How reliable is statistical wavelet estimation?

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. V59-V68 ◽  
Author(s):  
Jonathan A. Edgar ◽  
Mirko van der Baan
Keyword(s):  
Statistical Method ◽  
Estimation Method ◽  
Ground Truth ◽  
Well Logs ◽  
Deterministic Method ◽  
Wavelet Estimation ◽  
Phase Rotation ◽  
Phase Corrections ◽  
Kurtosis Maximization ◽  
Rate Criterion

Well logs often are used for the estimation of seismic wavelets. The phase is obtained by forcing a well-derived synthetic seismogram to match the seismic, thus assuming the well log provides ground truth. However, well logs are not always available and can predict different phase corrections at nearby locations. Thus, a wavelet-estimation method that reliably can predict phase from the seismic alone is required. Three statistical wavelet-estimation techniques were tested against the deterministic method of seismic-to-well ties. How the choice of method influences the estimated wavelet phase was explored, with the aim of finding a statistical method which consistently predicts a phase in agreement with well logs. It was shown that the statistical method of kurtosis maximization by constant phase rotation consistently is able to extract a phase in agreement with seismic-to-well ties. A statistical method based on a modified mutual-information-rate criterion was demonstrated to provide frequency-dependent phase wavelets where the deterministic method could not. Time-varying statistical wavelets also were estimated with good results — a challenge for deterministic approaches because of the short logging sequence. It was concluded that statistical techniques can be used as quality control tools for the deterministic methods, as a way of extrapolating phase away from wells, or to act as standalone tools in the absence of wells.

scholarly journals Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5978
Author(s):  
Norbert P. Szabó ◽  
Rafael Valadez-Vergara ◽  
Sabuhi Tapdigli ◽  
Aja Ugochukwu ◽  
István Szabó ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Statistical Method ◽  
Organic Matter Content ◽  
Estimation Method ◽  
Organic Content ◽  
Well Logs ◽  
Unconventional Reservoirs ◽  
Estimation Methods ◽  
Model Parameters ◽  
Total Organic Content

Several approaches have been applied for the evaluation of formation organic content. For further developments in the interpretation of organic richness, this research proposes a multivariate statistical method for exploring the interdependencies between the well logs and model parameters. A factor analysis-based approach is presented for the quantitative determination of total organic content of shale formations. Uncorrelated factors are extracted from well logging data using Jöreskog’s algorithm, and then the factor logs are correlated with estimated petrophysical properties. Whereas the first factor holds information on the amount of shaliness, the second is identified as an organic factor. The estimation method is applied both to synthetic and real datasets from different reservoir types and geologic basins, i.e., Derecske Trough in East Hungary (tight gas); Kingak formation in North Slope Alaska, United States of America (shale gas); and shale source rock formations in the Norwegian continental shelf. The estimated total organic content logs are verified by core data and/or results from other indirect estimation methods such as interval inversion, artificial neural networks and cluster analysis. The presented statistical method used for the interpretation of wireline logs offers an effective tool for the evaluation of organic matter content in unconventional reservoirs.

scholarly journals Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator

2020 ◽  
Vol 8 ◽  
Author(s):  
Xiang Qian Shi ◽  
Ho Lam Heung ◽  
Zhi Qiang Tang ◽  
Kai Yu Tong ◽  
Zheng Li
Keyword(s):  
Estimation Method ◽  
Joint Stiffness ◽  
Ground Truth ◽  
Clinical Score ◽  
Metacarpophalangeal Joint ◽  
Finger Joint ◽  
Joint Torque ◽  
Hand Rehabilitation ◽  
Stiffness Characteristic ◽  
Modified Ashworth Scale

Stroke has been the leading cause of disability due to the induced spasticity in the upper extremity. The constant flexion of spastic fingers following stroke has not been well described. Accurate measurements for joint stiffness help clinicians have a better access to the level of impairment after stroke. Previously, we conducted a method for quantifying the passive finger joint stiffness based on the pressure-angle relationship between the spastic fingers and the soft-elastic composite actuator (SECA). However, it lacks a ground-truth to demonstrate the compatibility between the SECA-facilitated stiffness estimation and standard joint stiffness quantification procedure. In this study, we compare the passive metacarpophalangeal (MCP) joint stiffness measured using the SECA with the results from our designed standalone mechatronics device, which measures the passive metacarpophalangeal joint torque and angle during passive finger rotation. Results obtained from the fitting model that concludes the stiffness characteristic are further compared with the results obtained from SECA-Finger model, as well as the clinical score of Modified Ashworth Scale (MAS) for grading spasticity. These findings suggest the possibility of passive MCP joint stiffness quantification using the soft robotic actuator during the performance of different tasks in hand rehabilitation.

