Golden Software Blog

Helping you learn more about the latest product information, tips, tricks, techniques, and customer stories so you can visualize data and communicate results with ease.

Create Aspect-Slope Maps in Seconds Using Surfer

My last blog article described how to create a slope map from a digital elevation model in Surfer. Moving forward on that topic, I found this blog article written for ArcMap on creating aspect-slope maps, which was improved upon for QGIS. This single map combines both the compass direction of slopes (aspect) and the steepness of the slopes (in degrees) and uses both color and saturation to display the combined results. Slopes facing different directions use different colors, and the brightness of that color shows the steepness of that slope (the brighter the color, the steeper the slope). I thought this was a really interesting map type and it made me wonder how this could be done in Surfer.

Coincidentally, at that time, a user asked me this exact question! The user wanted to come up with a way to see the very small slope variations in the soft sediments they have on the surface, using both aspect and slope. Looking at the slopes and aspect together may reveal small variations that otherwise could be overlooked.

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UK Votes to Leave EU

UK Votes to Leave EU

The United Kingdom (UK) voted on the United Kingdom European Union membership referendum, commonly referred to as the Brexit vote, on June 23, 2016. This referendum was to gauge citizen support for whether or not to remain a member of the European Union (EU), an economic and political partnership involving 28 European countries. Overall, the UK voted 51.9% to leave the EU, with 71.8% turnout. The world reacted when the news was announced on June 24. I've spent the last two weeks reading about this historic vote and what it means for the people of the UK, the EU, and the rest of the globe. It has already had some effects on international economic markets. It remains to be determined how it will affect the future, but I find this to be a fascinating time for trade, politics, economics, and international relations.

The first thing that interested me was the demographic breakdown of the vote. Various exit and other polls were done with information about how different demographics voted. I sifted through the information available at Lord Ashcroft Polls, and noticed some clear correlations between education level and age and how an individual in the poll voted. Older voters, less educated voters, and less employed voters were more likely to vote to leave the EU. I wondered if those in less-than-ideal socioeconomic situations were looking for anything different that may help provide a higher quality of life. Another issue that caught my attention was when voters made their decisions. Nearly 25% of those polled made their decision within the week before casting their votes! Just over 1/3 always knew how they would vote. The remaining ~40% made their minds up in the last 6+ months. To me, this shows some uncertainty about how to vote or perhaps uncertainty about what the effects of the vote would be on the individual and UK.

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Jennifer Woodson
Hi, I had listed the bars in reverse order, but it is fixed now. Thanks for catching that! Jennifer Woodson... Read More
Thursday, 21 July 2016 10:21
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Eliminate Subsurface Uncertainty with Newly Released Strater 5

Eliminate Subsurface Uncertainty with Newly Released Strater 5

Today marks another exciting day in Golden Software history with the latest release of Strater. Strater is an intuitive subsurface visualization program designed to display a wide variety of well log, borehole, and cross section data.

All fifteen log types available in Strater.

Introduced in 2004, Strater is a leading competitor in subsurface modeling software. Designed for geoscientists and engineers, Strater converts geotechnical, geophysical, environmental, and mining data from a wide variety of file formats into 15 different log types, borehole models, and cross section views. Strater offers unsurpassed flexibility in design and layout of the log and cross section displays. Virtually every aspect of Strater’s plot is customizable, allowing users to quickly and easily create publication-quality reports.

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100 Years of National Park Services

100 Years of National Park Services

In a few short months, the United States National Park Service will celebrate its one hundredth anniversary. August 25, 2016 marks the day when President Woodrow Wilson signed into law the Organic Act. This act created the National Park Service, a federal bureau tasked with “conserving the scenery and the natural and historical objects and the wild life therein…and by such means will leave them unimpaired for the enjoyment of future generations.”

Today, the National Park System covers more than 84 million acres across 50 states, the District of Columbia, American Samoa, Guam, Puerto Rico, Saipan, and the Virgin Islands. Over 400 areas consisting of national parks, preserves, monuments, resources, rivers, and historical sites are managed by the National Park Service.

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Predicting Local Precipitation and Temperature from Oceanic Niño Index

It seems that this year is one of the colder and wetter years in my recent memory, at least in Colorado. Several ski areas have stayed open or have reopened every weekend past the original closing date because of additional snow fall. At least one ski area was still open this June, which is traditionally biking, hiking, and mountain climbing season. Trail Ridge Road in early June reportedly had 20 foot deep snowbanks in places, which is some of the highest I can remember. I recall back in the spring hearing about winter 2016 being one of the strongest El Niño years. So, I began to wonder, did we receive more precipitation this year because of the El Niño? Does Colorado normally receive more precipitation in El Niño years? And, because I love to see actual data and graphs “proving” the results, how can I visualize this?

I started by collecting precipitation data from NOAA for the entire state of Colorado. The data only went through the end of April, 2016 so I wasn’t able to evaluate the last 6 weeks of data. I compiled the data from 1950 to 2016, using only the data from January through the end of April. I then separated the data into El Niño years, La Niña years, and Normal years, based on the oceanic niño index information. I then created a bar chart in Grapher displaying the data. I was surprised to discover that it didn’t seem to affect precipitation over the entire state whether the ocean temperatures were cool (blue bars) or warm (red bars).

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