scholarly journals The spatial data-adaptive minimum-variance distortionless-response beamformer on seismic single-sensor data

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. Q29-Q42 ◽  
Author(s):  
Ionelia Panea ◽  
Guy Drijkoningen
Keyword(s):  
Spatial Data ◽  
Seismic Data ◽  
Minimum Variance ◽  
Sensor Data ◽  
Near Surface ◽  
Total Signal ◽  
Optimal Weights ◽  
Minimum Variance Distortionless Response ◽  
Single Sensor ◽  
Data Adaptive

Coherent noise generated by surface waves or ground roll within a heterogeneous near surface is a major problem in land seismic data. Array forming based on single-sensor recordings might reduce such noise more robustly than conventional hardwired arrays. We use the minimum-variance distortionless-response (MVDR) beamformer to remove (aliased) surface-wave energy from single-sensor data. This beamformer is data adaptive and robust when the presumed and actual desired signals are mismatched. We compute the intertrace covariance for the desired signal, and then for the total signal (desired [Formula: see text]) to obtain optimal weights. We use the raw data of only one array for the covariance of the total signal, and the wavenumber-filtered version of a full seismic single-sensor record for the covariance of the desired signal. In the determination of optimal weights, a parameter that controls the robustness of the beamformer against an arbitrary desired signal mismatch has to be chosen so that the results are optimal. This is similar to stabilization in deconvolution problems. This parameter needs to be smaller than the largest eigenvalue provided by the singular value decomposition of the presumed desired signal covariance. We compare results of MVDR beamforming with standard array forming on single-sensor synthetic and field seismic data. We apply 2D and 3D beamforming and show prestack and poststack results. MVDR beamformers are superior to conventional hardwired arrays for all examples.

scholarly journals Interferometric ground-roll removal: Attenuation of scattered surface waves in single-sensor data

Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. SA15-SA25 ◽  
Author(s):  
David F. Halliday ◽  
Andrew Curtis ◽  
Peter Vermeer ◽  
Claudio Strobbia ◽  
Anna Glushchenko ◽  
...  
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Seismic Data ◽  
Sensor Data ◽  
Seismic Survey ◽  
Seismic Interferometry ◽  
Near Surface ◽  
Reflection Data ◽  
Ground Roll ◽  
Single Sensor

Land seismic data are contaminated by surface waves (or ground roll). These surface waves are a form of source-generated noise and can be strongly scattered by near-surface heterogeneities. The resulting scattered ground roll can be particularly difficult to separate from the desired reflection data, especially when this scattered ground roll propagates in the crossline direction. We have used seismic interferometry to estimate scattered surface waves, recorded during an exploration seismic survey, between pairs of receiver locations. Where sources and receivers coincide, these interreceiver surface-wave estimates were adaptively subtracted from the data. This predictive-subtraction process can successfully attenuate scattered surface waves while preserving the valuable reflected arrivals, forming a new method of scattered ground-roll attenuation. We refer to this as interferometric ground-roll removal.

Nonlinear beamforming for enhancement of 3D prestack land seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V283-V296 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Maxim Dmitriev ◽  
Dmitry Neklyudov ◽  
Maxim Protasov ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Sensor Data ◽  
Reservoir Properties ◽  
Near Surface ◽  
3D Data ◽  
Common Reflection Surface ◽  
Single Sensor ◽  
The Common

We have developed nonlinear beamforming (NLBF), a method for enhancing modern 3D prestack seismic data acquired onshore with small field arrays or single sensors in which weak reflected signals are buried beneath the strong scattered noise induced by a complex near surface. The method is based on the ideas of multidimensional stacking techniques, such as the common-reflection-surface stack and multifocusing, but it is designed specifically to improve the prestack signal-to-noise ratio of modern 3D land seismic data. Essentially, NLBF searches for coherent local events in the prestack data and then performs beamforming along the estimated surfaces. Comparing different gathers that can be extracted from modern 3D data acquired with orthogonal acquisition geometries, we determine that the cross-spread domain (CSD) is typically the most convenient and efficient. Conventional noise removal applied to modern data from small arrays or single sensors does not adequately reveal the underlying reflection signal. Instead, NLBF supplements these conventional tools and performs final aggregation of weak and still broken reflection signals, where the strength is controlled by the summation aperture. We have developed the details of the NLBF algorithm in CSD and determined the capabilities of the method on real 3D land data with the focus on enhancing reflections and early arrivals. We expect NLBF to help streamline seismic processing of modern high-channel-count and single-sensor data, leading to improved images as well as better prestack data for estimation of reservoir properties.

