Attenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation . We present a novel filtering method , which aims at reducing random noise while protecting seismic structures . This paper deals with basic principle of coherency cube of the third generation and the application of the coherency cube and image sharpening to seismic data interpretation . Combination of the coherency cube with image sharpening is able to fully use seismic data and to enhance accuracy of seismic data interpretation . Identifying how geoscientists interact with seismic images allows a deeper understanding of their rationale and the seismic interpretation process itself . Moreover , identifying nuances involving seismic interpreters performing slightly different roles opens new possibilities relative to how decision support systems could become part of the seismic interpretation . In seismic reflection prospecting for oil and gas a key step is the ability to estimate the seismic wavelet ( impulse response ) traveling through the Earth . Such estimation enables filters to be designed to deblur the recorded seismic time series and allows the integration of " downhole " and surface seismic data for seismic interpretation purposes . An appropriate model for the seismic time series is a noisy-input/noisy-output linear model . Development of stereographic visualization has opened the third and fourth dimensions in seismic analysis and interpretation . The ability to look , simultaneously , at several data volumes and attributes in a true 3-D sense opens the door to visualization systems applied to regional AVO analysis and volume-based repeatability analysis , as part of the 4-D processing flow . As in his Discussion of our previous seismic interpretation paper , ( Roksandic , 1995 ) , Roksandic offers readers of Geophysics several valuable interpretation concepts in this present comprehensive Discussion . We will address his issues in order and keep our responses as brief as possible . Recently , research community proposed machine learning techniques to extract information from seismic images , aiming at assisting this interpretation process . Although useful , these techniques solve just part of the seismic interpretation problem . They focus on identifying specific features ( e.g. salt diapirs , reservoir facies , mini-basins ) but they fail in identifying and analyzing the spatial correlation among them . This paper presents some algorithms and numerical experiments for different models of media . A special emphasis is given to “ non-ray ” waves which play an important role in seismic interpretation theory . Some formats have been developed to a comprehensive level , which is widely accepted in the seismological community . The IASPEI Seismic Format ( ISF ) was adopted as a standard format for exchanging seismic parameter data by the IASPEI Commission on Seismic Observation and Interpretation . The ISC as well as the NEIC and an increasing number of national data centers publish their catalogues in the ISF format , which also complies with the IMS1.0 format that was developed for the verification of the Comprehensive Nuclear Test Ban Treaty ( CTBT ) . Long-term smart-sensing technologies are emerging as valuable solutions to the increasing need for persistent sensor-based surveillance , that deliver enhanced performance when sensor fields are employed with intelligence implemented at the sensor node . We have developed a baseline technology of movement and seismic detection for perimeter intrusion . The sensor technology system employs computational and signal analysis algorithms with a degree of low power operation , wireless communication , and incident-driven sensor awakening . Under an excitation of 2.75 g intensity , the peak signal-to-noise-plus-distortion ratio ( SNDR ) of the proposed accelerometer was 72 dB , and decreased to 49.1 dB after 3 weeks of continuous shaking . After enabling automatically nulling of the sensor for 0.5 s period of time , the peak SNDR was 75.2 dB. Based on this operational mode , the nulling accelerometer could be applied for usage in IoT devices for seismic detection . The measurement results verify the functions and performance of the proposed accelerometer . This paper examines the use of two characteristic neural network architectures to signal and image processing applications . Digital image halftoning and seismic event detection are treated as optimization problems , to which symmetric Hopfield-type networks with near-neighbor-connections provide efficient solutions . A solution to the halftoning problem , provided by simulated annealing , is also examined and compared to the neural network one . Specifically , we first simulate lossy compression on two real-world seismic data sets , and we then evaluate algorithm performance using implementations on real hardware . In terms of compression ratios , recovered signal error , power consumption , on-mote execution runtime , and classification accuracy of a seismic event detection task ( on decompressed signals ) , results show that CS performs comparable to ( and in many cases better than ) the other algorithms evaluated . A main benefit to users is that CS , a lightweight and nonadaptive compression technique , can guarantee a desired level of compression performance ( and thus , radio usage and power consumption ) without subjugating recovered signal quality . In radio , radar , sonar , and seismic signal detection there is often the problem of processing received signals which have been distorted by a linear operation in the process of being transmitted . Examples are scattering and multiple-path propagation of radio waves . This study , from innovation adoption perspective , is based on the analysis of basic theories of Rayleigh wave . For this principle , identification methods for seismic signal detection were introduced . Typically , a sensing system for ground information measuring involves both time-domain and frequency-domain algorithms . The system can also handle incremental learning from examples . Applications of the proposed system to seismic image analysis are included . > Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention . One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning . As a crucial step before classification , a feature