#kriging
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Rare earth drilling grid: 50m vs 100m Kriging Variance for Indicated 2026 — https://skymineral.com/blog/rare-earth-drilling-grid-50m-vs-100m-kriging-variance-for-indicated-2026.php
Rare earth drilling grid: 50m vs 100m Kriging Variance for Indicated 2026
September 26, 2026 at 8:45 PM
nfdi4objects.bsky.social
🍃 Plant Remains, Spatial Gains - BYOD Workshop 🗺️

Bis morgen noch anmelden bei @WiNoDa! Praxisnaher Workshop zu geostatistischen Methoden für archäobotanische Daten – von Variogrammen bis Kriging. Vorkenntnisse nicht nötig.

💼 Workshop: 9.10. | ✍️ Anmeldung: bis 25.9.
🔗 winoda.de/event/byod-de/
Plant Remains, Spatial Gains – BYOD Geo-statistical Workshop – WiNoDa Knowledge Lab
winoda.de
September 24, 2026 at 11:24 AM
spurven90.bsky.social
Kan vi få noen kritiske spørsmål til Knutsen etter dette? Slipper Brann inn i kampen. Publikum blir oppildnet og kriging kan ikke Glimt. Tar ut fotrappe spillere som Auklend og Blomberg når man blir 10 mot 11? Saltnes inn på HØYRE indreløper? Bidrar nada kontra fra venstre. @studioglimt.bsky.social
September 20, 2026 at 7:17 PM
vanuatugis.bsky.social
Why can't gstat::krige accept duplicate locations when universal kriging?
Let us create a simple simulation worthy of gstat::krige with universal kriging, in the language of linear mixed models, i.e.: Where X is a n x n_vars matrix of n_vars additional variables, betas is a n_vars x 1 so-called fixed-effects vector, Z is a n x q indicator matrix of q locations, each repeating say n / q times, e is normal random noise from N(0, sig2e), and b is a q x 1 so-called random-effects vector, with a standard Gaussian q x q covariance matrix or kernel: Where d_i, d_j are a pair of 2D (lat, lon) locations (there are q of these), and sill and range standard params. In code: library(sp) library(gstat) library(MASS) n
gis.stackexchange.com
September 20, 2026 at 6:25 PM
vanuatugis.bsky.social
Simple kriging in raster
I want to use a Python script for interpolation in 1st image to have the result of 2nd one, this result is from simple kriging saga. I tried the code below but I have two main problems. * Need a lot of time, over 3-4 hours to have * The result is this in 3d image and it's really different. from osgeo import gdal,ogr import numpy as np import gstools as gs import time #read a 2-D array from image filefolder="C:/Users/fermi/Desktop/SUEWS/SUEWSapp/kriging/" rasterinput=filefolder+"T2UFfill_T2_01_14.tif" src_ds = gdal.Open( rasterinput ) b3 = np.array(src_ds.GetRasterBand(1).ReadAsArray()) # the coordinates of x and y x= np.arange(322580., 335780., 100.) y = np.arange(3906895., 3913695. , 100.) start = time.time() print("hello") # your input data as x, y, data points data = b3 cond_pos = gs.generate_grid((x,y)) cond_val = [1.0] mean = np.mean(data) # simple gaussian model since your plot looks rather smooth model = gs.Gaussian(dim=2,len_scale=1) krige = gs.krige.Simple(model, cond_pos, data) # output on a structured grid (image) with specified axis xaxis, yaxis = y,x field = krige.structured([xaxis, yaxis], return_var=False)
gis.stackexchange.com
September 18, 2026 at 4:02 PM
vanuatugis.bsky.social
Gstat outputs NULL when using variogram() function
I am trying to do a kriging interpolation on a fake data set to familiarize myself with gstat. This code has worked before on a larger df but for some reason when I try to make the variogram, the output is empty, and I get a NULL error message. I've tried getting rid of the log() part and that did not help. x
gis.stackexchange.com
September 16, 2026 at 3:00 PM
nilshenrik.bsky.social
Psykologisk kriging er jammen ganske artig!
