Application of neural nets to seismic signal analysis (original) (raw)
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A New View on The Processing of Seismic Data With Artificial Neural Networks
2019
Artificial Intelligence, which works on the ability to learn in machines, has a widespread field of research. One of the most researched topics of artificial intelligence is artificial neural networks. Artificial neural networks are effective today with the solution of complex problems, calculation and processing of information. Seismic method, which is one of the basic applications of geophysical field, is widely used especially for the detection of oil by using seismic waves. With the literature review, it is seen that the types of artificial neural network architectures are used. It has been determined that different methods are used in the processing of seismic data. Using the convolutional neural network (CNN), one of the artificial neural network architectures, it is aimed to achieve success in oil detection by seismic waves.
Neural networks and discrimination of seismic signals
Computers & Geosciences, 1995
Recent developments in algorithms and computer architecture make neural networks a useful tool in designing pattern recognition systems. We show how a simple multilayer perceptron with 23 neurons can be trained easily and used to classify seismic signals. Applied to broadband seismic signal, the perceptron permitted the recognition of different types of events on the basis of their frequency. Applied to a real-time, automatic, seismic data acquisition system, it saved more than 50% CPU time in a detection procedure.
Neural Networks in Seismic Discrimination
Monitoring a Comprehensive Test Ban Treaty, 1996
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A neural networks based seismic object detection technique
SEG Technical Program Expanded Abstracts 2005, 2005
Seismic technology has historically focused on resolving the structures. To a lesser degree, presence or absence of reservoir has also been a focus. This paper shows the use of seismic data to detect several other "seismic objects". The technique to be referred to as "meta attribute", uses the combination of "artificial intelligence" of neural networks and the "natural intelligence" of an interpreter. Examples of many geologic features and reservoir properties detected using this technique will be provided. They include hydrocarbon probability, lithofacies, chimney, faults and salt.
Discrimination of Seismic Signals Using Artificial Neural Networks
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2007
The automatic discrimination of seismic signals is an important practical goal for earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, two classes of seismic signals recorded routinely in geophysical laboratory of the National Center for Scientific and Technical Research in Morocco are considered. They correspond to signals associated to local earthquakes and chemical explosions. The approach adopted for the development of an automatic discrimination system is a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "modified Mexican hat wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
Neural networks for geophysicists and their application to seismic data interpretation
The Leading Edge, 2019
There has been a surge of interest in neural networks for the interpretation of seismic images over the last few years. Network-based learning methods can provide fast and accurate automatic interpretation, provided that there are many training labels. We provide an introduction to the field for geophysicists who are familiar with the framework of forward modeling and inversion. We explain the similarities and differences between deep networks and other geophysical inverse problems and show their utility in solving problems such as lithology interpolation between wells, horizon tracking, and segmentation of seismic images. The benefits of our approach are demonstrated on field data from the Sea of Ireland and the North Sea.
Artificial Neural Networks as Emerging Tools for Earthquake Detection
Computación y Sistemas
As seismic networks continue to spread and monitoring sensors become more efficient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identification, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recent advances are related to the application of Artificial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.
arXiv (Cornell University), 2020
Over the past decades, the Groningen Gas Field (GGF) has been increasingly faced by induced earthquakes resulting from gas production. The seismic monitoring network at Groningen has been recently densified to improve the seismic network performance, resulting in increasing amounts of seismic data. Although traditional automated event detection techniques generally are successful in detecting events from continuous data, its detection success is challenged in cases of lower signal-tonoise ratios. The data stream coming from these networks has initiated specific interest in neural networks for automated classification and interpretation. Here, we explore the feasibility of neural networks in detecting the occurrence of seismic events. For this purpose, a three-layered feedforward neural network was trained using public data of a seismic event in the GGF obtained from the Royal Netherlands Meteorological Institute (KNMI) data portal. The first arrival times and duration of earthquake waveforms determined by KNMI for a subset of the station data, were used to detect the arrival times and event duration for the other uninterpreted station data. Subsequently various attributes were used as input for the neural network, that were based on different short term averaging/long term averaging (STA/LTA) and frequency sub-band settings. Using these input data, the network's parameters were iteratively improved to maximize its capability in successfully discriminating seismic events from noise and determine the event duration. Results show an increase of 65% in accurately detecting seismic events and determining their duration as compared to the reference method. This clears the way for improved interpretation of signal waveforms and automated seismic event classification in the Groningen area.