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OF ROCKS AND ROBOTS Continued from p. 33


be debated by geoscience profession- als); those duties, which rely mostly on Bureau of Labor Statistics (BLS) defini- tions, are available on the websites men- tioned above. Factors that are believed to make a job less susceptible to automation include close interaction with people (nurses being a typical example; in geoscience, a good example might be the never-ending task of relationship-build- ing with clients current and prospective), creative or critical thinking, and negotia- tion, among others. Legislation provides another possible barrier to automation; in states that regulate certain activities, a licensed (human) professional might still need to provide the final review and approval of any conclusion generated by artificial intelligence. Fortunately or unfortunately (depending on your perspective) for geologists, geological licensing has been under question in several states, my own state of Florida springing to mind.


Curious as to what geologists on the ground had to say about automation in their field, I devised a short and infor- mal anonymous survey, which I posted on several online forums geared toward geoscientists, garnering responses from 33 geologists.


A quick profile of our respondents:


• Eighty-two percent of respon- dents were in the United States.


• Half of respondents were employed by private firms, almost a quarter in academia, and the remainder by state and federal gov- ernments.


• At about 30% of respondents, the most common self-identified indus- try employer was oil and mining, followed by environmental at 18%.


• While I (sadly) did not request years of experience, using titles (e.g. senior, chief) as a proxy, I estimate that 42% were senior-level, 52% staff or junior level, and 6% students.


•Seventy-two percent of all respondents indicated they were responsible for hiring decisions at their firm.


The geologists who responded about automation had mixed responses on its impact on the outlook of careers in geol- ogy. About 42% percent indicated that they believe automation will overall help employment prospects in geology; about 12% predicted it will hurt; and about 45% said it would neither hurt nor help.


46 TPG • Jul.Aug.Sep 2018


When asked whether they had already witnessed automation in the geological field, most of them indicated that they had. At present, automation is warm- ly regarded, credited with efficiency improvements, elimination of the repeti- tive and mundane, less paper clutter. Areas mentioned as having experienced at least some automation included: data management; seismic interpretation; well log correlation; mapping; remote sensing; drilling; microprobe analysis; XRF analysis; and client invoicing. With some overlap, data management (col- lection, processing, storage, analysis) at 24% and in particular mapping at 21% proved the most popular automation experiences.


Overall, ten (30%) responded that automation had not yet impacted their own role, but 4 of these added the caveat that if software enhancements, the paper to electronic transition, and other incre- mental improvements were grouped under automation, then indeed, certain tasks had undergone automation. One respondent on the side of “No” noted that the ocean floor maps they developed did “not [have] enough data to automate” and therefore required “creativ[ity].” A consulting geologist noted that “thin- section production and SEM imaging” had been automated for “faster produc- tion of an inferior product. They ended up hiring less skilled people and clients became used to receiving the inferior product.” One geologist held that auto- mation had “greatly impacted efficiency and performance” even as another “saw an increase in technological employ- ment.” A Chief Environmental Scientist summarized the impact thus: “Yes, there has been automation, but not job elimi- nation types of automation. Think of it as the automation of a washing machine or vacuum cleaner. The automation in the form of GIS, report writing software, smart phones, etc., has made geologists more productive but not cost jobs.”


When asked for their prognostica- tions, geologists’ crystal balls offered varied visions on the impacts of automa- tion, ranging from “none at any time” on the one end to “given the overall trend of automation, I foresee it eliminating jobs in the future (decades)” at the other.


Fieldwork was often cited as resistant to automation. One geologist explained that “the nature of fieldwork and the need to make decisions factoring in many variables will delay the impacts of automation for a long time.” Another


likewise asserted that automation “will never replace walking, looking, and get- ting hands-on dirty looking at what’s there up close and real!”


Similarly, critical thinking was seen as an area where machines would struggle to gain the edge over flesh. “Research requires critical thought and analysis that no machine will ever be capa- ble of,” wrote one scientist. A petroleum geologist posited, “A geologist will always be needed to account for geo- logic processes.” One respon- dent warned that we “have to be cautious of allowing computers to do too much and push aside the human fac- tor. Human insight cannot be computerized.”


However, one area that many respon- dents viewed as susceptible to automa- tion was basic data analysis, which one geologist grouped under the pur- view of “counting or identifying” things. “Energy and economic geology are going to be greatly impacted,” wrote another. However, automation in geology “all depends on amount of data,” caveated a researcher, continuing that “plenty of data permits automation,” but without it, one needs “first principles and the ability to think.” Several geologists pre- dicted improved automated processing and analysis of well logs, cores, cross- sections, seismic data, and remotely- acquired imagery, noting that this was already occurring and would continue to be refined into the future. As a side effect, several expected—probably accu- rately, according to economists—that low level and technician jobs will be lost. One feared that this could nega- tively impact newcomers to the field, who have traditionally used these posi- tions as a first step on the career lad- der, with anecdotal evidence suggesting that already an increasingly long—and therefore costly—educational pedigree is required before securing a job.


Thus, coding and software skills were seen as essential for the new era: “Employees will need experience in auto- mation-related areas: coding, machine learning, etc. They need this already!” This could be addressed by education,


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