Seismic-while-drilling applications from the first DrillCAM trial with wireless geophones and instrumented top drive

2020 ◽  
Vol 39 (6) ◽  
pp. 422-429
Author(s):  
Andrey Bakulin ◽  
Ali Aldawood ◽  
Ilya Silvestrov ◽  
Emad Hemyari ◽  
Flavio Poletto
Keyword(s):  
Real Time ◽  
Polycrystalline Diamond ◽  
Drill String ◽  
Sensor Data ◽  
Continuous Data ◽  
Near Surface ◽  
Initial Application ◽  
Look Ahead ◽  
Drilling Operations ◽  
Single Sensor

Advanced geophysical sensing while drilling is being driven by trends to automate and optimize drilling and the desire to better characterize complex near surface and overburden in desert environments. We introduce the DrillCAM system, which combines a set of geophysical techniques from seismic while drilling (SWD), drill-string vibration health, estimation of formation properties at the bit, and imaging ahead of and around the bit. We present data acquisition, processing, and initial application results from the first field trial on an onshore well in a desert environment. In this study, we focus on SWD applications. For the first time, wireless geophones installed around a rig were used to acquire continuous data while drilling. We demonstrate the feasibility of such a system to provide flexible acquisition geometries that are easily expandable with increasing bit depth without interference from drilling operations. Using a top-drive sensor as a pilot, we transform the drill-bit noise into meaningful and reliable seismic signals. The data were used to retrieve a check shot while drilling, make kinematic look-ahead predictions, and obtain a vertical seismic profiling corridor stack matching surface seismic. Robust near-offset check-shot signals were received from roller-cone and polycrystalline diamond compact (PDC) bits above 7200 ft after limited preprocessing of challenging single-sensor data with supergrouping. Detecting signals from deeper sections drilled with PDC bits may require more advanced processing by using an entire 2D spread of wireless geophones and downhole pilots. The real-time capabilities of the system make the data available for continuous data processing and interpretation that will facilitate drilling automation and improve real-time decision making.

An effective data processing workflow for broadband single-sensor single-source land seismic data

2020 ◽  
Vol 39 (6) ◽  
pp. 401-410
Author(s):  
Simon Cordery
Keyword(s):  
Seismic Data ◽  
Amplitude Spectrum ◽  
Low Frequency ◽  
Residual Effects ◽  
Signal Spectrum ◽  
Frequency Noise ◽  
Single Source ◽  
Noise Characteristics ◽  
Single Sensor ◽  
Data Adaptive

Examples of raw and processed broadband single-sensor single-source land seismic data acquired in the Middle East region have been found to be significantly noisy, and very low-frequency signal has been either missing or unrecoverable. In response, an effective and pragmatic processing workflow has been developed that substantially improves the quality of the final processed data, to the extent where we can say that original survey objectives can be met. The new workflow includes early deterministic deconvolution for a number of filtering effects in the recorded signal wavelet, with the aim of flattening the signal wavelet amplitude spectrum over the vibroseis sweep frequencies and zeroing the wavelet phase. This includes the key innovative step of converting the recorded particle motion to that of the vibroseis far-field signal, those respectively being particle acceleration and particle displacement. This significantly boosts low-frequency amplitudes relative to higher frequencies such that it becomes possible to deterministically compensate for earth's absorption using a large gain limit with less concern for overamplifying high-frequency noise. An application of a source designature compensates for the nonflat design of the pilot sweep, further increasing signal amplitudes over the low-frequency ramp-up portion of the sweep. With the flattened signal spectrum, it is possible to better assess trace noise characteristics across the full bandwidth and perform better QC for its removal. Subsequent statistical deconvolution becomes more of a correction for residual effects on the signal wavelet, and the use of trace supergrouping further mitigates the effect of noise on statistical deconvolution and other data-adaptive processes.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

State Estimation Using Randomly Delayed Measurements

1993 ◽  
Vol 115 (1) ◽  
pp. 19-26 ◽  
Author(s):  
A. Ray ◽  
L. W. Liou ◽  
J. H. Shen
Keyword(s):  
State Estimation ◽  
Minimum Variance ◽  
The State ◽  
Sensor Data ◽  
State Estimator ◽  
Uniform Asymptotic Stability ◽  
Simulation Experiments ◽  
State Estimate ◽  
Sampling Instant ◽  
Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

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