scholarly journals Voxel-Based Scene Representation for Camera Pose Estimation of a Single RGB Image

2020 ◽  
Vol 10 (24) ◽  
pp. 8866
Author(s):  
Sangyoon Lee ◽  
Hyunki Hong ◽  
Changkyoung Eem
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Ground Truth ◽  
Training Dataset ◽  
Interest Points ◽  
Camera Pose Estimation ◽  
Polygonal Region ◽  
Camera Pose ◽  
End To End ◽  
Image Pairs

Deep learning has been utilized in end-to-end camera pose estimation. To improve the performance, we introduce a camera pose estimation method based on a 2D-3D matching scheme with two convolutional neural networks (CNNs). The scene is divided into voxels, whose size and number are computed according to the scene volume and the number of 3D points. We extract inlier points from the 3D point set in a voxel using random sample consensus (RANSAC)-based plane fitting to obtain a set of interest points consisting of a major plane. These points are subsequently reprojected onto the image using the ground truth camera pose, following which a polygonal region is identified in each voxel using the convex hull. We designed a training dataset for 2D–3D matching, consisting of inlier 3D points, correspondence across image pairs, and the voxel regions in the image. We trained the hierarchical learning structure with two CNNs on the dataset architecture to detect the voxel regions and obtain the location/description of the interest points. Following successful 2D–3D matching, the camera pose was estimated using n-point pose solver in RANSAC. The experiment results show that our method can estimate the camera pose more precisely than previous end-to-end estimators.

scholarly journals GPS and Stereovision-Based Visual Odometry: Application to Urban Scene Mapping and Intelligent Vehicle Localization

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Lijun Wei ◽  
Cindy Cappelle ◽  
Yassine Ruichek ◽  
Frédérick Zann
Keyword(s):  
Feature Detection ◽  
Reference Frames ◽  
Estimation Method ◽  
Ground Truth ◽  
Urban Environments ◽  
Relative Depth ◽  
Gps Receiver ◽  
Minimization Method ◽  
Vehicle Localization ◽  
Stereoscopic System

We propose an approach for vehicle localization in dense urban environments using a stereoscopic system and a GPS sensor. Stereoscopic system is used to capture the stereo video flow, to recover the environments, and to estimate the vehicle motion based on feature detection, matching, and triangulation from every image pair. A relative depth constraint is applied to eliminate the tracking couples which are inconsistent with the vehicle ego-motion. Then the optimal rotation and translation between the current and the reference frames are computed using an RANSAC based minimization method. Meanwhile, GPS positions are obtained by an on-board GPS receiver and periodically used to adjust the vehicle orientations and positions estimated by stereovision. The proposed method is tested with two real sequences obtained by a GEM vehicle equipped with a stereoscopic system and a RTK-GPS receiver. The results show that the vision/GPS integrated trajectory can fit the ground truth better than the vision-only method, especially for the vehicle orientation. And vice-versa, the stereovision-based motion estimation method can correct the GPS signal failures (e.g., GPS jumps) due to multipath problem or other noises.

Time-varying wavelet estimation and deconvolution by kurtosis maximization

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. V11-V18 ◽  
Author(s):  
Mirko van der Baan
Keyword(s):  
Field Measurements ◽  
Phase Changes ◽  
Data Driven ◽  
Wiener Filtering ◽  
Time Varying ◽  
Seismic Wavelet ◽  
Phase Rotation ◽  
Physical Laws ◽  
Kurtosis Maximization ◽  
Zero Phase

Phase mismatches sometimes occur between final processed sections and zero-phase synthetics based on well logs, despite best efforts for controlled-phase acquisition and processing. The latter are often based on deterministic corrections derived from field measurements and physical laws. A statistical analysis of the data can reveal whether a time-varying nonzero phase is present. This assumes that the data should be white with respect to all statistical orders after proper deterministic corrections have been applied. Kurtosis maximization by constant phase rotation is a statistical method that can reveal the phase of a seismic wavelet. It is robust enough to detect time-varying phase changes. Phase-only corrections can then be applied by means of a time-varying phase rotation. Alternatively, amplitude and phase deconvolution can be achieved using time-varying Wiener filtering. Time-varying wavelet extraction and deconvolution can also be used as a data-driven alternative to amplitude-only inverse-[Formula: see text] deconvolution.