September 15, 2026 at 5:06 PM
agri-rural-studies.bsky.social
Article：Bayesian Data Fusion Framework for Soil Moisture Interpolation in Entre Ríos, Argentina: Analysis of Topographic Indices

Authors: César Augusto Aguirre et al.

Keywords: soil moisture; spatial variability; geostatistics; Ordinary Kriging; topography

doi.org/10.59978/ar0...
September 13, 2026 at 5:57 PM
vanuatugis.bsky.social
QGIS Smart Map Plugin - Error Running Interpolation
I am new to using the Smart-Map plugin. I added my data and did the ordinary Kriging interpolation. Just before it reaches 100% I get the following error message: An error has occurred while executing Python code: ValueError: invalid literal for int() with base 10: 'ccw' Traceback (most recent call last): File "C:\Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python/plugins\Smart_Map\Smart_Map.py", line 3809, in pushButton_Krigagem_clicked Output_Layer_File_tiff = self.export_raster_to_qgis(Input_Table, Output_Layer_File_tiff, Output_Layer_Name, z_field) File "C:\Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python/plugins\Smart_Map\Smart_Map.py", line 8427, in export_raster_to_qgis self.define_raster_color_ramp(rlayer, Output_Layer_Name) File "C:\Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python/plugins\Smart_Map\Smart_Map.py", line 8526, in define_raster_color_ramp QgsColorRampShader.ColorRampItem(classes[2], QColor(int(stops[6]),int(stops[7]),int(stops[8]),int(stops[9])), '%.6f' % classes[2]), ValueError: invalid literal for int() with base 10: 'ccw' Python version: 3.9.5 (tags/v3.9.5:0a7dcbd, May 3 2021, 17:27:52) [MSC v.1928 64 bit (AMD64)] QGIS version: 3.28.1-Firenze Firenze, fde3b8fbb8c Python Path: C:\Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python/plugins\generate_swmm_inp C:/PROGRA~1/QGIS32~1.1/apps/qgis/./python C:/Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python C:/Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python/plugins C:/PROGRA~1/QGIS32~1.1/apps/qgis/./python/plugins C:\Program Files\QGIS 3.28.1\bin\python39.zip C:\PROGRA~1\QGIS32~1.1\apps\Python39\DLLs C:\PROGRA~1\QGIS32~1.1\apps\Python39\lib C:\Program Files\QGIS 3.28.1\bin C:\PROGRA~1\QGIS32~1.1\apps\Python39 C:\PROGRA~1\QGIS32~1.1\apps\Python39\lib\site-packages C:\PROGRA~1\QGIS32~1.1\apps\Python39\lib\site-packages\win32 C:\PROGRA~1\QGIS32~1.1\apps\Python39\lib\site-packages\win32\lib C:\PROGRA~1\QGIS32~1.1\apps\Python39\lib\site-packages\Pythonwin C:/Users/ACER/AppData/Roaming/QGIS/QGIS3\profiles\default/python C:\Users\ACER\AppData\Roaming\QGIS\QGIS3\profiles\default\python\plugins\mmqgis/forms C:\Users\ACER\AppData\Roaming\QGIS\QGIS3\profiles\default\python\site-packages D:/Belajar Smart Map Can anyone tell me, what's the problem and how to resolves?
gis.stackexchange.com
September 11, 2026 at 9:06 AM
jeromeollier.bsky.social
Conditional KRIGING prediction of ship–ship hydrodynamic responses during non-parallel berthing - @frontiersin.bsky.social Marine Science

www.frontiersin.org/journals/mar...
September 9, 2026 at 7:59 PM
colincarlson.bsky.social
🚨 NEW: Biodiversity monitoring can guide surveillance for emerging pathogens. In a new PLOS Ecosystems study led by @mdcatchen.bsky.social, we present computational algorithms to select surveillance sites that capture the most actionable data. Out now: dx.plos.org/10.1371/jour...