extraction algorithm shall be implemented to transfer data from input space to feature space resulting in dimensionality reduction . In this paper , two feature extraction methods Principal Component Analysis ( PCA ) and Fisher Discriminant Analysis ( FDA ) in seismic object detection are compared . It is aimed to study fluid migration pathways in the North Sea and SVM classifier is used for classification thereafter . MEMS technology has been applied in many fields including deep oil exploration and seismic detection areas , of which poor performance has limited its development until the sigma-delta modulation is ... This article introduces a Portable Testing Instrument of Seismic Detector Base on MSP430F149 , which is an ultra-low power 16-bit micro controller . It is coupled with the μC/OS-II that is multi-tasking real-time operating system . This model is based on the study of the 3D seismic data in addition to 26 well logs , and is grounded on the careful study of the geology of the region . Furthermore , we propose two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available . Finally , we propose a scheme for evaluating different models on this dataset , and we share the results of our baseline models . In this work we describe a machine learning pipeline for facies classification based on wireline logging measurements . The algorithm has been designed to work even with a relatively small training set and amount of features . The experimental results indicate that the ensemble learning performs better than individual single ones . Moreover , the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models . Thin and discontinuous lithostratigraphic units were best resolved using non-preprocessed variables , and segmentation coupled with postregularized RF classifications ( 0.652 ±0.011 ) . These methods may be used to improve the accuracy of classifications across a wide variety of spatial modeling applications . The classification accuracy was evaluated and compared with respect to the sensor type – multi-spectral or radar – and the number of acquisitions needed . Our results showed that very dense time series allowed for very high accuracy classifications ( > 90 % ) of small scale vegetation types . The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed . Accurate and early diagnosis can save lives and reduce the percentage of mortality . This study focuses classification accuracy analysis of ovarian cancer . The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model . In this study , the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index ( EVI2 ) was investigated . Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012 . A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages , i.e. , the transplanting and the vegetative to reproductive transition phases , of the SCR was proposed , and the overall classification accuracy was 91.7 % . Different target types are then tested . Although targets which provide detailed compositional information produce higher accuracies of classification for subsidiary classes , there is a trade off between the added information and added complexity which can decrease classification accuracy . Overall , the results show that the network seems to be able to identify the classes that are present within pixels but not their proportions . Despite its long history , there has been renewed interest in this problem with the advent of high-dimensional and complex data . Specifically , in the machine learning literature , there have been recent methodological developments such as classification accuracy tests . The goal of this work is to present a regression approach to comparing multivariate distributions of complex data . The encoder-decoder neural network architecture has been widely used in image segmentation . In recent years , the same architecture has also been used in seismic surveys for facies classification applications . In this paper , a modified U-Net architecture with trainable soft attention mechanism and dilated convolution is proposed to improve the automatic seismic facies analysis . Characterizing the interpretational uncertainty in facies classification is quite difficult , but it can be very important for reservoir development decisions . Thus , automation of the facies classification process using machine learning is a potentially intuitive and efficient way to facilitate facies interpretation based on large-volume data . It can also enable more adequate quantification of the uncertainty in facies classification by ensuring that possible alternative lithological scenarios are not overlooked .
Attenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation . We present a novel filtering method , which aims at reducing random noise while protecting seismic structures .
This paper deals with basic principle of coherency cube of the third generation and the application of the coherency cube and image sharpening to seismic data interpretation . Combination of the coherency cube with image sharpening is able to fully use seismic data and to enhance accuracy of seismic data interpretation .
Identifying how geoscientists interact with seismic images allows a deeper understanding of their rationale and the seismic interpretation process itself . Moreover , identifying nuances involving seismic interpreters performing slightly different roles opens new possibilities relative to how decision support systems could become part of the seismic interpretation .
In seismic reflection prospecting for oil and gas a key step is the ability to estimate the seismic wavelet ( impulse response ) traveling through the Earth . Such estimation enables filters to be designed to deblur the recorded seismic time series and allows the integration of " downhole " and surface seismic data for seismic interpretation purposes . An appropriate model for the seismic time series is a noisy-input/noisy-output linear model .
Development of stereographic visualization has opened the third and fourth dimensions in seismic analysis and interpretation . The ability to look , simultaneously , at several data volumes and attributes in a true 3-D sense opens the door to visualization systems applied to regional AVO analysis and volume-based repeatability analysis , as part of the 4-D processing flow .