Wavelet estimation for a multidimensional acoustic or elastic earth

Geophysics ◽  
1990 ◽  
Vol 55 (7) ◽  
pp. 902-913 ◽  
Author(s):  
Arthur B. Weglein ◽  
Bruce G. Secrest
Keyword(s):  
Estimation Method ◽  
Normal Derivative ◽  
Source Spectrum ◽  
Earth Model ◽  
Far Field ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Array Pattern ◽  
The Earth ◽  
Source Array

A new and general wave theoretical wavelet estimation method is derived. Knowing the seismic wavelet is important both for processing seismic data and for modeling the seismic response. To obtain the wavelet, both statistical (e.g., Wiener‐Levinson) and deterministic (matching surface seismic to well‐log data) methods are generally used. In the marine case, a far‐field signature is often obtained with a deep‐towed hydrophone. The statistical methods do not allow obtaining the phase of the wavelet, whereas the deterministic method obviously requires data from a well. The deep‐towed hydrophone requires that the water be deep enough for the hydrophone to be in the far field and in addition that the reflections from the water bottom and structure do not corrupt the measured wavelet. None of the methods address the source array pattern, which is important for amplitude‐versus‐offset (AVO) studies. This paper presents a method of calculating the total wavelet, including the phase and source‐array pattern. When the source locations are specified, the method predicts the source spectrum. When the source is completely unknown (discrete and/or continuously distributed) the method predicts the wavefield due to this source. The method is in principle exact and yet no information about the properties of the earth is required. In addition, the theory allows either an acoustic wavelet (marine) or an elastic wavelet (land), so the wavelet is consistent with the earth model to be used in processing the data. To accomplish this, the method requires a new data collection procedure. It requires that the field and its normal derivative be measured on a surface. The procedure allows the multidimensional earth properties to be arbitrary and acts like a filter to eliminate the scattered energy from the wavelet calculation. The elastic wavelet estimation theory applied in this method may allow a true land wavelet to be obtained. Along with the derivation of the procedure, we present analytic and synthetic examples.

scholarly journals Lora RTT Ranging Characterization and Indoor Positioning System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiang Liu ◽  
XiuJun Bai ◽  
Xingli Gan ◽  
Shan Yang
Keyword(s):  
Estimation Method ◽  
Ground Truth ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Cumulative Distribution ◽  
Positioning System ◽  
Positioning Error ◽  
Positioning Systems ◽  
Ground Truth Location ◽  
Indoor Positioning System

In recent years, indoor positioning systems (IPS) are increasingly very important for a smart factory, and the Lora positioning system based on round-trip time (RTT) has been developed. This paper introduces the ranging characterization, RTT measurement, and position estimation method. In particular, a particle filter localization method-aided Lora pseudorange fitting correction is designed to solve the problem of indoor positioning; the cumulative distribution function (CDF) criteria are used to measure the quality of the estimated location in comparison to the ground truth location; when the positioning error on the x -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 61% and 99%, which are higher than the 32% and 85% without pseudorange correction. When the positioning error on the y -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 71% and 99.9%, which are higher than the 52% and 94.8% without pseudorange correction.

scholarly journals Monocular weakly supervised depth and pose estimation method based on multi-information fusion

2021 ◽  
Author(s):  
Zhimin Zhang ◽  
◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
◽  
...  
Keyword(s):  
Information Fusion ◽  
Pose Estimation ◽  
Video Sequence ◽  
Stereo Matching ◽  
Estimation Method ◽  
Ground Truth ◽  
Estimation Accuracy ◽  
Monocular Video ◽  
Monocular Depth ◽  
Weakly Supervised

