نرم افزارتسطیح اراضی VISUAL LEVEL

The Inverse Distance to a Power method

Inverse Distance to a Power

در اين روش واسطه يابي بوسيله ميانگين وزني داده ها انجام ميشود يعني تاثير نسبي يك نقطه با دور شدن از شبكه كاهش مي يابد

Kriging

اين روش که روش پيش فرض نرم افزار می باشد جزو قابل اطمينان ترين گزينه ها بين روش هاي واسطه يابي است و در بسياري از زمينه ها كاربرد دارد

The Minimum Curvature Method

Minimum Curvature is widely used in the earth sciences. The interpolated surface generated by Minimum Curvature is analogous to a thin, linearly elastic plate passing through each of the data values, with a minimum amount of bending. Minimum Curvature generates the smoothest possible surface while attempting to honor your data as closely as possible. Minimum Curvature is not an exact interpolator, however. This means that your data are not always honored exactly

Minimum Curvature

روش كمترين انحنا بطور گسترده در علم زمين شناسي استفاده ميشود.سطحي كه با اين روش ساخته ميشود شبيه يک صفحه نازك كشسان است كه با انحناي كم از داده ها مي گذرد.

The Modified Shepard's Method

Modified Shepard's Method

اين روش شبيه به روش Inverse Distance to a Power است با اين تفاوت كه معادلات كمترين مربعات حل شده، به صورت محلي است.

The Natural Neighbor Method

Natural Neighbor

اين روش از طريق ايجاد مثلث هاي متشابه و تصحيح آنها بعد از اضافه شدن داده جديد، كار مي كند.

The Nearest Neighbor Method

Nearest Neighbor

اين روش مقادير نزديكترين نقطه را به هر گره شبكه اختصاص مي دهد. کاربران قدیمی برنامه می توانند با استفاده از این روش فایل ورودی رقوم ارتفاعی زمین پروژه های قبلی خود را بدون هرگونه تغییر محسوسی به نسخه جدید منتقل و مورد استفاده و ارزیابی مجدد قرار دهند.

The Polynomial Regression Method

Polynomial Regression is used to define large-scale trends and patterns in your data. Polynomial Regression is not really an interpolator because it does not attempt to predict unknown Z values. There are several options you can use to define the type of trend surface

Polynomial Regression

اين روش براي تعريف روندها و خصوصيات بزرگ مقياس در داده ها است.

The Radial Basis Function Interpolation Method

Radial Basis Function interpolation is a diverse group of data interpolation methods. In terms of the ability to fit your data and produce a smooth surface, the multiquadric method is considered by many to be the best. All of the Radial Basis Function methods are exact interpolators, so they attempt to honor your data. You can introduce a smoothing factor to all the methods in an attempt to produce a smoother surface.

Radial Basis Function

تركيبي از روش هاي مختلف براي توليد سطح صاف است.

The Triangulation with Linear Interpolation Method

The Triangulation with Linear Interpolation method in SURFER uses the optimal Delaunay triangulation. This algorithm creates triangles by drawing lines between data points. The original points are connected in such a way that no triangle edges are intersected by other triangles. The result is a patchwork of triangular faces over the extent of the grid. This method is an exact interpolator. Each triangle defines a plane over the grid nodes lying within the triangle, with the tilt and elevation of the triangle determined by the three original data points defining the triangle. All grid nodes within a given triangle are defined by the triangular surface. Because the original data are used to define the triangles, the data are honored very closely. Triangulation with Linear Interpolation works best when your data are evenly distributed over the grid area. Data sets containing sparse areas result in distinct triangular facets on the map.

 Triangulation with Linear Interpolation

اين روش مثلث هاي بهينه اي را با وصل خطوطي بين نقاط داده ايجاد ميكند.

The Moving Average Method

Moving Average

اين روش مقاديري را كه با ميانگين گيري از داده هاي داخل بيضوي نقاط شبكه بدست مي آيد به گره هاي شبكه اختصاص مي دهد.

The Data Metrics Methods

The collection of data metrics methods creates grids of information about the data on a node-by-node basis. The data metrics methods are not, in general, weighted average interpolators of the Z-values. For example, you can obtain information such as:

a) The number of data points used to interpolate each grid node.  If the number of data points used is fairly equal at each grid node, then the quality of the grid at each grid node can be interpreted.

b) The standard deviation, variance, coefficient of variation, and median absolute deviation of the data at each grid node. These are measures of the variability in space of the grid, which is important information for statistical analysis.

Data Metrics

در اين روش واسطه يابي به صورت نقطه به نقطه انجام مي گيرد.

The Local Polynomial Method

Local Polynomial

در اين روش مقادير محاسبات به روش كمترين مربعات بر روي بيضوي به گره هاي شبكه اختصاص داده مي شود.