Fig 3. The case study for South Korea. (A): Bivariate plot of dose (increasing green) and dose uncertainty (increasing blue). (B): Bivariate plot of dose (increasing green) and prevalence uncertainty (increasing orange; measured as the sum of estimated variance from kriging across each host-virus pair). (C): The spatial strata for which occurrence, prevalence, and both forms of sampling should occur (see main text for how these are computed). (D): The priority scores for regions where discovery sampling is a priority (increasingly blue) and prevalence sampling is the priority (increasingly purple) in their respective strata. (E): The total priority map with sites selected using Balanced Acceptance Sampling. The marker shape of each site corresponds to the type of sampling that should occur there, based on the stratum in (C) that each point falls within. Administrative boundaries come from GADM.
September 9, 2026 at 4:15 PM
paperposterbot.bsky.social
arXiv📈🤖
Bayesian Emulation of Multi-fidelity Earth System Modelling Using Hierarchical Gaussian Processes
By Xiong, Kimpton, Yang et al
Multi-fidelity Earth system models provide simulations at different levels of complexity and computational cost, but exhaustive exploration of the parameter space at the highest fidelity is often prohibitively expensive. Multi-fidelity emulators can reduce this burden by combining abundant lower-fidelity simulations with limited high-fidelity evaluations. We compare four Gaussian-process-based multi-fidelity approaches: the Kennedy--O'Hagan autoregressive model (K&O), hierarchical kriging (HK), Bayesian hierarchical emulation for multi-level models (BayHEm), and multi-fidelity deep Gaussian processes (MF-DGP). We evaluate the methods using two contrasting applications: a three-fidelity tsunami simulator and a two-fidelity implementation of the Joint UK Land Environment Simulator (JULES). Performance is assessed using leave-one-out predictive accuracy, uncertainty representation, design requirements, and computational characteristics. In the tsunami application, BayHEm gives the lowest normalised root mean square error (NRMSE = 0.031) and highest SCORE (3.025), while MF-DGP performs worse than the single-fidelity baseline when only 10 high-fidelity simulations are available. In the JULES application, MF-DGP gives the lowest NRMSE (0.079) and highest SCORE (3.032), with all 30 held-out high-fidelity observations lying within their nominal 95\% predictive intervals. These contrasting results show that no single multi-fidelity emulator is uniformly superior. Instead, method choice should reflect the complexity of the inter-fidelity relationship, the amount of high-fidelity information available, and the importance placed on predictive accuracy and uncertainty quantification.
September 9, 2026 at 9:13 AM
vinkius-mcp-ai.bsky.social
AI agents can now perform advanced geostatistical interpolation using the new kriging-estimation-model capability to predict resource grades and uncertainty.
September 7, 2026 at 9:35 PM
gladwold.bsky.social
det har vært mye kriging på Helgoland da, til å være en så liten øy.
September 2, 2026 at 9:57 AM
vanuatugis.bsky.social
Stratified ordinary kriging using gstat and stars packages
The gstat package supports stratified ordinary kriging using sp objects as shown at https://github.com/r-spatial/gstat/blob/master/demo/examples.R (the code below). This year (https://r-spatial.github.io/gstat/news/index.html) gstat also got support for sf and stars objects. While sf and stars works well for many geostatistical methods, I am unable to make it work for stratified ordinary kriging. # an example of gstat + sp library(gstat) library(sp) data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y meuse$part.a = krige(part.a ~ 1, meuse.grid, meuse, nmax = 1)$var1.pred #> [inverse distance weighted interpolation] x1 = krige( log(zinc) ~ 1, meuse[meuse$part.a == 0, ], meuse.grid[meuse.grid$part.a == 0, ], model = vgm(.548, "Sph", 900, .0654), nmin = 20, nmax = 40, maxdist = 1000 ) #> [using ordinary kriging] x2 = krige( log(zinc) ~ 1, meuse[meuse$part.a == 1, ], meuse.grid[meuse.grid$part.a == 1, ], model = vgm(.716, "Sph", 900), nmin = 20, nmax = 40, maxdist = 1000 ) #> [using ordinary kriging] x = rbind(as.data.frame(x1), as.data.frame(x2)) gridded(x) = ~x+y spplot(x["var1.pred"], main = "stratified kriging predictions") The main problem is how to use only selected values from a stars object. library(stars) #> Loading required package: abind #> Loading required package: sf #> Linking to GEOS 3.7.1, GDAL 2.3.2, PROJ 5.2.0 library(sp) data(meuse.grid) gridded(meuse.grid) = ~x+y meuse.grid_stars = st_as_stars(meuse.grid) meuse.grid_stars[meuse.grid_stars$part.a == 1, ] #> Warning in structure(eval(rlang::expr(x[[i]][!!!args])), levels = attr(x[[i]], : Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes. #> Consider 'structure(list(), *)' instead. Is it possible to do stratified ordinary kriging using gstat and stars?
