layer for classification.
另外,如图2所示,在对卷积神经网络进行训练时,选取已知事件中典型的波形图像对卷积神经网络进行训练,例如,选取3万张已知事件的波形图像对卷积神经网络进行训练,得到训练好的卷积神经网络的分类精度可达95.13%。In addition, as shown in Figure 2, when training the convolutional neural network, select typical waveform images of known events to train the convolutional neural network, for example, select 30,000 waveform images of known events to convolute The neural network is trained, and the classification accuracy of the trained convolutional neural network can reach 95.13%.
在上述实施例中,如图2所示,所述预设支持向量机模型通过以下步骤获得:In the above embodiment, as shown in Figure 2, the preset support vector machine model is obtained through the following steps:
构建支持向量机,所述支持向量机的输入量维度为N×M;Constructing a support vector machine, the input dimension of the support vector machine is N×M;
利用第二训练数据集对所述支持向量机进行训练,得到预设支持向量机模型;其中,所述第二数据集包括所述待识别事件的组合波形特征以及对应的事件的类别。The support vector machine is trained by using the second training data set to obtain a preset support vector machine model; wherein the second data set includes the combined waveform features of the event to be identified and the corresponding event category.
其中,构建的支持向量机的输入量的维度由待识别事件的组合特征的维度决定。所述第二数据集中的组合波形特征由所述第一数据集经步骤S1和步骤S2后得到。Wherein, the dimension of the input quantity of the constructed support vector machine is determined by the dimension of the combination feature of the event to be recognized. The combined waveform features in the second data set are obtained from the first data set after step S1 and step S2.
在上述实施例中,所述预设支持向量机模型包括多个输入量维度N不同的支持向量机。In the above embodiment, the preset support vector machine model includes a plurality of support vector machines with different input dimensions N.
由于支持向量机构建和训练的较为便捷,为了便于对不同通道数的事件进行识别,所述预设支持向量机模型可以包括多个支持向量机,每个支持向量机输入量的维度中N值不同,可以对不同通道数的事件进行分类。Due to the relatively convenient construction and training of support vector machines, in order to facilitate the identification of events with different channel numbers, the preset support vector machine model can include a plurality of support vector machines, and the N value in the dimension of each support vector machine input quantity Different, events with different channel numbers can be classified.
本发明实施例提供了一种微震事件识别装置,如图3所示,所述装置包括:特征提取模块1、特征组合模块2以及识别模块3。其中:An embodiment of the present invention provides a device for identifying microseismic events. As shown in FIG. 3 , the device includes: a feature extraction module 1 , a feature combination module 2 and a recognition module 3 . in:
特征提取模块1用于基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;特征组合模块2用于组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;识别模块3用于基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。The feature extraction module 1 is used to extract the waveform features of each channel of the event to be identified based on the waveform image of each channel of the event to be identified using a preset convolutional neural network model; the feature combination module 2 is used to combine all channels of the event to be identified The combined waveform features of the event to be identified are obtained from the waveform features of the event to be identified; the identification module 3 is used to classify the event to be identified by using a preset support vector machine model based on the combined waveform feature of the event to be identified.
由于微震监测技术中的信号具有单事件多通道的特点,若采用卷积神经网络对微震事件进行识别,由于每个事件的通道数不同,需要构建并训练多个不同的卷积神经网络。卷积神经网络的训练耗时较长,无法满足快速识别微震事件的要求。支持向量机的构建和训练十分便捷,所以在对单事件多通道事件进行识别过程中,针对通道数的数的事件我们只需要构建和训练相应维度的支持向量机来进行分类即可。支持向量机很好的实现了对不同通道数的事件进行分类的同时,避免了使用卷积神经网络进行事件分类时构建和训练卷积神经网络耗时长的问题。Since the signal in the microseismic monitoring technology has the characteristics of single event and multiple channels, if the convolutional neural network is used to identify the microseismic event, since the number of channels of each event is different, it is necessary to construct and train multiple different convolutional neural networks. The training of convolutional neural network takes a long time, which cannot meet the requirements of quickly identifying microseismic events. The construction and training of support vector machines is very convenient, so in the process of identifying single-event and multi-channel events, we only need to build and train support vector machines of corresponding dimensions to classify events with the number of channels. The support vector machine realizes the classification of events with different channel numbers well, and at the same time avoids the time-consuming problem of constructing and training the convolutional neural network when using the convolutional neural network for event classification.