As in his Discussion of our previous seismic interpretation paper , ( Roksandic , 1995 ) , Roksandic offers readers of Geophysics several valuable interpretation concepts in this present comprehensive Discussion . We will address his issues in order and keep our responses as brief as possible .
Recently , research community proposed machine learning techniques to extract information from seismic images , aiming at assisting this interpretation process . Although useful , these techniques solve just part of the seismic interpretation problem . They focus on identifying specific features ( e.g. salt diapirs , reservoir facies , mini-basins ) but they fail in identifying and analyzing the spatial correlation among them .
This paper presents some algorithms and numerical experiments for different models of media . A special emphasis is given to “ non-ray ” waves which play an important role in seismic interpretation theory .
Some formats have been developed to a comprehensive level , which is widely accepted in the seismological community . The IASPEI Seismic Format ( ISF ) was adopted as a standard format for exchanging seismic parameter data by the IASPEI Commission on Seismic Observation and Interpretation . The ISC as well as the NEIC and an increasing number of national data centers publish their catalogues in the ISF format , which also complies with the IMS1.0 format that was developed for the verification of the Comprehensive Nuclear Test Ban Treaty ( CTBT ) .
Long-term smart-sensing technologies are emerging as valuable solutions to the increasing need for persistent sensor-based surveillance , that deliver enhanced performance when sensor fields are employed with intelligence implemented at the sensor node . We have developed a baseline technology of movement and seismic detection for perimeter intrusion . The sensor technology system employs computational and signal analysis algorithms with a degree of low power operation , wireless communication , and incident-driven sensor awakening .
Under an excitation of 2.75 g intensity , the peak signal-to-noise-plus-distortion ratio ( SNDR ) of the proposed accelerometer was 72 dB , and decreased to 49.1 dB after 3 weeks of continuous shaking . After enabling automatically nulling of the sensor for 0.5 s period of time , the peak SNDR was 75.2 dB. Based on this operational mode , the nulling accelerometer could be applied for usage in IoT devices for seismic detection . The measurement results verify the functions and performance of the proposed accelerometer .
This paper examines the use of two characteristic neural network architectures to signal and image processing applications . Digital image halftoning and seismic event detection are treated as optimization problems , to which symmetric Hopfield-type networks with near-neighbor-connections provide efficient solutions . A solution to the halftoning problem , provided by simulated annealing , is also examined and compared to the neural network one .
Specifically , we first simulate lossy compression on two real-world seismic data sets , and we then evaluate algorithm performance using implementations on real hardware . In terms of compression ratios , recovered signal error , power consumption , on-mote execution runtime , and classification accuracy of a seismic event detection task ( on decompressed signals ) , results show that CS performs comparable to ( and in many cases better than ) the other algorithms evaluated . A main benefit to users is that CS , a lightweight and nonadaptive compression technique , can guarantee a desired level of compression performance ( and thus , radio usage and power consumption ) without subjugating recovered signal quality .
In radio , radar , sonar , and seismic signal detection there is often the problem of processing received signals which have been distorted by a linear operation in the process of being transmitted . Examples are scattering and multiple-path propagation of radio waves .
This study , from innovation adoption perspective , is based on the analysis of basic theories of Rayleigh wave . For this principle , identification methods for seismic signal detection were introduced . Typically , a sensing system for ground information measuring involves both time-domain and frequency-domain algorithms .
The system can also handle incremental learning from examples . Applications of the proposed system to seismic image analysis are included . >
Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention . One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning .
As a crucial step before classification , a feature extraction algorithm shall be implemented to transfer data from input space to feature space resulting in dimensionality reduction . In this paper , two feature extraction methods Principal Component Analysis ( PCA ) and Fisher Discriminant Analysis ( FDA ) in seismic object detection are compared . It is aimed to study fluid migration pathways in the North Sea and SVM classifier is used for classification thereafter .
MEMS technology has been applied in many fields including deep oil exploration and seismic detection areas , of which poor performance has limited its development until the sigma-delta modulation is ...
This article introduces a Portable Testing Instrument of Seismic Detector Base on MSP430F149 , which is an ultra-low power 16-bit micro controller . It is coupled with the μC/OS-II that is multi-tasking real-time operating system .
This model is based on the study of the 3D seismic data in addition to 26 well logs , and is grounded on the careful study of the geology of the region . Furthermore , we propose two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available . Finally , we propose a scheme for evaluating different models on this dataset , and we share the results of our baseline models .
