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Structural Data Collection with Mobile Devices: Accuracy, Redundancy

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Structural Data Collection with Mobile Devices: Accuracy, Redundancy *Manuscript Click here to view linked References 1 Structural data collection with mobile devices: 2 Accuracy, redundancy, and best practices 3 Richard W. Allmendinger*, Chris R. Siron, and Chelsea P. Scott1 4 Dept. of Earth and Atmospheric Sciences 5 Cornell University 6 Ithaca, NY 14850 USA 7 Keywords: Smartphone, compass, accuracy, redundancy, 8 Abstract 9 Smart phones are equipped with numerous sensors that enable orientation data 10 collection for structural geology at a rate up to an order of magnitude faster than tradi- 11 tional analog compasses. The rapidity of measurement enables structural geologists, for 12 the first time, to enjoy the benefits of data redundancy and quantitative uncertainty es- 13 timates. Recent work, however, has called into question the reliability of sensors on An- 14 droid devices. We present here our experience with programming a new smart phone 15 app from scratch, and using it and commercial apps on iOS devices along with analog 16 compasses in a series of controlled tests and typical field use cases. Additionally, we 17 document the relationships between a iPhone measurements and visible structures in 18 satellite, drawing on a database of 3,700 iPhone measurements of coseismic surface 19 cracks we made in northern Chile following the Mw8.1 Pisagua earthquake in 2014. By 20 comparing phone-collected attitudes to orientations determined independently of the 21 magnetic field, we avoid having to assume that the analog compass, which is subject to 22 its own uncertainties, is the canonical instrument. Our results suggest that iOS devices *Corresponding author. Email address: [email protected] 1 Now at: School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA -1- 23 are suitable for all but the most demanding applications as long as particular care is 24 taken with respect to metal objects that could affect the magnetic field. 25 1. Introduction 26 Structural geology has always suffered from a relatively small number of record- 27 ed, quantitative measurements. A field geologist working with a traditional analog 28 compass and paper field notebook typically records a few tens of orientation measure- 29 ments per day. Not only are the total measurements small in number, but repeat meas- 30 urements of sufficient quantity to establish statistical uncertainty are extremely rare in 31 the published literature. Why bother to make tens of measurement of a single bedding 32 surface at a single site when doing so would severely limit the number of outcrops we 33 could document in a day? As Ramsay and Huber wrote with respect to strain meas- 34 urements nearly a quarter century ago (Ramsay and Huber, 1983, p. 78), “it is often 35 more rewarding to spend time in the field collecting a lot of data of relatively low de- 36 gree of accuracy at many localities, rather than to concentrate on obtaining a few strain 37 data with an extremely high degree of accuracy.” However, without data redundancy, 38 it is impossible to evaluate the significance and accuracy of a measurement. Thus, the 39 ideal case would be to make lots of measurements of high accuracy and well established 40 uncertainty quickly enough that one can still visit many localities. 41 Smart phone compass/stereonet programs (“apps”) will inexorably replace tra- 42 ditional analog compasses (Brunton, Freiberg, Silva, etc.) because of convenience, cost, 43 and ubiquity but mostly because of their rapidity. The ability to record orientation 44 along with location (latitude and longitude or UTM) and date/time with a single tap of 45 an on-screen button reduces, by nearly an order of magnitude, the amount of time that 46 it takes to make a measurement. If many more measurements can be made in the same -2- 47 period of time, then field geologists can begin to enjoy the benefits of data redundancy 48 that simply are not feasible with analog instruments. Additionally, in every cash- 49 strapped geology department, the question will be, or is already being, asked: “why 50 should we invest thousands of dollars in analog compasses when every student already 51 has a smart phone at their disposal?” 52 The enthusiasm for smart phone orientation measurement apps was recently 53 dampened somewhat by Novakova and Pavlis (2017) as well as earlier studies (e.g., 54 Hama et al., 2014) that demonstrate considerable variation in quality of device sensors 55 and accuracy of recorded observation. Most conclude that the least reliable sensor is the 56 device magnetometer which likewise accords with our experience. However, compari- 57 sons in previous studies tend to be incomplete or misleading in assessment of accuracy 58 because: only one operating system (i.e., Android or iOS) is tested, multiple devices are 59 not used in testing, the details of the algorithms used in the apps are seldom well de- 60 scribed, the error in the dip is assessed separately from the error in the strike (rather 61 than using poles to planes), single measurements from analog compasses are regarded 62 as canonical rather than comparing multiple analog compass measurements to multiple 63 smart phone apps, and there is no assessment of accuracy that does not rely on the 64 magnetic field. 