具体地,在微震监测中,一个事件中接收到多个通道的波形信号,对应于多个波形图像。利用所述预设卷积神经网络模型提取各通道的波形特征后,将各通道的波形特征进行组合得到所述待识别事件的组合波形特征,这样就将所述待识别事件各通道的波形特征组合成为一个整体作为所述预设向量机的输入,实现对所述待识别事件的分类,判断待识别事件是否为微震事件。Specifically, in microseismic monitoring, waveform signals of multiple channels are received in one event, corresponding to multiple waveform images. After using the preset convolutional neural network model to extract the waveform features of each channel, the waveform features of each channel are combined to obtain the combined waveform features of the event to be identified, so that the waveform features of each channel of the event to be identified Combined into a whole as the input of the preset vector machine, it realizes the classification of the event to be identified, and judges whether the event to be identified is a microseismic event.
本发明实施例提供的一种微震事件识别装置,通过特征提取模块提取待识别事件各通道的波形特征,再通过特征组合模块将各通道的波形特征组合为一个整体即组合波形特征,输入分类模块实现对待识别事件的分类,最终实现对事件中微震事件的识别。实现了微震事件的自动识别,不依赖操作人员的知识水平和经验,准确度高,不受应用场景的影响,泛化能力强。The microseismic event recognition device provided by the embodiment of the present invention uses the feature extraction module to extract the waveform features of each channel of the event to be identified, and then uses the feature combination module to combine the waveform features of each channel into a whole, that is, the combined waveform feature, and input it to the classification module Realize the classification of the events to be identified, and finally realize the identification of microseismic events in the event. The automatic identification of microseismic events is realized without relying on the knowledge level and experience of operators, with high accuracy, not affected by application scenarios, and strong generalization ability.
本发明实施例公开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实施例所提供的方法,例如包括:基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。An embodiment of the present invention discloses 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, The computer can execute the methods provided by the above method embodiments, for example, including: based on the waveform images of each channel of the event to be identified, using a preset convolutional neural network model to extract the waveform features of each channel of the event to be identified; Identifying the waveform features of all channels of the event to obtain the combined waveform feature of the event to be identified; based on the combined waveform feature of the event to be identified, using a preset support vector machine model to classify the event to be identified.
本发明实施例提供一种非暂态计算机可读存储介质,所述非暂态计算机可读存储介质存储计算机指令,所述计算机指令使所述计算机执行上述各方法实施例所提供的方法,例如包括:基于待识别事件各通道的波形图像,利用预设卷积神经网络模型提取所述待识别事件各通道的波形特征;组合所述待识别事件所有通道的波形特征得到所述待识别事件的组合波形特征;基于所述待识别事件的组合波形特征,利用预设支持向量机模型对所述待识别事件进行分类。An embodiment of the present invention provides a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the methods provided in the above method embodiments, for example Including: based on the waveform image of each channel of the event to be identified, using a preset convolutional neural network model to extract the waveform features of each channel of the event to be identified; combining the waveform features of all channels of the event to be identified to obtain the event to be identified Combining waveform features: based on the combined waveform features of the event to be identified, the event to be identified is classified using a preset support vector machine model.
本领域普通技术人员可以理解:实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成,前述的程序可以存储于一计算机可读取存储介质中,该程序在执行时,执行包括上述方法实施例的步骤;而前述的存储介质包括:ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。Those of ordinary skill in the art can understand that all or part of the steps for realizing the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the It includes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other various media that can store program codes.