gis.stackexchange.com
September 1, 2026 at 3:05 PM
gladwold.bsky.social
Nå leste jeg litt om Helgoland (eller Heligoland) på wikipedia, og fy søren så mye kriging det har vært på den lille øya.
August 31, 2026 at 9:41 AM
paperposterbot.bsky.social
arXiv📈🤖
Wavelet-Based Bayesian Hierarchical Modeling with Regularized Horseshoe Priors for Spatially Correlated Functional Data
By Burbano-Moreno, Sousa, Hotta
Environmental monitoring networks increasingly record pollutant levels as curves observed over time at fixed stations. Yet, exposure and risk assessment often require predicting these curves, with their uncertainty, at unmonitored locations. Conventional smooth-basis methods for such spatially correlated functional data, including spline-based kriging, tend to over-smooth acute, high-frequency episodes such as pollution peaks. We propose a parsimonious wavelet-based Bayesian hierarchical model that anchors a single, shared Mat\'ern correlation matrix at each resolution level, avoiding the parameter inflation of coefficient-specific spatial processes. Adaptive sparsity is enforced through a spatially informed regularized horseshoe prior, allowing the model to borrow strength across neighboring locations. At the same time, a non-centered parameterization ensures stable inference under the No-U-Turn Sampler. In simulation studies across distinct signal-to-noise regimes, incorporating spatial dependence substantially stabilizes reconstruction under severe noise relative to strictly independent models. Applied to PM$_{10}$ concentrations from Mexico City and benchmarked against ordinary kriging for functional data and a hierarchical Bayesian wavelet alternative, the model captures acute pollution events and, beyond improving point prediction, delivers sharper and better-calibrated credible intervals at unobserved locations.
August 29, 2026 at 12:44 AM
paperposterbot.bsky.social
arXiv📈🤖
Wavelet-Based Bayesian Hierarchical Modeling with Regularized Horseshoe Priors for Spatially Correlated Functional Data
By Burbano-Moreno, Sousa, Hotta
Environmental monitoring networks increasingly record pollutant levels as curves observed over time at fixed stations. Yet, exposure and risk assessment often require predicting these curves, with their uncertainty, at unmonitored locations. Conventional smooth-basis methods for such spatially correlated functional data, including spline-based kriging, tend to over-smooth acute, high-frequency episodes such as pollution peaks. We propose a parsimonious wavelet-based Bayesian hierarchical model that anchors a single, shared Mat\'ern correlation matrix at each resolution level, avoiding the parameter inflation of coefficient-specific spatial processes. Adaptive sparsity is enforced through a spatially informed regularized horseshoe prior, allowing the model to borrow strength across neighboring locations. At the same time, a non-centered parameterization ensures stable inference under the No-U-Turn Sampler. In simulation studies across distinct signal-to-noise regimes, incorporating spatial dependence substantially stabilizes reconstruction under severe noise relative to strictly independent models. Applied to PM$_{10}$ concentrations from Mexico City and benchmarked against ordinary kriging for functional data and a hierarchical Bayesian wavelet alternative, the model captures acute pollution events and, beyond improving point prediction, delivers sharper and better-calibrated credible intervals at unobserved locations.