65 Here, our purpose is three fold: (1) introduce a new, free iOS app, Stereonet Mobi- 66 le, written by the senior author2 and document the basics of it’s functioning; (2) compare 67 Stereonet Mobile and a popular smart phone app, Fieldmove Clino, to each other and to 68 analog compass measurements on a datum by datum and group by group basis; and (3) 2 Because Stereonet Mobile is available for free from the iOS App Store, the senior author has no finan- cial conflict of interest. -3- 69 document the accuracy of app measurements independent of analog compass meas- 70 urements by using a massive database of measurements of surface crack measurements 71 and comparing those measurements with the same cracks visible on Google Earth im- 72 agery. 73 Our study, using only Apple® iPhones (iOS operating system), stands in contrast 74 to Novakova and Pavlis (2017) who used only Android devices. Together, these two 75 studies tend to suggest that the four different iPhones devices used here are significant- 76 ly superior in accuracy and reliability compared to the two tested Android devices. This 77 conclusion has also been reached by Midland Valley, the publisher of Fieldmove Clino 78 who state, “We have observed much larger variations in the measured data recorded 79 using Android devices which we suspect is largely down to the quality of the hardware 80 components inside the device” (Midland Valley, 2017). Our data are also consistent with 81 the recent work of (Cawood et al., 2017) who compared remotely sensed surface data 82 (LiDAR, Structure from Motion) to both digital and analog compass readings. However, 83 anyone collecting irreplaceable field data with any electronic device will want to con- 84 duct their own tests and continue to carry an analog compass in the field with them. 85 Even where a device is not reliable for data collection, many apps allow manual data 86 entry, giving the user some of the benefits of the smart phone (e.g., automatic recording 87 of location, time, and date) without the uncertainty in accuracy. 88 2. Stereonet Mobile 89 2.1. Device Sensors 90 Smart phones have a vast array of sensors to determine device orientation in- 91 cluding GPS receivers, accelerometers, gyroscopes, magnetometers, and even barome- 92 ters. From these sensors, it is possible to determine device orientation, position, velocity, -4- 93 and linear and rotational acceleration. The iOS operating system provides the pro- 94 grammer with this derived information through its CoreMotion routines which handle 95 the translation of the raw sensor data into the orientations that we as geologists want. 96 Foremost among these is magnetometer calibration which attempts to cancel out the ef- 97 fects of local magnetic fields, especially from other components within the device such 98 as the power supply, etc., so that the orientation with respect to magnetic north can be 99 determined. The basic reason that dip measurements collected with smart phones are 100 generally much more accurate than strikes is that the magnetometer is much more sen- 101 sitive to, and local perturbations more common in, the local magnetic field than in the 102 local gravity field. The dip of the device can be determined from the three components 103 of the acceleration due to gravity alone and does not have to depend on the magnetom- 104 eter at all. 105 Sensors in the iPhone, sampled by the Sensor Kinematics Pro app at about 30 Hz, 106 appear to be very stable (Fig. 1) especially in comparison to the Android devices tested 107 by Novakova and Pavlis (2017, their figure 2). Nonetheless, the iPhone magnetometer is 108 easily perturbed by passing even small metal objects within several centimeters of the 109 device (Fig. 2b). This has considerable implications for best practices in the field when 110 using phones as data collection devices. 111 2.2. Device Coordinate System and Determining Orientation 112 The iOS device coordinate system and the rotations about the three axes are 113 shown in Figure 2. One “reads” the face of the device like a right-handed map coordi- 114 nate system: the first axis, X1, is parallel to and in the short, or side-to-side, direction of 115 the face with positive to the right. The second axis, X2, is parallel to the face and the long 116 axis of the device with positive up, and X3, the third axis, is perpendicular to the face -5- 117 and positive upwards.
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