A Matlab code package for 2D/3D local slope estimation and structural filtering

Local slope is an important attribute that can help distinguish seismic signals from noise. Based on optimal slope estimation, many filtering methods can be designed to enhance the signal-to-noise ratio (S/N) of noisy seismic data. We present an open-source Matlab code package for local slope estimation and corresponding structural filtering. This package includes 2D and 3D examples with two main executable scripts and related sub-functions. All code files are in the Matlab format. In each main script, local slope is estimated based on the well-known plane wave destruction algorithm. Then, the seismic data are transformed to the flattened domain by utilizing this slope information. Further, the smoothing operator can be effectively applied in the flattened domain. We introduce the theory and mathematics related to these programs, and present the synthetic and field data examples to show the usefulness of this open-source package. The results of both local slope estimation and structural filtering demonstrate that this package can be conveniently and effectively applied to the seismic signal analysis and denoising.

Rainfall Trend And Seasonality Estimation For Marathwada Region

The present research work conducts a seasonality and trend analysis of rainfall over the 8 districts of the Marathwada region India. The study is carried out for the last 39 years ranging from 1980 to 2018. The rainfall data analysed pertains to pre-monsoon season, monsoon season (Kharif), and annual. The trend has been estimated using Sen’s slope estimation process along with Mann-Kendal test. It was observed that the all the Eight districts of the region show a negative trend in the annual rainfall received. Nanded district showed the largest negative trend in the annual rainfall. Out of eight districts seven districts of the region show a decline in rainfall during the monsoon season. The district of Nanded showed largest decline in the rainfall received during monsoon season. The present research work concludes with discussion on possible causes of such estimated trends.

Pre-stack diffraction separation by parameterizing the reflection local slope

Diffracted waves provide the opportunity to detect small-scale subsurface structures because they give wide illumination direction of geological discontinuities such as faults, pinch-outs, and collapsed columns. However, separating diffracted waves is challenging because diffracted waves have greater geometrical amplitude losses and are generally weaker than reflections. To retain more diffracted waves, a pre-stack diffraction separation method is proposed based on the local slope pattern and plane-wave destruction method. Generally, it is difficult to distinguish between the hyperbolic reflections and hyperbolic diffractions using the data-driven local slope estimation in the shot domain. Therefore, we transfer the slope estimation in the shot domain to the velocity analysis in the common midpoint domain and the ray parameter calculation in the stack domain. The connection between the local slope and the normal move-out velocity and the surface-ray parameter is known, which provides a novel approach for estimating the local slope of the hyperbolic reflected waves in the shot domain. The estimated slope can provide an exact slope-based operator for the plane-wave destruction (PWD) method, thus allowing the PWD to separate diffracted waves from reflected waves in the shot domain. Synthetic and field data tests demonstrate the feasibility and effectiveness of the proposed pre-stack diffraction separation method.

Non-stationary local slope estimation via forward-backward space derivative calculation

The local slope estimated from seismic images has a variety of meaningful applications. Slope estimation based on the plane-wave destruction (PWD) method is one of the widely accepted techniques in the seismic community. However, the PWD method suffers from its sensitivity to noise in the seismic data. We propose an improved slope estimation method based on the PWD theory that is more robust in the presence of strong random noise. The PWD operator derived in the Z-transform domain contains a phase-shift operator in space corresponding to the calculation of the first-order derivative of the wavefield in the space domain. The first-order derivative is discretized based on a forward finite difference in the traditional PWD method, which lacks the constraint from the backward direction. We propose an improved method by discretizing the first-order space derivative based on an averaged forward-backward finite-difference calculation. The forward-backward space derivative calculation makes the space-domain first-order derivative more accurate and better anti-noise since it takes more space grids for the derivative calculation. In addition, we introduce non-stationary smoothing to regularize the slope estimation and to make it even more robust to noise. We demonstrate the performance of the new slope estimation method by several synthetic and field data examples in different applications, including 2D/3D structural filtering, structure-oriented deblending, and horizon tracking.

Impacts of Point Cloud Density Reductions on Extracting Road Geometric Features from mobile LiDAR data

Density of point cloud data varies depending on several different factors. Nonetheless, the extent to which changes in density could impact the accuracy of extracting roadway geometric features from the data is unknown. This paper investigates the impacts of point density reduction on the extraction of four critical geometric features. The density of the data was first reduced, and the different features were extracted at different levels of point density. The information obtained at lower point density was compared to what was obtained using the at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e. reducing the point density to as low as 10% of the original data (30ppm2 on the pavement surface) had minor impacts on the assessments). In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed.

A Refined Phase Unwrapping Method for High Noisy Dense Fringe Interferogram Based on Adaptive Cubature Kalman Filter

Cubature Kalman filter phase unwrapping (CKFPU) is an effective algorithm in unwrapping the interferograms. The local phase slope estimation is a key factor that affects the unwrapped accuracy. However, the estimation accuracy of local phase slop is relatively low in high noisy and dense stripes areas, which usually leads to the unsatisfactory unwrapped results. In order to effectively solve this issue, the rewrapped map of the unwrapped phase (obtained by CKFPU algorithm), which is a filtered interferogram with clearer fringes and more detailed information, is proposed in this paper to improve the phase slope estimation. In order to solve the problem of imprecise error variance for the new phase slope estimation, an adaptive factor is introduced into the CKFPU algorithm to increase the stability and reliability of the phase unwrapping algorithm. The proposed method is compared with the standard CKFPU algorithm using both simulated and real data. The experimental results validate the feasibility and superiority of the proposed method for processing those high noise dense fringe interferograms.

FURTHER ADVENTURES IN MARS DTM QUALITY: SMOOTHING ERRORS, SHARPENING DETAILS

We have used high-precision, high-resolution digital terrain models (DTMs) of the NASA Mars Science Laboratory and Mars 2020 rover landing sites based on mosaicked images from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (MRO HiRISE) camera as a reference data set to evaluate DTMs based on Mars Express High Resolution Stereo Camera (MEX HRSC) images. The Next Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET/GXP® commercial photogram- metric system produces DTMs with relatively good (small) horizontal resolution but high error, and results are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give very similar tradeoffs between resolution and error. Smoothing the NGATE DTMs with a 5x5 lowpass filter is near optimal in terms of both combined resolution-error performance and local slope estimation, but smoothing with a single pass of an area-based matcher, which has been the standard approach for generating planetary DTMs at the U.S. Geological Survey to date results in similar errors and only slightly worse resolution. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Although DTM resolution and error each vary by a factor of 2, the product of resolution and error is much more consistent, varying by ≤20% across multiple image sets and matching algorithms. Refinement of the stereo DTM by photoclinometry can yield significant quantitative improvement in resolution and some improvement in error (improving their product by as much as a factor of 2), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe.

Terrain-Dependent Slip Risk Prediction for Planetary Exploration Rovers

SUMMARY Wheel slip prediction on rough terrain is crucial for secure, long-term operations of planetary exploration rovers. Although rough, unstructured terrain hampers mobility, prediction by modeling wheel–terrain interactions remains difficult owing to unclear terrain conditions and complexities of terramechanics models. This study proposes a vision-based approach with machine learning for predicting wheel slip risk by estimating the slope from 3D information and classifying terrain types from image information. It considers the slope estimation accuracy for risk prediction under sharp increases in wheel slip due to inclined ground. Experimental results obtained with a rover testbed on several terrain types validate this method.