In this work we describe a machine learning pipeline for facies classification based on wireline logging measurements . The algorithm has been designed to work even with a relatively small training set and amount of features .
The experimental results indicate that the ensemble learning performs better than individual single ones . Moreover , the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models .
Thin and discontinuous lithostratigraphic units were best resolved using non-preprocessed variables , and segmentation coupled with postregularized RF classifications ( 0.652 ±0.011 ) . These methods may be used to improve the accuracy of classifications across a wide variety of spatial modeling applications .
The classification accuracy was evaluated and compared with respect to the sensor type – multi-spectral or radar – and the number of acquisitions needed . Our results showed that very dense time series allowed for very high accuracy classifications ( > 90 % ) of small scale vegetation types . The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed .
Accurate and early diagnosis can save lives and reduce the percentage of mortality . This study focuses classification accuracy analysis of ovarian cancer . The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model .
In this study , the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index ( EVI2 ) was investigated . Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012 . A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages , i.e. , the transplanting and the vegetative to reproductive transition phases , of the SCR was proposed , and the overall classification accuracy was 91.7 % .
Different target types are then tested . Although targets which provide detailed compositional information produce higher accuracies of classification for subsidiary classes , there is a trade off between the added information and added complexity which can decrease classification accuracy . Overall , the results show that the network seems to be able to identify the classes that are present within pixels but not their proportions .
Despite its long history , there has been renewed interest in this problem with the advent of high-dimensional and complex data . Specifically , in the machine learning literature , there have been recent methodological developments such as classification accuracy tests . The goal of this work is to present a regression approach to comparing multivariate distributions of complex data .
The encoder-decoder neural network architecture has been widely used in image segmentation . In recent years , the same architecture has also been used in seismic surveys for facies classification applications . In this paper , a modified U-Net architecture with trainable soft attention mechanism and dilated convolution is proposed to improve the automatic seismic facies analysis .
Characterizing the interpretational uncertainty in facies classification is quite difficult , but it can be very important for reservoir development decisions . Thus , automation of the facies classification process using machine learning is a potentially intuitive and efficient way to facilitate facies interpretation based on large-volume data . It can also enable more adequate quantification of the uncertainty in facies classification by ensuring that possible alternative lithological scenarios are not overlooked .
Structure-enhancing Nonlinear Filtering of Seismic Images The Application of Coherency Cube and Image Sharpening to Seismic Data Interpretation Different Specialties, Different Gaze Strategies: Eye Tracking Opportunities in Seismic Interpretation Context Seismic wavelet estimation: a frequency domain solution to a geophysical noisy input-output problem Using virtual reality for quality control and interpretation of 4-D reservoir characterization and AVO analysis Reply by the authors to the discussion by M. M. Roksandić Assisting Seismic Image Interpretations with Hyperknowledge Mathematical models of elastic wave processes in seismology and seismic prospecting: forward and inverse problems [TOPIC] 암 치료의 효율성을 극대화하는 새로운 광 치료 입자 Low power, long life design for smart intelligence, surveillance, and reconnaissance (ISR) sensors Design of a CMOS MEMS Accelerometer Used in IoT Devices for Seismic Detection A Study of Neural Network Applications to Signal Processing Lossy Compression for Wireless Seismic Data Acquisition On the Measurement and Use of Time-Varying Communication Channels Personnel and vehicle signal sensing based on seismic detection A minimum entropy approach to rule learning from examples Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks Fisher Discriminant Analysis (FDA), a supervised feature reduction method in seismic object detection Modeling and test result of closed-loop MEMS accelerometer with wide dynamic range Development of Ultra-Low-Power Handheld Testing Instrument of Seismic Detector Based on μC/OS-II A Machine Learning Benchmark for Facies Classification A machine learning approach to facies classification using well logs Design Ensemble Machine Learning Model for Breast Cancer Diagnosis Spatial-Contextual Supervised Classifiers Explored: A Challenging Example of Lithostratigraphy Classification Grassland habitat mapping by intra-annual time series analysis - Comparison of RapidEye and TerraSAR-X satellite data Ovarian Cancer Classification Accuracy Analysis Using 15-Neuron Artificial Neural Networks Model Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images The identification of sub-pixel components from remotely sensed data : an evaluation of an artificial neural network approach Global and Local Two-Sample Tests via Regression. Improved Automating Seismic Facies Analysis Using Deep Dilated Attention Autoencoders Value of Geologically Derived Features in Machine Learning Facies Classification
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