Inverse distance weighting (IDW)

Inverse Distance Weighting (IDW) estimates the value at each evaluation point from a weighted average of its nearest known samples. Each neighbor's weight is inversely proportional to its distance raised to a chosen power, so closer samples contribute more than distant ones. With a higher power, nearby samples dominate; with a lower power, the estimate is smoother.

Parameters

• source (object)
  • object (geoscience object reference)
    • Reference to a geoscience object containing the spatial locations of known values. Must be a pointset or downhole-intervals object.
  • attribute (geoscience object attribute reference)
    • Reference to a one-dimensional continuous attribute inside the source object that contains the known values used for estimation.
  • filter (object, optional)
    • Restricts the estimation to a subset of the source samples by filtering on an attribute of the source object. See Filtering.

  • {
      "object": "https://{hub}.api.seequent.com/geoscience-object/orgs/{org_id}/workspaces/{workspace_id}/objects/path/my-pointset.json",
      "attribute": "locations.attributes[?name=='my-attribute']"
    }

• target (object)
  • object (geoscience object reference)
    • Reference to the geoscience object where the IDW results will be stored. A new attribute containing the estimated values is created on this object. Must be a pointset, downhole-intervals, regular-3d-grid, or regular-masked-3d-grid object.
  • attribute (geoscience object attribute target)
    • Target that points to the attribute where the IDW results will be created or updated.
  • filter (object, optional)
    • Restricts the estimation to a subset of the target locations by filtering on an attribute of the target object. Locations that are filtered out are left as NaN. See Filtering.

  • {
      "object": "https://{hub}.api.seequent.com/geoscience-object/orgs/{org_id}/workspaces/{workspace_id}/objects/path/my-pointset.json",
      "attribute": {
        "operation": "create",
        "name": "my-idw-result"
      }
    }

• neighborhood (object)
  • Search parameters that determine which nearby samples to use for each evaluation point.

  • {
      "ellipsoid": {
        "ellipsoid_ranges": {
          "major": 100.0,      // Major axis length of the search ellipsoid
          "semi_major": 75.0,  // Semi-major axis length
          "minor": 50.0        // Minor axis length
        },
        "rotation": {
          "dip_azimuth": 45.0, // First rotation about z-axis (0-360 degrees)
          "dip": 30.0,         // Second rotation about x-axis (0-180 degrees)
          "pitch": 0.0         // Third rotation about z-axis (0-360 degrees)
        }
      },
      "max_samples": 15        // Maximum number of nearby samples to use
    }

• power (number)
  • Positive number controlling how quickly a neighbor's influence decreases with distance. Weights are proportional to $1 / d^{p}$, where $d$ is the distance in the anisotropic search space and $p$ is the power. Common values are 1.0 and 2.0; larger values give nearby samples greater dominance.

Filtering

Both source.filter and target.filter accept a filter expression with a single where clause. The where clause is either a leaf condition or a composite (all_of / any_of) of nested expressions.

A leaf condition uses an operator together with either values (for membership operators in / not_in) or threshold (for the comparison operators equal, not_equal, greater_than, greater_than_or_equal_to, less_than, less_than_or_equal_to).

// Keep only samples whose "rock-type" is granite or basalt
{
  "where": {
    "type": "condition",
    "attribute": "locations.attributes[?name=='rock-type']",
    "operator": "in",
    "values": ["granite", "basalt"]
  }
}

// Keep only locations where "grade" is above a threshold AND "domain" is 1
{
  "where": {
    "type": "all_of",
    "filters": [
      {
        "type": "condition",
        "attribute": "locations.attributes[?name=='grade']",
        "operator": "greater_than",
        "threshold": 0.5
      },
      {
        "type": "condition",
        "attribute": "locations.attributes[?name=='domain']",
        "operator": "in",
        "values": [1]
      }
    ]
  }
}

Example

For more information, see the inverse-distance-weighting API reference.

Request

requests.post(
    "https://{hub}.api.seequent.com/compute/orgs/{org_id}/geostatistics/idw",
    headers={"Authorization": "Bearer {token}"},
    json={
        "source": {
          "object": "https://{hub}.api.seequent.com/geoscience-object/orgs/{org_id}/workspaces/{workspace_id}/objects/path/my-pointset.json",
          "attribute": "locations.attributes[?name=='my-attribute']",
        },
        "target": {
          "object": "https://{hub}.api.seequent.com/geoscience-object/orgs/{org_id}/workspaces/{workspace_id}/objects/path/my-grid.json",
          "attribute": {
              "operation": "create",
              "name": "my-idw-result"
          },
        },
        "neighborhood": {
            "ellipsoid": {
                "ellipsoid_ranges": {
                    "major": 100.0,
                    "semi_major": 50.0,
                    "minor": 20.0
                },
                "rotation": {
                    "dip_azimuth": 45.0,
                    "dip": 30.0,
                    "pitch": 10.0
                }
            },
            "max_samples": 50,
            "min_samples": 10,
        },
        "power": 2.0,
    },
)

Result

{
    "message": "IDW estimation completed.",
    "target": {
       "reference": "https://{hub}.api.seequent.com/geoscience-object/orgs/{org_id}/workspaces/{workspace_id}/objects/path/my-grid.json",
       "name": "My Grid",
       "schema_id": "/objects/regular-3d-grid/1.3.0",
       "attribute": {
         "reference": "cell_attributes[?key=='3cc3aa58-c928-4e79-b8ca-550174bff59e']",
         "name": "my-idw-result"
      }
    }
}

Tips

• The source and target can use the same object, but they don't have to.
• IDW with power = 2.0 (inverse-distance-squared) is a common default. Use a higher power when you want closer samples to dominate; use a lower power for a smoother estimate.
• For an unweighted average of the nearest neighbors (equal votes), use the k-nearest-neighbors task instead.