2019 has been a year full of milestones. INT celebrated its 30 years and has made IVAAP available to all members of the OSDU consortium as part its demo release. But this year has seen many more achievements, and among them is the consolidation of INT products as a complete ecosystem, an ecosystem centered around geoscience data, built for the cloud. One of the pieces of this ecosystem is INTViewer. With the new year approaching, let’s count down the ways that the latest iteration of this desktop application facilitates the ingestion of your data to the cloud.
Time slices take less disk space
Seismic datasets take a large amount of space. While storage is “cheap” on the cloud, when individual files take terabytes, creating a copy of that file is not an innocuous decision. Time slices provide an excellent visualization of a seismic survey but transposing a dataset effectively creates a copy of that data and not every workflow requires access to all possible time slices.
INTViewer 2019 now offers an option to choose how many time slices you want to create during transposition. Just a few slices is often enough, especially if you maintain a data library and use INT’s solutions to showcase your data to potential customers. The output of the transposition will be a much smaller file, cheaper to host and faster to upload.
Virtual headers save time and reduce storage costs
This feature was actually added in 2018, but is worth mentioning because of the cost savings. When you use INTViewer to prepare data, you might find that some headers are not populated. For example, for acquisition data, you might know the location of the source and the receivers, but not the offset or the location of the midpoint. These two header values can be calculated, and INTViewer proposes to create so-called “virtual headers” that will store this information.
Creating virtual headers doesn’t modify your SEG-Y file. It doesn’t change the size of the small index file that INTViewer creates to make fast data access possible. Without virtual headers, to show the midpoint or the offset, your only solution would be to rewrite your data. Not only this rewriting operation takes time, but it also creates yet another copy of your data, doubling your storage costs.
Quick validation of your data before you upload it to the cloud
New technologies bring new terminologies. One term in particular that has made its debut in the geoscience community moving to the cloud is the term of “snowball”. A snowball is the physical transport solution that cloud providers offer when the network becomes impractical to move large data files to the cloud. This is a painful process to “ship” your data with a snowball and even when network bandwidth does allow reasonable upload times, there is certainly no time to do it twice.
INTViewer has been designed to allow immediate quality control of your data. Drag and drop your SEG-Y file to INTViewer’s desktop, and you’ll visualize traces immediately. Performing a spectrum analysis is two clicks away. And there is no need to set up a project. After indexing your dataset locally, verifying the location of your data on a map is also instantaneous. This is a simple way to confirm the validity of the location headers and coordinate reference system prior to ingestion.
As you upload more and more data to the cloud, your validation process needs to become systematic. This is where the automation of INTViewer comes in handy. INTViewer is scriptable through Python, allowing you to repeat the exact same validation steps prior to ingesting your data to the cloud.
A more efficient and useful index file
Users of INTViewer are familiar with the .XGY file, an XML file that INTViewer creates during indexing. This file contains the meta data of a SEG-Y file after it’s been indexed. The format of this file has been changed in 2019 in two ways:
The meta-data of an indexed SEG-Y is now visible in the .XGY file. An example of such meta-data is the amplitude statistics (minimum amplitude, maximum amplitude, average, RMS). These statistics used to be stored in the companion binary .IGX file. Exposing these statistics in plain text allows our customers to extract this information in an automated manner just by parsing the .XGY file. This is especially useful if you are building your own data lake.
When indexing a 2D line, INTViewer automatically calculates the trajectory of that line. Likewise, when indexing a post-stack or a pre-stack, INTViewer derives the outline of this survey. This information was always stored in the .XGY file, but its projection to WGS84 was not… until 2019. While it’s also valuable information to extract and store in a proprietary database, cloud solutions such as IVAAP benefit from reading the projected geometry of a dataset instead of having to calculate it. The data loads faster on a map because there is only one file from the cloud to read to get all the meta-data, instead of two with an index from 2018. The number of files to read is important because cloud APIs consume more resources when accessing multiple files compared to the same accesses on a local file system.
Integration with IVAAP through the INTGeo plugins
Historically, the INTGeo plugins of INTViewer were written to access files posted on INTGeoServer. INTGeoServer is a lightweight geoscience server often used in conjunction with INT’s HTML5Viewer. INTViewer has long been able to efficiently visualize seismic and well datasets posted on INTGeoServer.
Likewise, with the release of INTViewer 2019, INTViewer can also access data posted in IVAAP. This means that if you have ingested your seismic datasets to Amazon S3, you can visualize these datasets in INTViewer by just pointing this application to your IVAAP instance. INTViewer is storage-agnostic and its tools (2D, 3D, F-K, Spectrum, etc.) will work without extra steps, as if the data was local.
This capability is quite useful to conclude an ingestion workflow. After you upload one or several datasets, you typically want to verify that your data wasn’t corrupted during this process, or simply that all files were posted. With the INTGeo plugins, you do not need to open IVAAP to perform this step, it can be done from the same desktop application used to flight-test this data prior to ingestion.
IVAAP supports multiple cloud vendors. In addition to Amazon S3, you can visualize data posted both to Microsoft Azure Blob Storage and Google Cloud Storage. If IVAAP has been deployed to access these data stores, you only need your IVAAP credentials and INTViewer to open the datasets they contain. This also applies to all files posted in an IVAAP “geofiles” connector, whether they are seismic (SEG-Y, SEP) or well (LAS, DLIS) files.
This concludes our countdown (happy new INTViewer?). While INTViewer stands on its own as an application for QA and QC, it is also a useful companion to a cloud ingestion workflow in general, and to IVAAP in particular. You reached this far—contact us for a demo or an evaluation!