August 28, 2026 at 12:55 PM
vanuatugis.bsky.social
Create a grid for more than one country in R
Some time ago I posted my own answer to create a grid inside a country to be used in kriging interpolation: 0........0.kriging error messages, grid file problem ? (automap, gstat) This grid was for South Africa. Now I have data point for Lesotho (the hole inside SA), but cant get around it, to work with both together. I need a joint grid for kriging in gstat. I used makegrid in sp: SA0
gis.stackexchange.com
August 26, 2026 at 3:07 PM
sivuyilenzimeni.com
I should probably write up my Spatial Analysis on Airbnb listings in Cape Town. It was a cool case study on how to use kriging, working with {sf}, {stars}, {tmap} etc on #rstats.
A satellite image of Cape Town overlayed with krige-derived airbnb price estimates. Airbnb listings in densely populated neighborhoods, in the periphery of the city, are priced cheaper than their counterparts.While Airbnb listings in wealthy neighbours, along the beach front and close to nature reserves are the most expensive.
August 26, 2026 at 9:34 AM
mathna-bot.bsky.social
Sara Avesani, R\"udiger Kempf, Michael Multerer, Holger Wendland: Samplet compression for conditionally positive definite kernels and universal Kriging https://arxiv.org/abs/2608.24283 https://arxiv.org/pdf/2608.24283 https://arxiv.org/html/2608.24283
August 26, 2026 at 6:49 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Three Distributional Approaches for PM10 Assessment in Northern Italy
By Sanctis, Gilardi, Milan et al
We propose three spatial methods for estimating the full probability distribution of PM10 concentrations, with the ultimate goal of assessing air quality in Northern Italy. Moving beyond spatial averages and simple indicators, we adopt a distributional perspective to capture the complex variability of pollutant concentrations across space. The first proposed approach predicts class-based compositions via Fixed Rank Kriging; the second estimates multiple, non-crossing quantiles through a spatial regression with differential regularization; the third directly reconstructs full probability densities leveraging on both Fixed Rank Kriging and multiple quantiles spatial regression within a Simplicial Principal Component Analysis framework. These approaches are applied to daily PM10 measurements, collected from 2018 to 2022 in Northern Italy, to estimate spatially continuous distributions and to identify regions at risk of regulatory exceedance. The three approaches exhibit localized differences, revealing how modeling assumptions may influence the prediction of fine-scale pollutant concentration patterns. Nevertheless, they consistently agree on the broader spatial patterns of pollution. This general agreement supports the robustness of a distributional approach, which offers a comprehensive and policy-relevant framework for assessing air quality and regulatory exceedance risks.
August 25, 2026 at 3:47 AM
matthollanders.bsky.social
Just watched your talk, it was great. The recovery of the population mean, is there a parallel with kriging/making predictions for unobserved locations with a GP?
August 23, 2026 at 1:07 AM
statme-bot.bsky.social
Montserrat Fuentes, Veronica B. Patterson: From Kriging to Spatial AI: Fifty Years of Spatial Statistics for Complex Dependent Data https://arxiv.org/abs/2608.20260 https://arxiv.org/pdf/2608.20260 https://arxiv.org/html/2608.20260
August 21, 2026 at 7:03 AM
vanuatugis.bsky.social
R kriging raster returns NA for some prediction points?
I am trying to interpolate a raster layer of chlorophyll-a data using kriging (gstat::krige) in R, however the resulting interpolation is showing NA values in a peculiar straight line through the raster (see last plot). From the interpolated raster I have then extracted values corresponding to spatial points. The chlorophyll-a raster (chl), colour palette (pal) and spatial points (subSP) relevant to this question can be found here. library(raster) library(gstat) library(rworldmap) data(countriesLow) #Load subsetted lat and lon coordinates load("track_points.Rdata") head(subSP) coordinates(subSP)
gis.stackexchange.com
August 20, 2026 at 11:12 AM