The depth and pose information are the basic issues in the field of robotics, autonomous driving, and virtual reality, and are also the focus and difficult issues of computer vision research. The supervised monocular depth and pose estimation learning are not feasible in environments where labeled data is not abundant. Self-supervised monocular video methods can learn effectively only by applying photometric constraints without expensive ground true depth label constraints, which results in an inefficient training process and suboptimal estimation accuracy. To solve these problems, a monocular weakly supervised depth and pose estimation method based on multi-information fusion is proposed in this paper. First, we design a high-precision stereo matching method to generate a depth and pose data as the "Ground Truth" labels to solve the problem that the ground truth labels are difficult to obtain. Then, we construct a multi-information fusion network model based on the "Ground truth" labels, video sequence, and IMU information to improve the estimation accuracy. Finally, we design the loss function of supervised cues based on "Ground Truth" labels cues and self-supervised cues to optimize our model. In the testing phase, the network model can separately output high-precision depth and pose data from a monocular video sequence. The resulting model outperforms mainstream monocular depth and poses estimation methods as well as the partial stereo matching method in the challenging KITTI dataset by only using a small number of real training data(200 pairs).

Automated Well-Log Processing and Lithology Classification by Identifying Optimal Features Through Unsupervised and Supervised Machine-Learning Algorithms

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2778-2800 ◽  
Author(s):  
Harpreet Singh ◽  
Yongkoo Seol ◽  
Evgeniy M. Myshakin
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Well Logs ◽  
Petroleum Engineering ◽  
Well Log ◽  
Classification Problems ◽  
Rock Types

Summary The application of specialized machine learning (ML) in petroleum engineering and geoscience is increasingly gaining attention in the development of rapid and efficient methods as a substitute to existing methods. Existing ML-based studies that use well logs contain two inherent limitations. The first limitation is that they start with one predefined combination of well logs that by default assumes that the chosen combination of well logs is poised to give the best outcome in terms of prediction, although the variation in accuracy obtained through different combinations of well logs can be substantial. The second limitation is that most studies apply unsupervised learning (UL) for classification problems, but it underperforms by a substantial margin compared with nearly all the supervised learning (SL) algorithms. In this context, this study investigates a variety of UL and SL ML algorithms applied on multiple well-log combinations (WLCs) to automate the traditional workflow of well-log processing and classification, including an optimization step to achieve the best output. The workflow begins by processing the measured well logs, which includes developing different combinations of measured well logs and their physics-motivated augmentations, followed by removal of potential outliers from the input WLCs. Reservoir lithology with four different rock types is investigated using eight UL and seven SL algorithms in two different case studies. The results from the two case studies are used to identify the optimal set of well logs and the ML algorithm that gives the best matching reservoir lithology to its ground truth. The workflow is demonstrated using two wells from two different reservoirs on Alaska North Slope to distinguish four different rock types along the well (brine-dominated sand, hydrate-dominated sand, shale, and others/mixed compositions). The results show that the automated workflow investigated in this study can discover the ground truth for the lithology with up to 80% accuracy with UL and up to 90% accuracy with SL, using six routine well logs [vp, vs, ρb, ϕneut, Rt, gamma ray (GR)], which is a significant improvement compared with the accuracy reported in the current state of the art, which is less than 70%.

Nonstationary phase estimation using regularized local kurtosis maximization

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. A75-A80 ◽  
Author(s):  
Mirko van der Baan ◽  
Sergey Fomel
Keyword(s):  
Statistical Estimation ◽  
Phase Estimation ◽  
Time Varying ◽  
Seismic Wavelet ◽  
Optimization Framework ◽  
Phase Rotation ◽  
Seismic Sections ◽  
The Stability ◽  
Kurtosis Maximization ◽  
Zero Phase

Phase mismatches sometimes occur between final processed seismic sections and zero-phase synthetics based on well logs — despite best efforts for controlled-phase acquisition and processing. Statistical estimation of the phase of a seismic wavelet is feasible using kurtosis maximization by constant-phase rotation, even if the phase is nonstationary. We cast the phase-estimation problem into an optimization framework to improve the stability of an earlier method based on a piecewise-stationarity assumption. After estimation, we achieve space-and-time-varying zero-phasing by phase rotation.

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