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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Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109086872A (en) * | 2018-07-30 | 2018-12-25 | 东北大学 | Seismic wave recognizer based on convolutional neural networks |
CN110308485A (en) * | 2019-07-05 | 2019-10-08 | 中南大学 | Microseismic signal classification method, device and storage medium based on deep learning |
CN110322894A (en) * | 2019-06-27 | 2019-10-11 | 电子科技大学 | A kind of waveform diagram generation and giant panda detection method based on sound |
CN110361779A (en) * | 2019-07-14 | 2019-10-22 | 广东石油化工学院 | A kind of microseismic event detection method and system based on chi square distribution |
CN110632662A (en) * | 2019-09-25 | 2019-12-31 | 成都理工大学 | An Algorithm for Automatic Microseismic Signal Recognition Using DCNN-Inception Network |
CN110910613A (en) * | 2019-12-10 | 2020-03-24 | 大连理工大学 | A wireless monitoring and early warning system for rock mass microseismic monitoring |
CN111123355A (en) * | 2020-01-07 | 2020-05-08 | 山东大学 | Rockburst prediction method and system based on microseismic monitoring data |
CN111126471A (en) * | 2019-12-18 | 2020-05-08 | 中国石油大学(华东) | Microseismic event detection method and system |
CN111160516A (en) * | 2018-11-07 | 2020-05-15 | 杭州海康威视数字技术股份有限公司 | Convolutional layer sparsization method and device of deep neural network |
CN114048669A (en) * | 2021-09-27 | 2022-02-15 | 吉林大学 | Hot dry rock microseism event detection method based on neural network |
CN115327613A (en) * | 2022-06-20 | 2022-11-11 | 华北科技学院 | Mine micro-seismic waveform automatic classification and identification method in multilayer multistage mode |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102650702A (en) * | 2012-05-03 | 2012-08-29 | 中国石油天然气股份有限公司 | Seismic waveform analysis and reservoir prediction method and device |
CN106405640A (en) * | 2016-08-26 | 2017-02-15 | 中国矿业大学(北京) | Automatic microseismic signal arrival time picking method based on depth belief neural network |
CN106526668A (en) * | 2016-11-14 | 2017-03-22 | 中国石油化工股份有限公司 | Original waveform extraction and imaging method |
-
2017
- 2017-10-13 CN CN201710955082.9A patent/CN107784276B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102650702A (en) * | 2012-05-03 | 2012-08-29 | 中国石油天然气股份有限公司 | Seismic waveform analysis and reservoir prediction method and device |
CN106405640A (en) * | 2016-08-26 | 2017-02-15 | 中国矿业大学(北京) | Automatic microseismic signal arrival time picking method based on depth belief neural network |
CN106526668A (en) * | 2016-11-14 | 2017-03-22 | 中国石油化工股份有限公司 | Original waveform extraction and imaging method |
Non-Patent Citations (4)
Title |
---|
AUTHOR LINKS OPEN OVERLAY PANELMOHAMED F.ABDELWAHED: "SGRAPH (SeismoGRAPHer): Seismic waveform analysis and integrated tools in seismology", 《COMPUTERS & GEOSCIENCES》 * |
姜福兴 等: "单事件多通道微震波形的特征提取与联合识别研究", 《煤炭学报》 * |
曾建雄 等: "基于支持向量机的矿山微震波形识别和分类研究", 《化工矿物与加工》 * |
谭智勇 等: "基于深度卷积神经网络的人群密度估计方法", 《计算机应用与软件》 * |
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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CN111160516B (en) * | 2018-11-07 | 2023-09-05 | 杭州海康威视数字技术股份有限公司 | Convolutional layer sparsification method and device for deep neural network |
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CN110361779A (en) * | 2019-07-14 | 2019-10-22 | 广东石油化工学院 | A kind of microseismic event detection method and system based on chi square distribution |
CN110632662A (en) * | 2019-09-25 | 2019-12-31 | 成都理工大学 | An Algorithm for Automatic Microseismic Signal Recognition Using DCNN-Inception Network |
CN110910613A (en) * | 2019-12-10 | 2020-03-24 | 大连理工大学 | A wireless monitoring and early warning system for rock mass microseismic monitoring |
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