SEG-Y Rev 2 again: little-endian is legal!

Big news! Little-endian byte order is finally legal in SEG-Y files.

That's not all. I already spilled the beans on 64-bit floats. You can now have up to 18 quintillion traces (18 exatraces?) in a seismic line. And, finally, the hyphen confusion is cleared up: it's 'SEG-Y', with a hyphen. All this is spelled out in the new SEG-Y specification, Revision 2.0, which was officially released yesterday after at least five years in the making. Congratulations to Jill Lewis, Rune Hagelund, Stewart Levin, and the rest of the SEG Technical Standards Committee

Back up a sec: what's an endian?

Whenever you have to think about the order of bytes (the 8-bit chunks in a 'word' of 32 bits, for example) — for instance when you send data down a wire, or store bytes in memory, or in a file on disk — you have to decide if you're Roman Catholic or Church of England.


It's not really about religion. It's about eggs.

In one of the more obscure satirical analogies in English literature, Jonathan Swift wrote about the ideological tussle between between two factions of Lilliputians in Gulliver's Travels (1726). The Big-Endians liked to break their eggs at the big end, while the Little-Endians preferred the pointier option. Chaos ensued.

Two hundred and fifty years later, Danny Cohen borrowed the terminology in his 1 April 1980 paper, On Holy Wars and a Plea for Peace — in which he positioned the Big-Endians, preferring to store the big bytes first in memory, against the Little-Endians, who naturally prefer to store the little ones first. Big bytes first is how the Internet shuttles data around, so big-endian is sometimes called network byte order. The drawing (right) shows how the 4 bytes in a 32-bit 'word' (the hexadecimal codes 0A, 0B, 0C and 0D) sit in memory.

Because we write ordinary numbers big-endian style — 2017 has the thousands first, the units last — big-endian might seem intuitive. Then again, lots of people write dates as, say, 31-03-2017, which is analogous to little-endian order. Cohen reviews the computational considerations in his paper, but really these are just conventions. Some architectures pick one, some pick the other. It just happens that the x86 architecture that powers most desktop and laptop computers is little-endian, so people have been illegally (and often accidentally) writing little-endian SEG-Y files for ages. Now it's actually allowed.

Still other byte orders are possible. Some processors, notably ARM and other RISC architectures, are middle-endian (aka mixed endian or bi-endian). You can think of this as analogous to the month-first American date format: 03-31-2017. For example, the two halves of a 32-bit word might be reversed compared to their 'pure' endian order. I guess this is like breaking your boiled egg in the middle. Swift did not tell us which religious denomination these hapless folks subscribe to.

OK, that's enough about byte order

I agree. So I'll end with this handy SEG-Y cheatsheet. Click here for the PDF.

References and acknowledgments

Cohen, Danny (April 1, 1980). On Holy Wars and a Plea for PeaceIETF. IEN 137. "...which bit should travel first, the bit from the little end of the word, or the bit from the big end of the word? The followers of the former approach are called the Little-Endians, and the followers of the latter are called the Big-Endians." Also published at IEEE ComputerOctober 1981 issue.

Thumbnail image: “Remember, people will judge you by your actions, not your intentions. You may have a heart of gold -- but so does a hard-boiled egg.” by Kate Ter Haar is licensed under CC BY 2.0

More precise SEG-Y?

The impending SEG-Y Revision 2 release allows the use of double-precision floating point numbers. This news might leave some people thinking: "What?".

Integers and floats

In most computing environments, there are various kinds of number. The main two are integers and floating point numbers. Let's take a quick look at integers, or ints, first.

Integers can only represent round numbers: 0, 1, 2, 3, etc. They can have two main flavours: signed and unsigned, and various bit-depths, e.g. 8-bit, 16-bit, and so on. An 8-bit unsigned integer can have values between 0 and 255; signed ints go from -128 to +127 using a mathematical operation called two's complement.

As you might guess, floating point numbers, or floats, are used to represent all the other numbers — you know, numbers like 4.1 and –7.2346312 × 10¹³ — we need lots of those.  

Floats in binary

OK, so we need to know about floats. To understand what double-precision means, we need to know how floats are represented in computers. In other words, how on earth can a binary number like 01000010011011001010110100010101 represent a floating point number?

