A New View on The Processing of Seismic Data With Artificial Neural Networks (original) (raw)
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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.
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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.
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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.
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.
Stratigraphic oil trap detection using seismic attributes and neural network
Seismic data interpretation mainly is classified to structural, stratigraphical and lithological interpretation. Reservoir Characterization based on Quantitative and Qualitative seismic data evaluation is very useful and inevitable for reservoir studies, therefore integrating seismic data inversion, seismic attributes and neural network classification will be helpful to detect oil traps. Stratigraphical interpretation evaluates litho facies lateral changes of reservoirs based on high quality of 3D seismic data cube. Evaluation of various time slices related to desired targets using relevant seismic attributes will illuminate strata anomalies. In this study, first appropriate seismic attributes used to detect probable stratigraphic oil trap (indicated by lineament). Instantaneous frequency, instantaneous phase and envelope attributes potentially be able to indicate structural and stratigraphical anomalies. Variance, Chaos, Semblance coherence attributes are completely susceptible to lateral facies changes and RMS Amplitude validates channel shape in reservoir. In the following, stratigraphical facies were classified by neural network based on unsupervised data. To facies classification and analysis by neural network, the maximum correlation is relevant to using instantaneous frequency, variance and envelope, which illuminated channel trend. Finally to confirm stratigraphic oil trap in this reservoir, seismic inversion and porosity estimation were applied.
Earth Science Informatics, 2021
The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new eme...
Application of feedback connection artificial neural network to seismic data filtering
Comptes Rendus Geoscience, 2008
The Elman artificial neural network (ANN) (feedback connection) was used for seismic data filtering. The recurrent connection which characterizes this network offers the advantage of storing values from the previous time step to be used in the current time step. The proposed structure has the advantage of training simplicity by back-propagation algorithm (steepest descent). Several trials were addressed on synthetic (with 10% and 50% of random and Gaussian noise) and real seismic data using respectively 10 to 30 neurons and a minimum of 60 neurons in the hidden layer. Both iteration number up to 4000 and arrest criteria were used to obtain satisfactory performances. Application of such networks on real data show that the filtered seismic section was efficient. Adequate cross-validation test is done to ensure the performance of network on new data sets.
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.