A seismic catalogue can score 100% on completeness and still be useless. Every cell may be filled. Every column may have a value. And those values may be wrong, misleading, or meaningless. This is the gap between completeness and quality — and it matters more than most data owners realize.
This article explains what data quality problems look like in seismic archives, why fill percentages lie, and what a quality review can surface.
Completeness vs. correctness
Completeness answers a simple question: is the cell empty or not? A completeness score counts how many fields have values. A column that is 95% filled looks healthy. A column that is 100% filled looks perfect.
Correctness asks a harder question: is the value right? A cell that contains (0,0,0,0) as its coordinates is technically filled. But those coordinates point to the Gulf of Guinea, not the Western Canadian Sedimentary Basin. The cell passes a completeness check. It fails a correctness check.
The four-zero problem
One of the most common quality defects in seismic archives is coordinates set to zero. This happens for several reasons. The original data may never have included coordinates. The import process may have defaulted empty fields to zero. The data entry may have used (0,0,0,0) as a placeholder that was never corrected.
The result is a line that exists in the catalogue but does not appear on the map. A user who searches by area will not find it. A map view that plots coverage will not show it. The line is invisible to any query that depends on geography — which, in Western Canada, is almost every query. No completeness score will catch it.
Common quality defects in seismic archives
Quality problems fall into categories. Each category has different causes, different symptoms, and different fixes.
Missing or generic line names
The line name is the primary identifier for a 2D seismic line. Without it, a user cannot search by name. They cannot match the catalogue entry to a tape label. They cannot tell whether the line they need is the line they are looking at.
Some archives have entries with no line name at all. Others have entries with generic names like "Line 1" or "Unknown" or the same placeholder repeated across hundreds of rows. These values fill the column. They do not identify the line.
Coordinate defects beyond zero
Zeroes are not the only way coordinates go wrong. A single default value repeated across every row, latitude and longitude reversed, and coordinates recorded in the wrong datum all break spatial search the same way: the map shows a false picture. Lines appear in the wrong place. Coverage gaps appear where data exists. Coverage appears where it does not.
Thin or missing project IDs
A project ID groups lines that were shot together as part of the same survey program. When project IDs are missing, lines that belong together cannot be found together. A user searching for "all lines from the 1987 ABC survey" cannot run that query if the project ID column is empty.
Thin project IDs are nearly as bad. An ID like "1987" or "Survey" does not distinguish the target from hundreds of other records. The column is filled. The value is useless.
Media present but unreadable
Not all quality problems are in the catalogue. Some are in the physical media. A tape that is listed as available may be unreadable: oxide shed, stuck layers, or drive incompatibility.
This is a gap between the catalogue and reality. The catalogue says the data exists. The retrieval process says it does not. The only way to know is to try to read the tape — or to record the results of previous attempts so that nobody tries twice.
Duplicates and near-duplicates
Archives that have been merged, migrated, or consolidated often contain duplicates. The same line appears twice with slightly different metadata. Two entries point to the same tape. Two entries describe the same data with different line names.
Duplicates confuse users and inflate counts. An archive that reports 50,000 lines may only contain 45,000 unique lines if 10% are duplicates. A licensing deal based on line count may be overpriced. A coverage map may show false density.
Why quality matters
Data quality is not a technical concern. It is a business concern. Quality problems affect three areas directly.
Acquisitions and divestitures
When a company sells assets, the data room must include a list of seismic coverage. Buyers evaluate that coverage as part of the deal. If the catalogue is wrong, the deal is based on false information.
An archive with zeroed coordinates cannot produce an accurate coverage map. An archive with missing line names cannot produce a reliable inventory. An archive with duplicates cannot produce a correct count. The seller does not know what they are selling. The buyer does not know what they are buying.
Licensing and resale
Data brokers license seismic to third parties. The licence is based on the data that exists. If the catalogue says a line is available and the tape is unreadable, the broker cannot fulfill the order. If the catalogue says a line covers one area and the actual coverage is elsewhere, the client receives the wrong data.
Quality problems create operational failures. The broker promises something they cannot deliver. The client receives something they did not order. Both parties lose time, money, and trust.
Operational retrieval
Operators retrieve seismic data for field development, infill drilling, and reservoir work. Speed matters. A line that cannot be found costs time. A line that is retrieved and turns out to be the wrong line costs more time.
Quality issues slow retrieval. If the line name in the catalogue does not match the label on the tape, someone must reconcile the difference. If the coordinates are wrong, someone must figure out the correct area. If the project ID is missing, someone must search by other attributes or walk the shelves.
What a scorecard-style review can surface
A quality review examines the catalogue systematically and produces a scorecard: a summary of what is present, what is missing, and what is suspect.
The first layer is completeness. For each column, how many cells are filled? A column with 98% fill is in good shape. A column with 40% fill needs attention. A column with 0% fill is not being used at all.
The second layer is correctness. Among the filled cells, how many contain obviously wrong values? Coordinates of (0,0,0,0). Line names that are blank or placeholder text. Project IDs that repeat across every row.
The third layer is consistency. Do the values follow a pattern? Are line names formatted the same way? Are coordinates in the same datum? Are dates in the same format? Inconsistency makes searching harder and suggests that the data came from multiple sources without normalization.
The scorecard does not fix the problems. It identifies them. Once identified, they can be prioritized and addressed. Some fixes are simple: a batch update to a default value. Others require research: cross-referencing field notes, reading tape headers, or checking against external sources.
The practical path forward
Quality work follows a sequence.
Step 1: Audit
Run a quality review against the current catalogue. Produce a scorecard that shows completeness, correctness, and consistency for each column. Identify the worst problems first.
Step 2: Fix the catalogue
Address the problems that the audit surfaced. Some will require batch updates. Others will require research. The goal is a catalogue where the values are accurate, not just present.
Step 3: Verify the media
For high-value or high-risk items, verify that the physical media matches the catalogue. Read tape headers. Check labels. Confirm that the data is retrievable. Update the catalogue to reflect reality.
Step 4: Migrate if needed
If the audit reveals unreadable media or obsolete formats, transcription may be necessary. Migrate the data to current media while the original can still be read. Log the results so that future retrievals go to the new copy.
This sequence moves from diagnosis to treatment. The audit tells you what is wrong. The fixes make it right. The verification confirms that the catalogue matches the physical inventory. The migration ensures that the data will remain accessible.
Frequently asked questions
How do I know if my archive has quality problems?
Run a completeness check on each column. If any column is below 90% filled, that is a signal. Then spot-check the filled values. If coordinates cluster at (0,0,0,0) or line names repeat a placeholder, that is a stronger signal. A formal audit will quantify both.
How long does a quality audit take?
For a small archive of a few thousand lines, days. For a large archive of tens of thousands of lines with multiple sources and inconsistent formats, weeks. The audit itself is fast. The research to fix what the audit finds takes longer.
Can I fix quality problems myself?
Simple problems, yes. If the issue is a batch of rows with a default value, you can update them in bulk once you have the correct values. Complex problems — missing line names, mismatched tape labels, duplicates from a merger — often require expertise in the archive itself.
The bottom line
Completeness is the starting point, not the finish line. A filled cell is not a correct cell. A 100% fill rate can hide a catalogue that is 100% wrong. Quality is the measure that matters.
If you do not know the quality state of your archive, the first step is a review. Once you know what is wrong, you can decide what to fix. If you would like help with that review, get in touch.