It's fairly easy to understand how integers are stored in binary: the 8-bit binary number 01001101 is the integer 77 in decimal, or 4D in hexadecimal; 11111111 is 255 (base 10) or FF (base 16) if we're dealing with unsigned ints, or -1 decimal if we're in the two's complement realm of signed ints.

Clearly we can only represent a certain number of values with, say, 16 bits. This would give us 65 536 integers... but that's not enough dynamic range represent tiny or gigantic floats, not if we want any precision at all. So we have a bit of a paradox: we'd like to represent a huge range of numbers (down around the mass of an electron, say, and up to Avogadro's number), but with reasonably high precision, at least a few significant figures. This is where floating point representations come in.

Scientific notation, sort of

If you're thinking about scientific notation, you're thinking on the right lines. We raise some base (say, 10) to some integer exponent, and multiply by another integer (called the mantissa, or significand). That way, we can write a huge range of numbers with plenty of precision, using only two integers. So:

$$ 3.14159 = 314159 \times 10^{-5} \ \ \mathrm{and} \ \ 6.02214 \times 10^{23} = 602214 \times 10^{18} $$

If I have two bytes at my disposal (so-called 'half precision'), I could have an 8-bit int for the integer part, called the significand, and another 8-bit int for the exponent. Then we could have floats from \(0\) up to \(255 \times 10^{255}\). The range is pretty good, but clearly I need a way to get negative significands — maybe I could use one bit for the sign, and leave 7 bits for the exponent. I also need a way to get negative exponents — I could assign a bias of –64 to the exponent, so that 127 becomes 63 and an exponent of 0 becomes –64. More bits would help, and there are other ways to apportion the bits, and we can use tricks like assuming that the significand starts with a 1, storing only the fractional part and thereby saving a bit. Every bit counts!

IBM vs IEEE floats

The IBM float and IEEE 754-2008 specifications are just different ways of splitting up the bits in a floating point representation. Single-precision (32-bit) IBM floats differ from single-precision IEEE floats in two ways: they use 7 bits and a base of 16 for the exponent. In contrast, IEEE floats — which are used by essentially all modern computers — use 8 bits and base 2 (usually) for the exponent. The IEEE standard also defines not-a-numbers (NaNs), and positive and negative infinities, among other conveniences for computing.

In double-precision, IBM floats get 56 bits for the fraction of the significand, allowing greater precision. There are still only 7 bits for the exponent, so there's no change in the dynamic range. 64-bit IEEE floats, however, use 11 bits for the exponent, leaving 52 bits for the fraction of the significand. This scheme results in 15–17 sigificant figures in decimal numbers.

The diagram below shows how four bytes (0x42, 0x6C, 0xAD, 0x15) are interpreted under the two schemes. The results are quite different. Notice the extra bit for the exponent in the IEEE representation, and the different bases and biases.

A four-byte word, 426CAD15 (in hexadecimal), interpreted as an IBM float (top) and an IEEE float (bottom). Most scientists would care about this difference!

A four-byte word, 426CAD15 (in hexadecimal), interpreted as an IBM float (top) and an IEEE float (bottom). Most scientists would care about this difference!

IBM, IEEE, and seismic

When SEG-Y was defined in 1975, there were only IBM floats — IEEE floats were not defined until 1985. The SEG allowed the use of IEEE floating-point numbers in Revision 1 (2002), and they are still allowed in the impending Revision 2 specification. This is important because most computers these days use IEEE float representations, so if you want to read or write IBM floats, you're going to need to do some work.

The floating-point format in a particular SEG-Y file should be indicated by a flag in bytes 3225–3226. A value of 0x01 indicates IBM floats, while 0x05 indicates IEEE floats. Then again, you can't believe everything you read in headers. And, unfortunately, you can't tell an IBM float just by looking at it. Meisinger (2004) wrote a nice article in CSEG Recorder about the perils of loading IBM as IEEE and vice versa — illustrated below. You should read it.

From Meisinger, D (2004). SEGY floating point confusion. CSEG Recorder 29(7).  Available online.

From Meisinger, D (2004). SEGY floating point confusion. CSEG Recorder 29(7). Available online.

I wrote this post by accident while writing about endianness, the main big change in the new SEG-Y revision. Stay tuned for that post! [Update: here it is!]