Pitfalls in horizon autopicking

2015 ◽  
Vol 3 (1) ◽  
pp. SB1-SB4 ◽  
Author(s):  
Donald A. Herron
Keyword(s):  
Data Quality ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Tracking Algorithms ◽  
Input Control ◽  
Noise Ratio ◽  
Selection Of

Interpreters use horizon autopicking in many seismic interpretations in the modern workstation environment. When properly used and with data quality permitting this technique enables efficient and accurate tracking of horizons but is not without its pitfalls. Four common pitfalls are improper selection of the input control or seed grid, not accounting for the “directional” behavior of tracking algorithms, attempting autopicking in areas with poor reflection continuity and/or low signal-to-noise ratio, and failing to recognize elements of geology that are not suitable for autopicking.

scholarly journals Collecting Microplastics in Gardens: Case Study (i) of Soil

2021 ◽  
Vol 9 ◽  
Author(s):  
Zahra Sobhani ◽  
Yunlong Luo ◽  
Christopher T. Gibson ◽  
Youhong Tang ◽  
Ravi Naidu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Point Of View ◽  
Signal To Noise ◽  
Statistical Point ◽  
Image Mapping ◽  
Noise Ratio ◽  
In The Beginning ◽  
Raman Image ◽  
Selection Of

As an emerging contaminant, microplastic is receiving increasing attention. However, the contamination source is not fully known, and new sources are still being identified. Herewith, we report that microplastics can be found in our gardens, either due to the wrongdoing of leaving plastic bubble wraps to be mixed with mulches or due to the use of plastic landscape fabrics in the mulch bed. In the beginning, they were of large sizes, such as > 5 mm. However, after 7 years in the garden, owing to natural degradation, weathering, or abrasion, microplastics are released. We categorize the plastic fragments into different groups, 5 mm–0.75 mm, 0.75 mm–100 μm, and 100–0.8 μm, using filters such as kitchenware, meaning we can collect microplastics in our gardens by ourselves. We then characterized the plastics using Raman image mapping and a logic-based algorithm to increase the signal-to-noise ratio and the image certainty. This is because the signal-to-noise ratio from a single Raman spectrum, or even from an individual peak, is significantly less than that from a spectrum matrix of Raman mapping (such as 1 vs. 50 × 50) that contains 2,500 spectra, from the statistical point of view. From the 10 g soil we sampled, we could detect the microplastics, including large (5 mm–100 μm) fragments and small (<100 μm) ones, suggesting the degradation fate of plastics in the gardens. Overall, these results warn us that we must be careful when we do gardening, including selection of plastic items for gardens.

Accurate Measurement of Trace Elements Using an Innovative Fixed Goniometer for a Simultaneous Spectrometer

1994 ◽  
Vol 38 ◽  
pp. 691-698
Author(s):  
K. Kansai ◽  
K. Toda ◽  
H. Kohno ◽  
T. Arai ◽  
R. Wilson
Keyword(s):  
Trace Elements ◽  
Exposure Time ◽  
Background Subtraction ◽  
Signal To Noise Ratio ◽  
Detection Limits ◽  
Background Correction ◽  
Analysis Time ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Selection Of

Advancements in trace clement analysis require improvements in both the signal-to-noise ratio and accurate background correction. With a sequential spectrometer, one can obtain detection limits of around 0.1 ppm for medium to heavy Z elements. Conditions can be individually optimized for each element, for example, selection of filters, collimators, crystals and background subtraction. The disadvantage is that the analysis time may become “long” if many elements are to be analyzed. This long exposure time can lead to the deterioration of some samples.

The selection of optimum conditions for spectrochemical methods. 1. Use of signal-to-noise ratio theory

1967 ◽  
Vol 44 (2) ◽  
pp. 80 ◽  
Author(s):  
W. J. McCarthy ◽  
J. D. Winefordner ◽  
P. A. St. John
Keyword(s):  
Signal To Noise Ratio ◽  
Optimum Conditions ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Selection Of

scholarly journals Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

2019 ◽  
Vol 8 (6) ◽  
pp. 23-37
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Huffman Coding ◽  
Signal To Noise ◽  
Codebook Design ◽  
Tlbo Algorithm ◽  
Noise Ratio ◽  
Selection Of

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

The selection of testing methods for biofuels using the Taguchi signal-to-noise ratio

1994 ◽  
Vol 6 (5) ◽  
pp. 405-413
Author(s):  
R. Irusta ◽  
G. Antolín ◽  
E. Velasco ◽  
J.C. García
Keyword(s):  
Signal To Noise Ratio ◽  
Testing Methods ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Selection Of

Two-Stage Selection of the Best Signal-To-Noise Ratio with Related Approximations

2009 ◽  
Vol 61 (1-4) ◽  
pp. 61-86 ◽  
Author(s):  
Makoto Aoshima ◽  
Nitis Mukhopadhyay ◽  
Kazuyoshi Yata
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Two Stage ◽  
Noise Ratio ◽  
Selection Of
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